code: # coding=utf-8
  # Copyright 2020 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  
  import unittest
  
  from transformers import OpenAIGPTConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ..test_configuration_common import ConfigTester
  from ..test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_tf_available():
      import tensorflow as tf
  
      from transformers.models.openai.modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTModel,
      )
  
  
  class TFOpenAIGPTModelTester:
      def __init__(
          self,
          parent,
      ):
          self.parent = parent
          self.batch_size = 13
          self.seq_length = 7
          self.is_training = True
          self.use_token_type_ids = True
          self.use_input_mask = True
          self.use_labels = True
          self.use_mc_token_ids = True
          self.vocab_size = 99
          self.hidden_size = 32
          self.num_hidden_layers = 5
          self.num_attention_heads = 4
          self.intermediate_size = 37
          self.hidden_act = "gelu"
          self.hidden_dropout_prob = 0.1
          self.attention_probs_dropout_prob = 0.1
          self.max_position_embeddings = 512
          self.type_vocab_size = 16
          self.type_sequence_label_size = 2
          self.initializer_range = 0.02
          self.num_labels = 3
          self.num_choices = 4
          self.scope = None
          self.pad_token_id = self.vocab_size - 1
  
      def prepare_config_and_inputs(self):
          input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
  
          input_mask = None
          if self.use_input_mask:
              input_mask = random_attention_mask([self.batch_size, self.seq_length])
  
          token_type_ids = None
          if self.use_token_type_ids:
              token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
  
          mc_token_ids = None
          if self.use_mc_token_ids:
              mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
  
          sequence_labels = None
          token_labels = None
          choice_labels = None
          if self.use_labels:
              sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
              token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
              choice_labels = ids_tensor([self.batch_size], self.num_choices)
  
          config = OpenAIGPTConfig(
              vocab_size=self.vocab_size,
              n_embd=self.hidden_size,
              n_layer=self.num_hidden_layers,
              n_head=self.num_attention_heads,
              # intermediate_size=self.intermediate_size,
              # hidden_act=self.hidden_act,
              # hidden_dropout_prob=self.hidden_dropout_prob,
              # attention_probs_dropout_prob=self.attention_probs_dropout_prob,
              n_positions=self.max_position_embeddings,
              # type_vocab_size=self.type_vocab_size,
              # initializer_range=self.initializer_range,
              pad_token_id=self.pad_token_id,
          )
  
          head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
  
          return (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          )
  
      def create_and_check_openai_gpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
          model = TFOpenAIGPTModel(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
  
          inputs = [input_ids, input_mask]
          result = model(inputs)
  
          result = model(input_ids)
  
          self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
  
      def create_and_check_openai_gpt_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
          model = TFOpenAIGPTLMHeadModel(config=config)
          inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
  
      def create_and_check_openai_gpt_double_head(
          self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
      ):
          model = TFOpenAIGPTDoubleHeadsModel(config=config)
  
          multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
          multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
          multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
  
          inputs = {
              "input_ids": multiple_choice_inputs_ids,
              "mc_token_ids": mc_token_ids,
              "attention_mask": multiple_choice_input_mask,
              "token_type_ids": multiple_choice_token_type_ids,
          }
          result = model(inputs)
          self.parent.assertEqual(
              result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)
          )
          self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))
  
      def create_and_check_openai_gpt_for_sequence_classification(
          self, config, input_ids, input_mask, head_mask, token_type_ids, *args
      ):
          config.num_labels = self.num_labels
          sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
          inputs = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
              "labels": sequence_labels,
          }
          model = TFOpenAIGPTForSequenceClassification(config)
          result = model(inputs)
          self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
  
      def prepare_config_and_inputs_for_common(self):
          config_and_inputs = self.prepare_config_and_inputs()
  
