from __future__ import annotations
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward)
from ...modeling_tf_outputs import (TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutputWithPast)
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss,
TFSharedEmbeddings, get_initializer, keras, keras_serializable, unpack_inputs)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import logging
from .configuration_gptj import GPTJConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B"
_CONFIG_FOR_DOC = "GPTJConfig"
def create_sinusoidal_positions(num_pos: int, dim: int) -> tf.Tensor:
    inv_freq = tf.cast(1.0 / (10000 ** (tf.range(0, dim, 2) / dim)), tf.float32)
    sinusoid_inp = tf.cast(tf.einsum("i , j -> i j", tf.range(num_pos, dtype=tf.float32), inv_freq), tf.float32)
    sin, cos = tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)
    out = tf.concat((sin, cos), axis=1)
    return out
def rotate_every_two(x: tf.Tensor) -> tf.Tensor:
    rotate_half_tensor = tf.stack((-x[:, :, :, 1::2], x[:, :, :, ::2]), axis=-1)
    new_shape = shape_list(rotate_half_tensor)[:-2] + [tf.math.reduce_prod(shape_list(rotate_half_tensor)[-2:])]
    rotate_half_tensor = tf.reshape(rotate_half_tensor, new_shape)
    return rotate_half_tensor
def apply_rotary_pos_emb(tensor: tf.Tensor, sincos: tf.Tensor) -> tf.Tensor:
    sin_pos, cos_pos = sincos
    sin_pos = tf.repeat(sin_pos[:, :, None, :], 2, 3)
    cos_pos = tf.repeat(cos_pos[:, :, None, :], 2, 3)
    return (tensor * cos_pos) + (rotate_every_two(tensor) * sin_pos)
class TFGPTJAttention(keras.layers.Layer):
    def __init__(self, config: GPTJConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.hidden_size
        self.num_attention_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_attention_heads
        if self.head_dim * self.num_attention_heads != self.embed_dim: raise ValueError(f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and `num_attention_heads`: {self.num_attention_heads}).")
        self.scale_attn = self.head_dim**0.5
        self.rotary_dim = config.rotary_dim
        self.attn_dropout = keras.layers.Dropout(config.attn_pdrop)
        self.resid_dropout = keras.layers.Dropout(config.resid_pdrop)
        self.q_proj = keras.layers.Dense(self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="q_proj")
        self.k_proj = keras.layers.Dense(self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="k_proj")
        self.v_proj = keras.layers.Dense(self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="v_proj")
        self.out_proj = keras.layers.Dense(self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="out_proj")
        self.max_positions = config.max_position_embeddings
        self.lower_triangle_mask = tf.reshape(tf.cast(tf.experimental.numpy.tril(tf.ones((self.max_positions, self.max_positions))), tf.int8), (1, 1, self.max_positions, self.max_positions))
        pos_embd_dim = self.rotary_dim or self.embed_dim
        self.embed_positions = create_sinusoidal_positions(self.max_positions, pos_embd_dim)
    def get_causal_mask(self, key_length, query_length) -> tf.Tensor: return tf.cast(self.lower_triangle_mask[:, :, key_length - query_length : key_length, :key_length], tf.bool)
    @staticmethod
    def get_masked_bias(dtype: tf.DType) -> tf.Tensor: return tf.cast(tf.constant(-1e9), dtype)
    def _split_heads(self, hidden_states: tf.Tensor, rotary: bool) -> tf.Tensor:
        new_shape = shape_list(hidden_states)[:-1] + [self.num_attention_heads, self.head_dim]
        hidden_states = tf.reshape(hidden_states, new_shape)
        if rotary: return hidden_states
        if len(shape_list(hidden_states)) == 4: return tf.transpose(hidden_states, (0, 2, 1, 3))
        if len(shape_list(hidden_states)) == 5: return tf.transpose(hidden_states, (0, 1, 3, 2, 4))
        raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}")
    def _merge_heads(self, hidden_states: tf.Tensor) -> tf.Tensor:
        if len(shape_list(hidden_states)) == 4: hidden_states = tf.transpose(hidden_states, (0, 2, 1, 3))
        elif len(shape_list(hidden_states)) == 5: hidden_states = tf.transpose(hidden_states, (0, 1, 3, 2, 4))
        else: raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}")
