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 ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFSequenceClassifierOutput
from ...modeling_tf_utils import (TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, 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
from .configuration_ctrl import CTRLConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "Salesforce/ctrl"
_CONFIG_FOR_DOC = "CTRLConfig"
def angle_defn(pos, i, d_model_size):
    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / d_model_size)
    return pos * angle_rates
def positional_encoding(position, d_model_size):
    angle_rads = angle_defn(np.arange(position)[:, np.newaxis], np.arange(d_model_size)[np.newaxis, :], d_model_size)
    sines = np.sin(angle_rads[:, 0::2])
    cosines = np.cos(angle_rads[:, 1::2])
    pos_encoding = tf.convert_to_tensor(np.concatenate([sines, cosines], axis=-1))
    return pos_encoding
def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
    matmul_qk = tf.matmul(q, k, transpose_b=True)
    dk = tf.cast(shape_list(k)[-1], dtype=matmul_qk.dtype)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
    if mask is not None: scaled_attention_logits += tf.cast(mask * -1e4, dtype=scaled_attention_logits.dtype)
    if attention_mask is not None:
        attention_mask = tf.cast(attention_mask, dtype=scaled_attention_logits.dtype)
        scaled_attention_logits = scaled_attention_logits + attention_mask
    attention_weights = stable_softmax(scaled_attention_logits, axis=-1)
    if head_mask is not None: attention_weights = attention_weights * head_mask
    output = tf.matmul(attention_weights, v)
    return output, attention_weights
class TFMultiHeadAttention(keras.layers.Layer):
    def __init__(self, d_model_size, num_heads, output_attentions=False, **kwargs):
        super().__init__(**kwargs)
        self.num_heads = num_heads
        self.d_model_size = d_model_size
        self.output_attentions = output_attentions
        self.depth = int(d_model_size / self.num_heads)
        self.Wq = keras.layers.Dense(d_model_size, name="Wq")
        self.Wk = keras.layers.Dense(d_model_size, name="Wk")
        self.Wv = keras.layers.Dense(d_model_size, name="Wv")
        self.dense = keras.layers.Dense(d_model_size, name="dense")
    def split_into_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])
    def call(self, v, k, q, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions, training=False):
        batch_size = shape_list(q)[0]
        q = self.Wq(q)
        k = self.Wk(k)
        v = self.Wv(v)
        q = self.split_into_heads(q, batch_size)
        k = self.split_into_heads(k, batch_size)
        v = self.split_into_heads(v, batch_size)
        if layer_past is not None:
            past_key, past_value = tf.unstack(layer_past, axis=0)
            k = tf.concat((past_key, k), axis=-2)
            v = tf.concat((past_value, v), axis=-2)
        if use_cache: present = tf.stack((k, v), axis=0)
        else: present = (None,)
        output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
        scaled_attention = tf.transpose(output[0], perm=[0, 2, 1, 3])
        attn = output[1]
        original_size_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model_size))
        output = self.dense(original_size_attention)
        outputs = (output, present)
        if output_attentions: outputs = outputs + (attn,)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "Wq", None) is not None:
            with tf.name_scope(self.Wq.name): self.Wq.build([None, None, self.d_model_size])
        if getattr(self, "Wk", None) is not None:
            with tf.name_scope(self.Wk.name): self.Wk.build([None, None, self.d_model_size])
        if getattr(self, "Wv", None) is not None:
            with tf.name_scope(self.Wv.name): self.Wv.build([None, None, self.d_model_size])
        if getattr(self, "dense", None) is not None:
            with tf.name_scope(self.dense.name): self.dense.build([None, None, self.d_model_size])
class TFPointWiseFeedForwardLayer(keras.layers.Layer):
    def __init__(self, d_model_size, dff, **kwargs):
        super().__init__(**kwargs)
        self.dense_0 = keras.layers.Dense(dff, activation="relu", name="0")
        self.dense_2 = keras.layers.Dense(d_model_size, name="2")
        self.d_model_size = d_model_size
        self.dff = dff
    def call(self, inputs, trainable=False):
        dense_0_output = self.dense_0(inputs)
        dense_2_output = self.dense_2(dense_0_output)
        return dense_2_output
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "dense_0", None) is not None:
            with tf.name_scope(self.dense_0.name): self.dense_0.build([None, None, self.d_model_size])
        if getattr(self, "dense_2", None) is not None:
            with tf.name_scope(self.dense_2.name): self.dense_2.build([None, None, self.dff])
class TFEncoderLayer(keras.layers.Layer):
    def __init__(self, d_model_size, num_heads, dff, rate=0.1, layer_norm_epsilon=1e-6, output_attentions=False, **kwargs):
        super().__init__(**kwargs)
        self.output_attentions = output_attentions
        self.multi_head_attention = TFMultiHeadAttention(d_model_size, num_heads, output_attentions=self.output_attentions, name="multi_head_attention")
        self.ffn = TFPointWiseFeedForwardLayer(d_model_size, dff, name="ffn")
        self.layernorm1 = keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm1")
        self.layernorm2 = keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm2")
        self.dropout1 = keras.layers.Dropout(rate)
        self.dropout2 = keras.layers.Dropout(rate)
        self.d_model_size = d_model_size
    def call(self, x, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions, training=False):