          (
              config,
              input_ids,
              input_mask,
              head_mask,
              token_type_ids,
              mc_token_ids,
              sequence_labels,
              token_labels,
              choice_labels,
          ) = config_and_inputs
  
          inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict
  
  
  @require_tf
  class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
  
      all_model_classes = (
          (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
          if is_tf_available()
          else ()
      )
      all_generative_model_classes = (
          (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
      test_head_masking = False
      test_onnx = False
  
      def setUp(self):
          self.model_tester = TFOpenAIGPTModelTester(self)
          self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)
  
      def test_config(self):
          self.config_tester.run_common_tests()
  
      def test_openai_gpt_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)
  
      def test_openai_gpt_lm_head(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_lm_head(*config_and_inputs)
  
      def test_openai_gpt_double_head(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_double_head(*config_and_inputs)
  
      def test_model_common_attributes(self):
          config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              model = model_class(config)
              assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
  
              if model_class in self.all_generative_model_classes:
                  x = model.get_output_embeddings()
                  assert isinstance(x, tf.keras.layers.Layer)
                  name = model.get_bias()
                  assert name is None
              else:
                  x = model.get_output_embeddings()
                  assert x is None
                  name = model.get_bias()
                  assert name is None
  
      def test_openai_gpt_sequence_classification_model(self):
          config_and_inputs = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)
  
      @slow
      def test_model_from_pretrained(self):
          for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              model = TFOpenAIGPTModel.from_pretrained(model_name)
              self.assertIsNotNone(model)
  
  
  @require_tf
  class TFOPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
      @slow
      def test_lm_generate_openai_gpt(self):
          model = TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
          input_ids = tf.convert_to_tensor([[481, 4735, 544]], dtype=tf.int32)  # the president is
          expected_output_ids = [
              481,
              4735,
              544,
              246,
              963,
              870,
              762,
              239,
              244,
              40477,
              244,
              249,
              719,
              881,
              487,
              544,
              240,
              244,
              603,
              481,
          ]  # the president is a very good man. " \n " i\'m sure he is, " said the
  
          output_ids = model.generate(input_ids, do_sample=False)
          self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
   # coding=utf-8
  # Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
   TF 2.0 Flaubert model.
  """
  
  import itertools
  import random
  import warnings
  from dataclasses import dataclass
  from typing import Dict, Optional, Tuple, Union
  
  import numpy as np
  import tensorflow as tf
  
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutput
  from ...modeling_tf_utils import (
      TFPreTrainedModel,
      TFSharedEmbeddings,
      get_initializer,
      keras_serializable,
      unpack_inputs,
  )
  from ...tf_utils import shape_list
  from ...utils import (
      ModelOutput,
      add_code_sample_docstrings,
      add_start_docstrings,
      add_start_docstrings_to_model_forward,
      logging,
  )
  from ..xlm.modeling_tf_xlm import (
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
  )
  from .configuration_flaubert import FlaubertConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "flaubert/flaubert_base_cased"
  _CONFIG_FOR_DOC = "FlaubertConfig"
  _TOKENIZER_FOR_DOC = "FlaubertTokenizer"
  
  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
      # See all Flaubert models at https://huggingface.co/models?filter=flaubert
  ]
  
  FLAUBERT_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 [tf.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>
  
      TF 2.0 models accepts two formats as inputs:
  
      - having all inputs as keyword arguments (like PyTorch models), or
      - having all inputs as a list, tuple or dict in the first positional arguments.
  
      This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
      tensors in the first argument of the model call function: `model(inputs)`.
  
      If you choose this second option, 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(inputs_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})`
  
      </Tip>
  
      Parameters:
          config ([`FlaubertConfig`]): 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.
  """
  
  FLAUBERT_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary.
  
              Indices can be obtained using [`FlaubertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
              [`PreTrainedTokenizer.encode`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`Numpy array` or `tf.Tensor` 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)
          langs (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
              A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
              languages ids which can be obtained from the language names by using two conversion mappings provided in
              the configuration of the model (only provided for multilingual models). More precisely, the *language name
              to language id* mapping is in `model.config.lang2id` (which is a dictionary string to int) and the
              *language id to language name* mapping is in `model.config.id2lang` (dictionary int to string).
  