        new_shape = shape_list(hidden_states)[:-2] + [self.num_attention_heads * self.head_dim]
        return tf.reshape(hidden_states, new_shape)
    def _attn(self, query: tf.Tensor, key: tf.Tensor, value: tf.Tensor, attention_mask: tf.Tensor | None = None, head_mask: tf.Tensor | None = None) -> Tuple[tf.Tensor, tf.Tensor]:
        query_length, key_length = shape_list(query)[-2], shape_list(key)[-2]
        causal_mask = self.get_causal_mask(key_length, query_length)
        query = tf.cast(query, tf.float32)
        key = tf.cast(key, tf.float32)
        attn_weights = tf.matmul(query, key, transpose_b=True)
        attn_weights = tf.where(causal_mask, attn_weights, self.get_masked_bias(attn_weights.dtype))
        attn_weights = attn_weights / self.scale_attn
        if attention_mask is not None: attn_weights = attn_weights + attention_mask
        attn_weights = stable_softmax(attn_weights, axis=-1)
        attn_weights = tf.cast(attn_weights, value.dtype)
        attn_weights = self.attn_dropout(attn_weights)
        if head_mask is not None: attn_weights = attn_weights * head_mask
        attn_output = tf.matmul(attn_weights, value)
        return attn_output, attn_weights
    def call(self, hidden_states: tf.Tensor, layer_past: Optional[Tuple[tf.Tensor, tf.Tensor]] = None, attention_mask: tf.Tensor | None = None, position_ids: tf.Tensor | None = None,
    head_mask: tf.Tensor | None = None, use_cache: bool = False, output_attentions: bool = False):
        query = self.q_proj(hidden_states)
        key = self.k_proj(hidden_states)
        value = self.v_proj(hidden_states)
        query = self._split_heads(query, True)
        key = self._split_heads(key, True)
        value = self._split_heads(value, False)
        sincos = tf.cast(tf.gather(self.embed_positions, position_ids, axis=0), hidden_states.dtype)
        sincos = tf.split(sincos, 2, axis=-1)
        if self.rotary_dim is not None:
            k_rot = key[:, :, :, : self.rotary_dim]
            k_pass = key[:, :, :, self.rotary_dim :]
            q_rot = query[:, :, :, : self.rotary_dim]
            q_pass = query[:, :, :, self.rotary_dim :]
            k_rot = apply_rotary_pos_emb(k_rot, sincos)
            q_rot = apply_rotary_pos_emb(q_rot, sincos)
            key = tf.concat((k_rot, k_pass), axis=-1)
            query = tf.concat((q_rot, q_pass), axis=-1)
        else:
            key = apply_rotary_pos_emb(key, sincos)
            query = apply_rotary_pos_emb(query, sincos)
        key = tf.transpose(key, (0, 2, 1, 3))
        query = tf.transpose(query, (0, 2, 1, 3))
        if layer_past is not None:
            past_key = layer_past[0]
            past_value = layer_past[1]
            key = tf.concat((past_key, key), axis=-2)
            value = tf.concat((past_value, value), axis=-2)
        if use_cache is True: present = (key, value)
        else: present = None
        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
        attn_output = self._merge_heads(attn_output)
        attn_output = self.out_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)
        outputs = (attn_output, present)
        if output_attentions: outputs += (attn_weights,)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "q_proj", None) is not None:
            with tf.name_scope(self.q_proj.name): self.q_proj.build([None, None, self.embed_dim])
        if getattr(self, "k_proj", None) is not None:
            with tf.name_scope(self.k_proj.name): self.k_proj.build([None, None, self.embed_dim])
        if getattr(self, "v_proj", None) is not None:
            with tf.name_scope(self.v_proj.name): self.v_proj.build([None, None, self.embed_dim])
        if getattr(self, "out_proj", None) is not None:
            with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.embed_dim])
class TFGPTJMLP(keras.layers.Layer):
    def __init__(self, intermediate_size: int, config: GPTJConfig, **kwargs):
        super().__init__(**kwargs)
        embed_dim = config.n_embd
        self.fc_in = keras.layers.Dense(intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="fc_in")
        self.fc_out = keras.layers.Dense(embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="fc_out")
        self.act = get_tf_activation(config.activation_function)
