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 ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSharedEmbeddings, keras, keras_serializable, unpack_inputs)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_opt import OPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
_CONFIG_FOR_DOC = "OPTConfig"
_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
_CAUSAL_LM_EXPECTED_OUTPUT = ("Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo.")
LARGE_NEGATIVE = -1e8
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0):
    bsz = input_ids_shape[0]
    tgt_len = input_ids_shape[1]
    mask = tf.fill((tgt_len, tgt_len), tf.cast(LARGE_NEGATIVE, tf.float32))
    mask = tf.linalg.band_part(mask, 0, -1) - tf.linalg.band_part(mask, 0, 0)
    if past_key_values_length > 0: mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1)
    return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1))
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
    src_len = shape_list(mask)[1]
    tgt_len = tgt_len if tgt_len is not None else src_len
    one_cst = tf.constant(1.0)
    mask = tf.cast(mask, dtype=one_cst.dtype)
    expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
    return (one_cst - expanded_mask) * LARGE_NEGATIVE
class TFOPTLearnedPositionalEmbedding(keras.layers.Embedding):
    def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
        self.offset = 2
        super().__init__(num_embeddings + self.offset, embedding_dim, **kwargs)
    def call(self, attention_mask, past_key_values_length: int = 0):
        attention_mask = tf.cast(attention_mask, tf.int64)
        positions = tf.math.cumsum(attention_mask, axis=1) * attention_mask - 1
        positions = positions[:, past_key_values_length:]
        return super().call(positions + self.offset)
class TFOPTAttention(keras.layers.Layer):
    def __init__(self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = keras.layers.Dropout(dropout)
        self.head_dim = embed_dim // num_heads
        if (self.head_dim * num_heads) != self.embed_dim: raise ValueError(f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {num_heads}).")
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
        self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
        self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
        self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
    def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3))
    def call(self, hidden_states: tf.Tensor, key_value_states: tf.Tensor | None = None, past_key_value: Tuple[Tuple[tf.Tensor]] | None = None, attention_mask: tf.Tensor | None = None,
    layer_head_mask: tf.Tensor | None = None, training: Optional[bool] = False) -> Tuple[tf.Tensor, tf.Tensor | None]:
        is_cross_attention = key_value_states is not None
        bsz, tgt_len, embed_dim = shape_list(hidden_states)
        query_states = self.q_proj(hidden_states) * self.scaling
        if is_cross_attention and past_key_value is not None:
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
            value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
        elif past_key_value is not None:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = tf.concat([past_key_value[0], key_states], axis=2)
            value_states = tf.concat([past_key_value[1], value_states], axis=2)
        else:
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
        if self.is_decoder: past_key_value = (key_states, value_states)
        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
        key_states = tf.reshape(key_states, proj_shape)
        value_states = tf.reshape(value_states, proj_shape)
        src_len = shape_list(key_states)[1]
        attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
        tf.debugging.assert_equal(shape_list(attn_weights), [bsz * self.num_heads, tgt_len, src_len], message=(f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {shape_list(attn_weights)}"))
        if attention_mask is not None:
            tf.debugging.assert_equal(shape_list(attention_mask), [bsz, 1, tgt_len, src_len], message=(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}"))
            attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
            attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
            attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
        attn_weights = stable_softmax(attn_weights, axis=-1)
        if layer_head_mask is not None:
            tf.debugging.assert_equal(shape_list(layer_head_mask), [self.num_heads], message=(f"Head mask for a single layer should be of size {(self.num_heads)}, but is {shape_list(layer_head_mask)}"))
            attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
            attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
        attn_probs = self.dropout(attn_weights, training=training)
        attn_output = tf.matmul(attn_probs, value_states)
        tf.debugging.assert_equal(shape_list(attn_output), [bsz * self.num_heads, tgt_len, self.head_dim], message=(f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {shape_list(attn_output)}"))
        attn_output = tf.transpose(tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3))
        attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
        attn_output = self.out_proj(attn_output)
        attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
        return attn_output, attn_weights, past_key_value
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        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, "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, "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 TFOPTDecoderLayer(keras.layers.Layer):
    def __init__(self, config: OPTConfig, **kwargs):
        super().__init__(**kwargs)
        self.do_layer_norm_before = config.do_layer_norm_before
        self.embed_dim = config.hidden_size
        self.self_attn = TFOPTAttention(embed_dim=self.embed_dim, num_heads=config.num_attention_heads, dropout=config.attention_dropout, name="self_attn", is_decoder=True)
        self.dropout = keras.layers.Dropout(config.dropout)
        self.activation_fn = get_tf_activation(config.activation_function)
        self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
        self.fc1 = keras.layers.Dense(config.ffn_dim, name="fc1")
        self.fc2 = keras.layers.Dense(self.embed_dim, name="fc2")
        self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
        self.config = config
    def call(self, hidden_states: tf.Tensor, attention_mask: np.ndarray | tf.Tensor | None = None, layer_head_mask: tf.Tensor | None = None, past_key_value: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
    training: Optional[bool] = False, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]:
        residual = hidden_states
        if self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states)
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        hidden_states, self_attn_weights, present_key_value = self.self_attn(hidden_states=hidden_states, past_key_value=self_attn_past_key_value,
        attention_mask=attention_mask, layer_head_mask=layer_head_mask)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = residual + hidden_states
        if not self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states)
        residual = hidden_states
        if self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout(hidden_states, training=training)
        hidden_states = residual + hidden_states
        if not self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states)
        return (hidden_states, self_attn_weights, present_key_value)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "self_attn", None) is not None:
            with tf.name_scope(self.self_attn.name): self.self_attn.build(None)
        if getattr(self, "self_attn_layer_norm", None) is not None:
            with tf.name_scope(self.self_attn_layer_norm.name): self.self_attn_layer_norm.build([None, None, self.embed_dim])
        if getattr(self, "fc1", None) is not None:
            with tf.name_scope(self.fc1.name): self.fc1.build([None, None, self.embed_dim])
        if getattr(self, "fc2", None) is not None:
            with tf.name_scope(self.fc2.name): self.fc2.build([None, None, self.config.ffn_dim])
        if getattr(self, "final_layer_norm", None) is not None:
            with tf.name_scope(self.final_layer_norm.name): self.final_layer_norm.build([None, None, self.embed_dim])
OPT_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>
    Args:
        config ([`OPTConfig`]): 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.