              See usage examples detailed in the [multilingual documentation](../multilingual).
          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)
          lengths (`tf.Tensor` or `Numpy array` of shape `(batch_size,)`, *optional*):
              Length of each sentence that can be used to avoid performing attention on padding token indices. You can
              also use *attention_mask* for the same result (see above), kept here for compatibility Indices selected in
              `[0, ..., input_ids.size(-1)]`:
          cache (`Dict[str, tf.Tensor]`, *optional*):
              Dictionary string to `tf.FloatTensor` that contains precomputed hidden states (key and values in the
              attention blocks) as computed by the model (see `cache` output below). Can be used to speed up sequential
              decoding.
  
              The dictionary object will be modified in-place during the forward pass to add newly computed
              hidden-states.
          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 [`~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).
  """
  
  
  def get_masks(slen, lengths, causal, padding_mask=None):
      """
      Generate hidden states mask, and optionally an attention mask.
      """
      bs = shape_list(lengths)[0]
      if padding_mask is not None:
          mask = padding_mask
      else:
          # assert lengths.max().item() <= slen
          alen = tf.range(slen)
          mask = tf.math.less(alen, tf.expand_dims(lengths, axis=1))
  
      # attention mask is the same as mask, or triangular inferior attention (causal)
      if causal:
          attn_mask = tf.less_equal(
              tf.tile(tf.reshape(alen, (1, 1, slen)), (bs, slen, 1)), tf.reshape(alen, (1, slen, 1))
          )
      else:
          attn_mask = mask
  
      # sanity check
      # assert shape_list(mask) == [bs, slen]
      if tf.executing_eagerly():
          tf.debugging.assert_equal(shape_list(mask), [bs, slen])
          assert causal is False or shape_list(attn_mask) == [bs, slen, slen]
  
      return mask, attn_mask
  
  
  class TFFlaubertPreTrainedModel(TFPreTrainedModel):
      """
      An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
      models.
      """
  
      config_class = FlaubertConfig
      base_model_prefix = "transformer"
  
      @property
      def dummy_inputs(self):
          # Sometimes XLM has language embeddings so don't forget to build them as well if needed
          inputs_list = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
          attns_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
          if self.config.use_lang_emb and self.config.n_langs > 1:
              return {
                  "input_ids": inputs_list,
                  "attention_mask": attns_list,
                  "langs": tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]]),
              }
          else:
              return {"input_ids": inputs_list, "attention_mask": attns_list}
  
  
  @add_start_docstrings(
      "The bare Flaubert Model transformer outputting raw hidden-states without any specific head on top.",
      FLAUBERT_START_DOCSTRING,
  )
  class TFFlaubertModel(TFFlaubertPreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.transformer = TFFlaubertMainLayer(config, name="transformer")
  
      @unpack_inputs
      @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFBaseModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
          cache: Optional[Dict[str, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: Optional[bool] = False,
      ) -> Union[Tuple, TFBaseModelOutput]:
          outputs = self.transformer(
              input_ids=input_ids,
              attention_mask=attention_mask,
              langs=langs,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              lengths=lengths,
              cache=cache,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
  
          return outputs
  
      # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
      def serving_output(self, output):
          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 TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
  
  
  # Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMMultiHeadAttention with XLM->Flaubert
  class TFFlaubertMultiHeadAttention(tf.keras.layers.Layer):
      NEW_ID = itertools.count()
  
      def __init__(self, n_heads, dim, config, **kwargs):
          super().__init__(**kwargs)
          self.layer_id = next(TFFlaubertMultiHeadAttention.NEW_ID)
          self.dim = dim
          self.n_heads = n_heads
          self.output_attentions = config.output_attentions
          assert self.dim % self.n_heads == 0
  
          self.q_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="q_lin")
          self.k_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="k_lin")
          self.v_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="v_lin")
          self.out_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="out_lin")
          self.dropout = tf.keras.layers.Dropout(config.attention_dropout)
          self.pruned_heads = set()
  
      def prune_heads(self, heads):
          raise NotImplementedError
  
      def call(self, input, mask, kv, cache, head_mask, output_attentions, training=False):
          """
          Self-attention (if kv is None) or attention over source sentence (provided by kv).
          """
          # Input is (bs, qlen, dim)
          # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
          bs, qlen, dim = shape_list(input)
  
          if kv is None:
              klen = qlen if cache is None else cache["slen"] + qlen
          else:
              klen = shape_list(kv)[1]
  