        normed = self.layernorm1(x)
        attn_outputs = self.multi_head_attention(normed, normed, normed, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions, training=training)
        attn_output = attn_outputs[0]
        attn_output = self.dropout1(attn_output, training=training)
        out1 = x + attn_output
        out2 = self.layernorm2(out1)
        ffn_output = self.ffn(out2)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = out1 + ffn_output
        outputs = (out2,) + attn_outputs[1:]
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "multi_head_attention", None) is not None:
            with tf.name_scope(self.multi_head_attention.name): self.multi_head_attention.build(None)
        if getattr(self, "ffn", None) is not None:
            with tf.name_scope(self.ffn.name): self.ffn.build(None)
        if getattr(self, "layernorm1", None) is not None:
            with tf.name_scope(self.layernorm1.name): self.layernorm1.build([None, None, self.d_model_size])
        if getattr(self, "layernorm2", None) is not None:
            with tf.name_scope(self.layernorm2.name): self.layernorm2.build([None, None, self.d_model_size])
@keras_serializable
class TFCTRLMainLayer(keras.layers.Layer):
    config_class = CTRLConfig
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions
        self.use_cache = config.use_cache
        self.return_dict = config.use_return_dict
        self.d_model_size = config.n_embd
        self.num_layers = config.n_layer
        self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size)
        self.w = keras.layers.Embedding(input_dim=config.vocab_size, output_dim=config.n_embd, embeddings_initializer=get_initializer(config.initializer_range), name="w")
        self.dropout = keras.layers.Dropout(config.embd_pdrop)
        self.h = [TFEncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop, config.layer_norm_epsilon, self.output_attentions, name=f"h_._{i}") for i in range(config.n_layer)]
        self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="layernorm")
    def get_input_embeddings(self): return self.w
    def set_input_embeddings(self, new_embeddings): self.w = new_embeddings
    def _prune_heads(self, heads_to_prune): raise NotImplementedError
    @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,
    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[Tuple, TFBaseModelOutputWithPast]:
        if past_key_values is not None:
            if input_ids is not None: input_ids = input_ids[:, -1:]
            if inputs_embeds is not None: inputs_embeds = inputs_embeds[:, -1:]
            if token_type_ids is not None: token_type_ids = token_type_ids[:, -1:]
        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, dtype=tf.int32), axis=0)
            position_ids = tf.tile(position_ids, [input_shape[0], 1])
        if attention_mask is not None:
            attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1] + past_length))
            one_cst = tf.constant(1.0)
            ten_thousand_cst = tf.constant(-10000.0)
            attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
            attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), ten_thousand_cst)
        if head_mask is not None: raise NotImplementedError
        else: head_mask = [None] * self.num_layers
        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.w(token_type_ids)
            token_type_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, dtype=token_type_embeds.dtype))
        else: token_type_embeds = tf.constant(0.0)
        position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
        if inputs_embeds is None:
            check_embeddings_within_bounds(input_ids, self.w.input_dim)
            inputs_embeds = self.w(input_ids)
        seq_len = input_shape[-1]
        mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
        inputs_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, inputs_embeds.dtype))
        pos_embeds = tf.gather(self.pos_encoding, position_ids)
        pos_embeds = tf.cast(pos_embeds, dtype=token_type_embeds.dtype)
        hidden_states = inputs_embeds + pos_embeds + token_type_embeds
        hidden_states = self.dropout(hidden_states, training=training)
        output_shape = input_shape + [shape_list(hidden_states)[-1]]
        presents = () if use_cache else None
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, (h, 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 = h(hidden_states, mask, layer_past, attention_mask, head_mask[i], use_cache, output_attentions, training=training)
            hidden_states, present = outputs[:2]
            if use_cache: presents = presents + (present,)
            if output_attentions: all_attentions = all_attentions + (outputs[2],)
        hidden_states = self.layernorm(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, "w", None) is not None:
            with tf.name_scope(self.w.name): self.w.build(None)
        if getattr(self, "layernorm", None) is not None:
            with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, self.config.n_embd])
        if getattr(self, "h", None) is not None:
            for layer in self.h:
                with tf.name_scope(layer.name): layer.build(None)
class TFCTRLPreTrainedModel(TFPreTrainedModel):
    config_class = CTRLConfig
    base_model_prefix = "transformer"
CTRL_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 ([`CTRLConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CTRL_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 (`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 (`torch.FloatTensor` 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` or `Numpy array` 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.