"""
@add_start_docstrings("The bare OPT Model outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING)
class TFOPTPreTrainedModel(TFPreTrainedModel):
    config_class = OPTConfig
    base_model_prefix = "model"
OPT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`tf.Tensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`tf.Tensor` of shape `(0)`, *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)
        head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`)
            contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*, defaults to `True`):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`). Set to `False` during training, `True` during generation
        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 [`~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).
"""
@keras_serializable
class TFOPTDecoder(keras.layers.Layer):
    config_class = OPTConfig
    def __init__(self, config: OPTConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.padding_idx = config.pad_token_id
        self.layerdrop = config.layerdrop
        num_embeddings = config.max_position_embeddings
        self.embed_tokens = TFSharedEmbeddings(config.vocab_size, config.word_embed_proj_dim, config.pad_token_id, name="embed_tokens")
        self.embed_positions = TFOPTLearnedPositionalEmbedding(num_embeddings, config.hidden_size, name="embed_positions")
        if config.do_layer_norm_before and not config._remove_final_layer_norm: self.final_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
        else: self.final_layer_norm = None
        if config.word_embed_proj_dim != config.hidden_size:
            self.project_out = keras.layers.Dense(config.word_embed_proj_dim, name="project_out", use_bias=False)
            self.project_in = keras.layers.Dense(config.hidden_size, name="project_in", use_bias=False)
        else:
            self.project_in = None
            self.project_out = None
        self.layers = [TFOPTDecoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
        self.dropout = keras.layers.Dropout(config.dropout)
    def get_embed_tokens(self): return self.embed_tokens
    def set_embed_tokens(self, embed_tokens): self.embed_tokens = embed_tokens
    def set_input_embeddings(self, new_embeddings):
        self.embed_tokens.vocab_size = new_embeddings.shape[0]
        self.embed_tokens.weight = new_embeddings
    def get_input_embeddings(self): return self.embed_tokens
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, past_key_values_length):
        _, seq_length = input_shape
        tf.debugging.assert_equal(seq_length + past_key_values_length, shape_list(attention_mask)[1], message=f"Attention mask shape should be (batch_size, seq_length + past_key_values_length) but is {shape_list(attention_mask)[1]} with input_ids shape {input_shape} and past length {past_key_values_length}.")
        expanded_attn_mask = _expand_mask(attention_mask, tgt_len=input_shape[-1])
        if seq_length > 1: combined_attention_mask = (_make_causal_mask(input_shape, past_key_values_length=past_key_values_length) + expanded_attn_mask)
        else: combined_attention_mask = expanded_attn_mask
        return combined_attention_mask
    @unpack_inputs
    def call(self, input_ids: TFModelInputType | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, attention_mask: np.ndarray | tf.Tensor | None = None,
    head_mask: np.ndarray | tf.Tensor | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = 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]]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None: input_shape = shape_list(input_ids)
        elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1]
        else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        past_key_values_length = shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0
        if inputs_embeds is None:
            check_embeddings_within_bounds(input_ids, self.embed_tokens.vocab_size)
            inputs_embeds = self.embed_tokens(input_ids)
        if attention_mask is None: attention_mask = tf.ones((input_shape[0], input_shape[1] + past_key_values_length), dtype=tf.bool)
        else: tf.debugging.assert_equal(shape_list(attention_mask)[1], past_key_values_length + input_shape[1], message=(f"The provided attention mask has length {tf.shape(attention_mask)[1]}, but its length should be {past_key_values_length + input_shape[1]} (sum of the lengths of current and past inputs)"))
        pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
        attention_mask = self._prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values_length)
        if self.project_in is not None: inputs_embeds = self.project_in(inputs_embeds)
        hidden_states = inputs_embeds + pos_embeds
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        present_key_values = () if use_cache else None
        for attn_mask_name, attn_mask in [("head_mask", head_mask)]:
            if attn_mask is not None: tf.debugging.assert_equal(shape_list(attn_mask)[0], len(self.layers), message=(f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for {shape_list(attn_mask)[0]}."))