          # assert dim == self.dim, f'Dimensions do not match: {dim} input vs {self.dim} configured'
          dim_per_head = self.dim // self.n_heads
          mask_reshape = (bs, 1, qlen, klen) if len(shape_list(mask)) == 3 else (bs, 1, 1, klen)
  
          def shape(x):
              """projection"""
              return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
  
          def unshape(x):
              """compute context"""
              return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
  
          q = shape(self.q_lin(input))  # (bs, n_heads, qlen, dim_per_head)
  
          if kv is None:
              k = shape(self.k_lin(input))  # (bs, n_heads, qlen, dim_per_head)
              v = shape(self.v_lin(input))  # (bs, n_heads, qlen, dim_per_head)
          elif cache is None or self.layer_id not in cache:
              k = v = kv
              k = shape(self.k_lin(k))  # (bs, n_heads, qlen, dim_per_head)
              v = shape(self.v_lin(v))  # (bs, n_heads, qlen, dim_per_head)
  
          if cache is not None:
              if self.layer_id in cache:
                  if kv is None:
                      k_, v_ = cache[self.layer_id]
                      k = tf.concat([k_, k], axis=2)  # (bs, n_heads, klen, dim_per_head)
                      v = tf.concat([v_, v], axis=2)  # (bs, n_heads, klen, dim_per_head)
                  else:
                      k, v = cache[self.layer_id]
  
              cache[self.layer_id] = (k, v)
  
          f_dim_per_head = tf.cast(dim_per_head, dtype=q.dtype)
          q = tf.multiply(q, tf.math.rsqrt(f_dim_per_head))  # (bs, n_heads, qlen, dim_per_head)
          k = tf.cast(k, dtype=q.dtype)
          scores = tf.matmul(q, k, transpose_b=True)  # (bs, n_heads, qlen, klen)
          mask = tf.reshape(mask, mask_reshape)  # (bs, n_heads, qlen, klen)
          # scores.masked_fill_(mask, -float('inf'))                            # (bs, n_heads, qlen, klen)
          mask = tf.cast(mask, dtype=scores.dtype)
          scores = scores - 1e30 * (1.0 - mask)
          weights = tf.nn.softmax(scores, axis=-1)  # (bs, n_heads, qlen, klen)
          weights = self.dropout(weights, training=training)  # (bs, n_heads, qlen, klen)
  
          # Mask heads if we want to
          if head_mask is not None:
              weights = weights * head_mask
  
          context = tf.matmul(weights, v)  # (bs, n_heads, qlen, dim_per_head)
          context = unshape(context)  # (bs, qlen, dim)
          outputs = (self.out_lin(context),)
  
          if output_attentions:
              outputs = outputs + (weights,)
  
          return outputs
  
  
  # Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMTransformerFFN
  class TFFlaubertTransformerFFN(tf.keras.layers.Layer):
      def __init__(self, in_dim, dim_hidden, out_dim, config, **kwargs):
          super().__init__(**kwargs)
  
          self.lin1 = tf.keras.layers.Dense(dim_hidden, kernel_initializer=get_initializer(config.init_std), name="lin1")
          self.lin2 = tf.keras.layers.Dense(out_dim, kernel_initializer=get_initializer(config.init_std), name="lin2")
          self.act = get_tf_activation("gelu") if config.gelu_activation else get_tf_activation("relu")
          self.dropout = tf.keras.layers.Dropout(config.dropout)
  
      def call(self, input, training=False):
          x = self.lin1(input)
          x = self.act(x)
          x = self.lin2(x)
          x = self.dropout(x, training=training)
  
          return x
  
  
  @keras_serializable
  class TFFlaubertMainLayer(tf.keras.layers.Layer):
      config_class = FlaubertConfig
  