        use_cache (`bool`, *optional*):
            If set to `True`, `past` key value states are returned and can be used to speed up decoding (see `past`).
        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).
"""
@add_start_docstrings("The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.", CTRL_START_DOCSTRING)
class TFCTRLModel(TFCTRLPreTrainedModel):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFCTRLMainLayer(config, name="transformer")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(CTRL_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[Tuple, TFBaseModelOutputWithPast]:
        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)
class TFCTRLBiasLayer(keras.layers.Layer):
    def __init__(self, shape, initializer, trainable, name, **kwargs):
        super().__init__(name=name, **kwargs)
        self.shape = shape
        self.initializer = initializer
        self.trainable = trainable
    def build(self, input_shape):
        self.bias = self.add_weight(name="bias", shape=self.shape, initializer=self.initializer, trainable=self.trainable)
        super().build(input_shape)
    def call(self, x): return x + self.bias
@add_start_docstrings("The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).", CTRL_START_DOCSTRING)
class TFCTRLLMHeadModel(TFCTRLPreTrainedModel, TFCausalLanguageModelingLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.transformer = TFCTRLMainLayer(config, name="transformer")
        self.bias_layer = TFCTRLBiasLayer(name="lm_head", shape=[1, config.vocab_size], initializer="zeros", trainable=True)
    def get_output_embeddings(self): return self.get_input_embeddings()
    def set_output_embeddings(self, value): self.set_input_embeddings(value)
    def get_bias(self): return {"lm_head.bias": self.bias_layer.bias}
    def set_bias(self, value):
        vocab_size = value["lm_head.bias"].shape[-1]
        self.bias_layer = TFCTRLBiasLayer(name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=True)
        self.bias_layer.build(None)
        self.bias_layer.bias.assign(value["lm_head.bias"])
    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(CTRL_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, labels: np.ndarray | tf.Tensor | None = None,
    training: Optional[bool] = False) -> Union[Tuple, TFCausalLMOutputWithPast]:
        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 = tf.matmul(hidden_states, self.transformer.w.weights, transpose_b=True)
        logits = self.bias_layer(logits)
        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,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TFCausalLMOutputWithPast(loss=loss, logits=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, "bias_layer", None) is not None:
            with tf.name_scope(self.bias_layer.name): self.bias_layer.build(None)
@add_start_docstrings("""
    The CTRL Model transformer with a sequence classification head on top (linear layer).
    [`TFCTRLForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-1, GPT-2) 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).
    """, CTRL_START_DOCSTRING)
class TFCTRLForSequenceClassification(TFCTRLPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.classifier = keras.layers.Dense(config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier", use_bias=False)
        self.transformer = TFCTRLMainLayer(config, name="transformer")
        self.config = config
    def get_output_embeddings(self):
        logger.warning("Sequence classification models do not have output embeddings. `.get_output_embeddings` will be removed in transformers v4.32.")
        return self.transformer.w
    @unpack_inputs
    @add_start_docstrings_to_model_forward(CTRL_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, labels: np.ndarray | tf.Tensor | None = None,
    training: Optional[bool] = False) -> Union[Tuple, TFSequenceClassifierOutput]:
        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.classifier(hidden_states)
        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, input_ids.shape[-1] - 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 input_ids is not None: batch_size, sequence_length = shape_list(input_ids)[:2]
            else: batch_size, sequence_length = shape_list(inputs_embeds)[:2]
            if self.config.pad_token_id is None and batch_size != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
            if not tf.is_tensor(sequence_lengths): in_logits = logits[0:batch_size, sequence_lengths]
            loss = self.hf_compute_loss(tf.reshape(labels, [-1, 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 TFSequenceClassifierOutput(loss=loss, logits=pooled_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, "classifier", None) is not None:
            with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.n_embd])
        if getattr(self, "transformer", None) is not None:
            with tf.name_scope(self.transformer.name): self.transformer.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.                                                                 #
    #################################################################################################################################################################################################
"""