        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states: all_hidden_states += (hidden_states,)
            past_key_value = past_key_values[idx] if past_key_values is not None else None
            hidden_states, layer_self_attn, present_key_value = decoder_layer(hidden_states, attention_mask=attention_mask, layer_head_mask=head_mask[idx] if head_mask is not None else None, past_key_value=past_key_value)
            if use_cache: present_key_values += (present_key_value,)
            if output_attentions: all_self_attns += (layer_self_attn,)
        if self.final_layer_norm is not None: hidden_states = self.final_layer_norm(hidden_states)
        if self.project_out is not None: hidden_states = self.project_out(hidden_states)
        if output_hidden_states: all_hidden_states += (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, present_key_values, all_hidden_states, all_self_attns] if v is not None)
        else: return TFBaseModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=present_key_values, hidden_states=all_hidden_states, attentions=all_self_attns)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embed_tokens", None) is not None:
            with tf.name_scope(self.embed_tokens.name): self.embed_tokens.build(None)
        if getattr(self, "embed_positions", None) is not None:
            with tf.name_scope(self.embed_positions.name): self.embed_positions.build(None)
        if getattr(self, "final_layer_norm", None) is not None:
            with tf.name_scope(self.final_layer_norm.name): self.final_layer_norm.build([None, None, self.config.hidden_size])
        if getattr(self, "project_out", None) is not None:
            with tf.name_scope(self.project_out.name): self.project_out.build([None, None, self.config.hidden_size])
        if getattr(self, "project_in", None) is not None:
            with tf.name_scope(self.project_in.name): self.project_in.build([None, None, self.config.word_embed_proj_dim])
        if getattr(self, "layers", None) is not None:
            for layer in self.layers:
                with tf.name_scope(layer.name): layer.build(None)
@keras_serializable
class TFOPTMainLayer(keras.layers.Layer):
    config_class = OPTConfig
    def __init__(self, config: OPTConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.decoder = TFOPTDecoder(config, name="decoder")
    def get_input_embeddings(self): return self.decoder.embed_tokens
    def set_input_embeddings(self, new_embeddings): self.decoder.set_input_embeddings(new_embeddings)
    @unpack_inputs
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = 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,
    **kwargs) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.decoder(input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        if not return_dict: return outputs
        return TFBaseModelOutputWithPast(last_hidden_state=outputs.last_hidden_state, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "decoder", None) is not None:
            with tf.name_scope(self.decoder.name): self.decoder.build(None)
@add_start_docstrings("The bare TF OPT Model outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING)
@keras_serializable
class TFOPTModel(TFOPTPreTrainedModel):
    config_class = OPTConfig
    def __init__(self, config: OPTConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.config = config
        self.model = TFOPTMainLayer(config, name="model")
    def get_input_embeddings(self): return self.model.decoder.embed_tokens
    def set_input_embeddings(self, new_embeddings): self.model.set_input_embeddings(new_embeddings)
    @unpack_inputs
    @add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
    def call(self, input_ids: TFModelInputType | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None,
    past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = 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,
    **kwargs) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.model(input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        if not return_dict: return outputs
        return TFBaseModelOutputWithPast(last_hidden_state=outputs.last_hidden_state, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def serving_output(self, output):
        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
        return TFBaseModelOutputWithPast(last_hidden_state=output.last_hidden_state, past_key_values=pkv, hidden_states=hs, attentions=attns)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "model", None) is not None:
            with tf.name_scope(self.model.name): self.model.build(None)
@add_start_docstrings("The OPT Model transformer with a language modeling head on top.", OPT_START_DOCSTRING)
@keras_serializable
class TFOPTForCausalLM(TFOPTPreTrainedModel, TFCausalLanguageModelingLoss):
    config_class = OPTConfig
    def __init__(self, config: OPTConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.config = config
        self.model = TFOPTMainLayer(config, name="model")
    def get_output_embeddings(self): return self.model.get_input_embeddings()
    def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_cache=None, **kwargs):
        attention_mask = kwargs.get("attention_mask", None)
        if past_key_values: inputs = tf.expand_dims(inputs[:, -1], -1)
        return {"input_ids": inputs, "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": use_cache}
    @unpack_inputs
    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,
    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, **kwargs) -> Union[TFCausalLMOutputWithPast, Tuple[tf.Tensor]]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.model(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, 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)
        logits = self.model.decoder.embed_tokens(outputs[0], mode="linear")
        loss = None
        if labels is not None:
            shifted_logits = logits[:, :-1]
            labels = labels[:, 1:]
            loss = self.hf_compute_loss(labels, shifted_logits)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFCausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
    def serving_output(self, output):
        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
        hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
        attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
        return TFCausalLMOutputWithPast(past_key_values=pkv, hidden_states=hs, attentions=attns, loss=output.loss, logits=output.logits)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "model", None) is not None:
            with tf.name_scope(self.model.name): self.model.build(None)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