      def __init__(self, config, **kwargs):
          super().__init__(**kwargs)
  
          self.config = config
          self.n_heads = config.n_heads
          self.n_langs = config.n_langs
          self.dim = config.emb_dim
          self.hidden_dim = self.dim * 4
          self.n_words = config.n_words
          self.pad_index = config.pad_index
          self.causal = config.causal
          self.n_layers = config.n_layers
          self.use_lang_emb = config.use_lang_emb
          self.layerdrop = getattr(config, "layerdrop", 0.0)
          self.pre_norm = getattr(config, "pre_norm", False)
          self.output_attentions = config.output_attentions
          self.output_hidden_states = config.output_hidden_states
          self.return_dict = config.use_return_dict
          self.max_position_embeddings = config.max_position_embeddings
          self.embed_init_std = config.embed_init_std
          self.dropout = tf.keras.layers.Dropout(config.dropout)
          self.embeddings = TFSharedEmbeddings(
              self.n_words, self.dim, initializer_range=config.embed_init_std, name="embeddings"
          )
          self.layer_norm_emb = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm_emb")
          self.attentions = []
          self.layer_norm1 = []
          self.ffns = []
          self.layer_norm2 = []
  
          for i in range(self.n_layers):
              self.attentions.append(
                  TFFlaubertMultiHeadAttention(self.n_heads, self.dim, config=config, name=f"attentions_._{i}")
              )
              self.layer_norm1.append(
                  tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name=f"layer_norm1_._{i}")
              )
              # if self.is_decoder:
              #     self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps))
              #     self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout))
              self.ffns.append(
                  TFFlaubertTransformerFFN(self.dim, self.hidden_dim, self.dim, config=config, name=f"ffns_._{i}")
              )
              self.layer_norm2.append(
                  tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name=f"layer_norm2_._{i}")
              )
  
      def build(self, input_shape):
          with tf.name_scope("position_embeddings"):
              self.position_embeddings = self.add_weight(
                  name="embeddings",
                  shape=[self.max_position_embeddings, self.dim],
                  initializer=get_initializer(self.embed_init_std),
              )
  
          if self.n_langs > 1 and self.use_lang_emb:
              with tf.name_scope("lang_embeddings"):
                  self.lang_embeddings = self.add_weight(
                      name="embeddings",
                      shape=[self.n_langs, self.dim],
                      initializer=get_initializer(self.embed_init_std),
                  )
  
          super().build(input_shape)
  
      def get_input_embeddings(self):
          return self.embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.weight = value
          self.embeddings.vocab_size = shape_list(value)[0]
  
      @unpack_inputs
      def call(
          self,
          input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
          cache: Optional[Dict[str, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: Optional[bool] = False,
      ) -> Union[Tuple, TFBaseModelOutput]:
          # removed: src_enc=None, src_len=None
  
          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:
              bs, slen = shape_list(input_ids)
          elif inputs_embeds is not None:
              bs, slen = shape_list(inputs_embeds)[:2]
          else:
              raise ValueError("You have to specify either input_ids or inputs_embeds")
  
          if lengths is None:
              if input_ids is not None:
                  lengths = tf.reduce_sum(
                      tf.cast(tf.not_equal(input_ids, self.pad_index), dtype=input_ids.dtype), axis=1
                  )
              else:
                  lengths = tf.convert_to_tensor([slen] * bs)
          # mask = input_ids != self.pad_index
  
          # check inputs
          # assert shape_list(lengths)[0] == bs
          if tf.executing_eagerly():
              tf.debugging.assert_equal(
                  shape_list(lengths)[0], bs
              ), f"Expected batch size {shape_list(lengths)[0]} and received batch size {bs} mismatched"
          # assert lengths.max().item() <= slen
          # input_ids = input_ids.transpose(0, 1)  # batch size as dimension 0
          # assert (src_enc is None) == (src_len is None)
          # if src_enc is not None:
          #     assert self.is_decoder
          #     assert src_enc.size(0) == bs
  