        self.dropout = keras.layers.Dropout(config.embd_pdrop)
        self.embed_dim = config.n_embd
        self.intermediate_size = intermediate_size
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.fc_in(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.fc_out(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "fc_in", None) is not None:
            with tf.name_scope(self.fc_in.name): self.fc_in.build([None, None, self.embed_dim])
        if getattr(self, "fc_out", None) is not None:
            with tf.name_scope(self.fc_out.name): self.fc_out.build([None, None, self.intermediate_size])
class TFGPTJBlock(keras.layers.Layer):
    def __init__(self, config: GPTJConfig, **kwargs):
        super().__init__(**kwargs)
        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
        self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
        self.attn = TFGPTJAttention(config, name="attn")
        self.mlp = TFGPTJMLP(inner_dim, config, name="mlp")
        self.config = config
    def call(self, hidden_states: tf.Tensor, layer_past: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, position_ids: tf.Tensor | None = None,
    head_mask: tf.Tensor | None = None, use_cache: bool = False, output_attentions: bool = False):
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_outputs = self.attn(hidden_states=hidden_states, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask,
        use_cache=use_cache, output_attentions=output_attentions)
        attn_output = attn_outputs[0]
        outputs = attn_outputs[1:]
        feed_forward_hidden_states = self.mlp(hidden_states)
        hidden_states = attn_output + feed_forward_hidden_states + residual
        if use_cache: outputs = (hidden_states,) + outputs
        else: outputs = (hidden_states,) + outputs[1:]
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "ln_1", None) is not None:
            with tf.name_scope(self.ln_1.name): self.ln_1.build([None, None, self.config.n_embd])
        if getattr(self, "attn", None) is not None:
            with tf.name_scope(self.attn.name): self.attn.build(None)
        if getattr(self, "mlp", None) is not None:
            with tf.name_scope(self.mlp.name): self.mlp.build(None)
@keras_serializable
class TFGPTJMainLayer(keras.layers.Layer):
    config_class = GPTJConfig
    def __init__(self, config: GPTJConfig, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)
        self.config = config
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.use_cache = config.use_cache
        self.return_dict = config.use_return_dict
        self.num_hidden_layers = config.n_layer
        self.n_embd = config.n_embd
        self.n_positions = config.n_positions
        self.initializer_range = config.initializer_range
        self.wte = TFSharedEmbeddings(config.vocab_size, config.hidden_size, initializer_range=config.initializer_range, name="wte")
        self.drop = keras.layers.Dropout(config.embd_pdrop)
        self.h = [TFGPTJBlock(config, name=f"h_._{i}") for i in range(config.n_layer)]
        self.ln_f = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f")
        self.embed_dim = config.n_embd
    def get_input_embeddings(self): return self.wte
    def set_input_embeddings(self, value: tf.Tensor):
        self.wte.weight = value
        self.wte.vocab_size = shape_list(value)[0]
    def _prune_heads(self, heads_to_prune): raise NotImplementedError
    @unpack_inputs
    def call(self, input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, use_cache=None,
    output_attentions=None, output_hidden_states=None, return_dict=None, training=False) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
            input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
        elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        if past_key_values is None:
            past_length = 0
            past_key_values = [None] * len(self.h)
        else: past_length = shape_list(past_key_values[0][0])[-2]
        if position_ids is None: position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0)
        if attention_mask is not None:
            attention_mask_shape = shape_list(attention_mask)
            attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]))
            one_cst = tf.constant(1.0)
            attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
            attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0))
        if head_mask is not None: raise NotImplementedError
        else: head_mask = [None] * self.num_hidden_layers
        position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
        if inputs_embeds is None:
            check_embeddings_within_bounds(input_ids, self.wte.vocab_size)
            inputs_embeds = self.wte(input_ids, mode="embedding")
        if token_type_ids is not None:
            token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
            token_type_embeds = self.wte(token_type_ids, mode="embedding")
        else: token_type_embeds = tf.constant(0.0)
        token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype)
        hidden_states = inputs_embeds + token_type_embeds
        hidden_states = self.drop(hidden_states, training=training)
        output_shape = input_shape + [shape_list(hidden_states)[-1]]
        presents = () if use_cache else None
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
            if output_hidden_states: all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
            outputs = block(hidden_states=hidden_states, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask[i],
            use_cache=use_cache, output_attentions=output_attentions, training=training)
            hidden_states = outputs[0]
            if use_cache: presents = presents + (outputs[1],)
            if output_attentions: all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
        hidden_states = self.ln_f(hidden_states)
        hidden_states = tf.reshape(hidden_states, output_shape)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if output_attentions:
            attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
            all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
        if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
        return TFBaseModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "wte", None) is not None:
            with tf.name_scope(self.wte.name): self.wte.build(None)
        if getattr(self, "ln_f", None) is not None:
            with tf.name_scope(self.ln_f.name): self.ln_f.build([None, None, self.embed_dim])
        if getattr(self, "h", None) is not None:
            for layer in self.h:
                with tf.name_scope(layer.name): layer.build(None)
class TFGPTJPreTrainedModel(TFPreTrainedModel):
    config_class = GPTJConfig
    base_model_prefix = "transformer"
    _keys_to_ignore_on_load_unexpected = [r"h.\d+.attn.bias"]
GPTJ_START_DOCSTRING = r"""
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.
    <Tip>
    TensorFlow models and layers in `transformers` accept two formats as input:
    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.
    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:
    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!
    </Tip>
    Parameters:
        config ([`GPTJConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
GPTJ_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length` if `past` is `None` else `past[0].shape[-2]` (`sequence_length` of
            input past key value states). Indices of input sequence tokens in the vocabulary.
            If `past` is used, only input IDs that do not have their past calculated should be passed as `input_ids`.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
            [`PreTrainedTokenizer.encode`] for details.
            [What are input IDs?](../glossary#input-ids)
        past_key_values (`List[tf.Tensor]` of length `config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
            `past` output below). Can be used to speed up sequential decoding. The token ids which have their past
            given to this model should not be passed as input ids as they have already been computed.
        attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
            config will be used instead.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
            in eager mode, in graph mode the value will always be set to True.