          # generate masks
          mask, attn_mask = get_masks(slen, lengths, self.causal, padding_mask=attention_mask)
          # if self.is_decoder and src_enc is not None:
          #     src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
  
          # position_ids
          if position_ids is None:
              position_ids = tf.expand_dims(tf.range(slen), axis=0)
              position_ids = tf.tile(position_ids, (bs, 1))
  
          if tf.executing_eagerly():
              # assert shape_list(position_ids) == [bs, slen]  # (slen, bs)
              tf.debugging.assert_equal(
                  shape_list(position_ids), [bs, slen]
              ), f"Position id shape {shape_list(position_ids)} and input shape {[bs, slen]} mismatched"
              # position_ids = position_ids.transpose(0, 1)
  
          # langs
          if langs is not None and tf.executing_eagerly():
              # assert shape_list(langs) == [bs, slen]  # (slen, bs)
              tf.debugging.assert_equal(
                  shape_list(langs), [bs, slen]
              ), f"Lang shape {shape_list(langs)} and input shape {[bs, slen]} mismatched"
              # langs = langs.transpose(0, 1)
  
          # Prepare head mask if needed
          # 1.0 in head_mask indicate we keep the head
          # attention_probs has shape bsz x n_heads x N x N
          # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
          # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x qlen x klen]
          if head_mask is not None:
              raise NotImplementedError
          else:
              head_mask = [None] * self.n_layers
  
          # do not recompute cached elements
          if cache is not None and input_ids is not None:
              _slen = slen - cache["slen"]
              input_ids = input_ids[:, -_slen:]
              position_ids = position_ids[:, -_slen:]
              if langs is not None:
                  langs = langs[:, -_slen:]
              mask = mask[:, -_slen:]
              attn_mask = attn_mask[:, -_slen:]
  
          # embeddings
          if inputs_embeds is None:
              inputs_embeds = self.embeddings(input_ids)
  
          tensor = inputs_embeds + tf.gather(self.position_embeddings, position_ids)
  
          if langs is not None and self.use_lang_emb:
              tensor = tensor + tf.gather(self.lang_embeddings, langs)
          if token_type_ids is not None:
              tensor = tensor + self.embeddings(token_type_ids)
  
          tensor = self.layer_norm_emb(tensor)
          tensor = self.dropout(tensor, training=training)
          mask = tf.cast(mask, dtype=tensor.dtype)
          tensor = tensor * tf.expand_dims(mask, axis=-1)
  
          # hidden_states and attentions cannot be None in graph mode.
          hidden_states = () if output_hidden_states else None
          attentions = () if output_attentions else None
  
          # transformer layers
          for i in range(self.n_layers):
              # LayerDrop
              dropout_probability = random.uniform(0, 1)
  
              if training and (dropout_probability < self.layerdrop):
                  continue
  
              if output_hidden_states:
                  hidden_states = hidden_states + (tensor,)
  
              # self attention
              if not self.pre_norm:
                  attn_outputs = self.attentions[i](
                      tensor,
                      attn_mask,
                      None,
                      cache,
                      head_mask[i],
                      output_attentions,
                      training=training,
                  )
                  attn = attn_outputs[0]
  
                  if output_attentions:
                      attentions = attentions + (attn_outputs[1],)
  
                  attn = self.dropout(attn, training=training)
                  tensor = tensor + attn
                  tensor = self.layer_norm1[i](tensor)
              else:
                  tensor_normalized = self.layer_norm1[i](tensor)
                  attn_outputs = self.attentions[i](
                      tensor_normalized,
                      attn_mask,
                      None,
                      cache,
                      head_mask[i],
                      output_attentions,
                      training=training,
                  )
                  attn = attn_outputs[0]
  
                  if output_attentions:
                      attentions = attentions + (attn_outputs[1],)
  
                  attn = self.dropout(attn, training=training)
                  tensor = tensor + attn
  
              # encoder attention (for decoder only)
              # if self.is_decoder and src_enc is not None:
              #     attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache)
              #     attn = nn.functional.dropout(attn, p=self.dropout, training=self.training)
              #     tensor = tensor + attn
              #     tensor = self.layer_norm15[i](tensor)
  