        training (`bool`, *optional*, defaults to `False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""
@add_start_docstrings("The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.", GPTJ_START_DOCSTRING)
class TFGPTJModel(TFGPTJPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFGPTJMainLayer(config, name="transformer")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING)
    def call(self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None,
    token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    training: Optional[bool] = False) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
        outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids,
        head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
@add_start_docstrings("The GPT-J Model transformer with a language modeling head on top.", GPTJ_START_DOCSTRING)
class TFGPTJForCausalLM(TFGPTJPreTrainedModel, TFCausalLanguageModelingLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFGPTJMainLayer(config, name="transformer")
        self.lm_head = keras.layers.Dense(config.vocab_size, kernel_initializer=get_initializer(config.initializer_range), name="lm_head")
        self.config = config
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_cache=None, **kwargs):
        token_type_ids = kwargs.get("token_type_ids", None)
        if past_key_values:
            inputs = tf.expand_dims(inputs[:, -1], -1)
            if token_type_ids is not None: token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1)
        position_ids = kwargs.get("position_ids", None)
        attention_mask = kwargs.get("attention_mask", None)
        if attention_mask is not None and position_ids is None:
            position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True)
            if past_key_values: position_ids = tf.expand_dims(position_ids[:, -1], -1)
        return {"input_ids": inputs, "attention_mask": attention_mask, "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": use_cache, "token_type_ids": token_type_ids}
    @unpack_inputs
    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None,
    token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None,
    labels: np.ndarray | tf.Tensor | None = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, training: Optional[bool] = False) -> Union[TFCausalLMOutputWithPast, Tuple[tf.Tensor]]:
        transformer_outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids,
        position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        hidden_states = transformer_outputs[0]
        lm_logits = self.lm_head(hidden_states)
        loss = None
        if labels is not None:
            shifted_logits = lm_logits[:, :-1]
            labels = labels[:, 1:]
            loss = self.hf_compute_loss(labels, shifted_logits)
        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFCausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "lm_head", None) is not None:
            with tf.name_scope(self.lm_head.name): self.lm_head.build([None, None, self.config.n_embd])
@add_start_docstrings("""
    The GPT-J Model transformer with a sequence classification head on top (linear layer).
    [`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT, GPT-2, GPT-Neo) do.
    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """, GPTJ_START_DOCSTRING)
class TFGPTJForSequenceClassification(TFGPTJPreTrainedModel, TFSequenceClassificationLoss):
    _keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.transformer = TFGPTJMainLayer(config, name="transformer")
        self.score = keras.layers.Dense(self.num_labels, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="score")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None,
    token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, labels: np.ndarray | tf.Tensor | None = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]:
        if labels is not None and self.config.pad_token_id is None and input_ids.shape[0] != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        transformer_outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids,
        head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)
        logits_shape = shape_list(logits)
        in_logits = None
        if self.config.pad_token_id is None: sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (tf.argmax(tf.cast(tf.math.equal(input_ids, self.config.pad_token_id), input_ids.dtype), axis=-1) - 1)
                sequence_lengths = tf.where(sequence_lengths >= 0, sequence_lengths, tf.cast(shape_list(input_ids[-1]), sequence_lengths.dtype) - 1)
                in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1)
            else:
                sequence_lengths = -1
                logger.warning_once(f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be unexpected if using padding tokens in conjunction with `inputs_embeds.`")
        loss = None
        if labels is not None:
            if not tf.is_tensor(sequence_lengths): in_logits = logits[0 : logits_shape[0], sequence_lengths]
            loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(in_logits, [-1, self.num_labels]))
        pooled_logits = in_logits if in_logits is not None else logits
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFSequenceClassifierOutputWithPast(loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "score", None) is not None:
            with tf.name_scope(self.score.name): self.score.build([None, None, self.config.n_embd])
@add_start_docstrings("The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).", GPTJ_START_DOCSTRING)
class TFGPTJForQuestionAnswering(TFGPTJPreTrainedModel, TFQuestionAnsweringLoss):
    _keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.transformer = TFGPTJMainLayer(config, name="transformer")
        self.qa_outputs = keras.layers.Dense(self.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None,
    token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    inputs_embeds: np.ndarray | tf.Tensor | None = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    training: Optional[bool] = False) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
        transformer_outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids,
        position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, training=training)
        sequence_output = transformer_outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)
        loss = None
        if start_positions is not None and end_positions is not None:
            labels = {"start_position": start_positions}
            labels["end_position"] = end_positions
            loss = self.hf_compute_loss(labels, (start_logits, end_logits))
        if not return_dict:
            output = (start_logits, end_logits) + transformer_outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFQuestionAnsweringModelOutput(loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.build(None)
        if getattr(self, "qa_outputs", None) is not None:
            with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