              # FFN
              if not self.pre_norm:
                  tensor = tensor + self.ffns[i](tensor)
                  tensor = self.layer_norm2[i](tensor)
              else:
                  tensor_normalized = self.layer_norm2[i](tensor)
                  tensor = tensor + self.ffns[i](tensor_normalized)
  
              tensor = tensor * tf.expand_dims(mask, axis=-1)
  
          # Add last hidden state
          if output_hidden_states:
              hidden_states = hidden_states + (tensor,)
  
          # update cache length
          if cache is not None:
              cache["slen"] += tensor.size(1)
  
          # move back sequence length to dimension 0
          # tensor = tensor.transpose(0, 1)
  
          if not return_dict:
              return tuple(v for v in [tensor, hidden_states, attentions] if v is not None)
  
          return TFBaseModelOutput(last_hidden_state=tensor, hidden_states=hidden_states, attentions=attentions)
  
  
  # Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMPredLayer
  class TFFlaubertPredLayer(tf.keras.layers.Layer):
      """
      Prediction layer (cross_entropy or adaptive_softmax).
      """
  
      def __init__(self, config, input_embeddings, **kwargs):
          super().__init__(**kwargs)
  
          self.asm = config.asm
          self.n_words = config.n_words
          self.pad_index = config.pad_index
  
          if config.asm is False:
              self.input_embeddings = input_embeddings
          else:
              raise NotImplementedError
              # self.proj = nn.AdaptiveLogSoftmaxWithLoss(
              #     in_features=dim,
              #     n_classes=config.n_words,
              #     cutoffs=config.asm_cutoffs,
              #     div_value=config.asm_div_value,
              #     head_bias=True,  # default is False
              # )
  
      def build(self, input_shape):
          # The output weights are the same as the input embeddings, but there is an output-only bias for each token.
          self.bias = self.add_weight(shape=(self.n_words,), initializer="zeros", trainable=True, name="bias")
  
          super().build(input_shape)
  
      def get_output_embeddings(self):
          return self.input_embeddings
  
      def set_output_embeddings(self, value):
          self.input_embeddings.weight = value
          self.input_embeddings.vocab_size = shape_list(value)[0]
  
      def get_bias(self):
          return {"bias": self.bias}
  
      def set_bias(self, value):
          self.bias = value["bias"]
          self.vocab_size = shape_list(value["bias"])[0]
  
      def call(self, hidden_states):
          hidden_states = self.input_embeddings(hidden_states, mode="linear")
          hidden_states = hidden_states + self.bias
  
          return hidden_states
  
  
  @dataclass
  class TFFlaubertWithLMHeadModelOutput(ModelOutput):
      """
      Base class for [`TFFlaubertWithLMHeadModel`] outputs.
  
      Args:
          logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
              Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
          hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
              Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
              `(batch_size, sequence_length, hidden_size)`.
  
              Hidden-states of the model at the output of each layer plus the initial embedding outputs.
          attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
              Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
              sequence_length)`.
  
              Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
              heads.
      """
  
      logits: tf.Tensor = None
      hidden_states: Optional[Tuple[tf.Tensor]] = None
      attentions: Optional[Tuple[tf.Tensor]] = None
  
  
  @add_start_docstrings(
      """
      The Flaubert Model transformer with a language modeling head on top (linear layer with weights tied to the input
      embeddings).
      """,
      FLAUBERT_START_DOCSTRING,
  )
  class TFFlaubertWithLMHeadModel(TFFlaubertPreTrainedModel):
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.transformer = TFFlaubertMainLayer(config, name="transformer")
          self.pred_layer = TFFlaubertPredLayer(config, self.transformer.embeddings, name="pred_layer_._proj")
  
      def get_lm_head(self):
          return self.pred_layer
  
      def get_prefix_bias_name(self):
          warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
          return self.name + "/" + self.pred_layer.name
  
      def prepare_inputs_for_generation(self, inputs, **kwargs):
          mask_token_id = self.config.mask_token_id
          lang_id = self.config.lang_id
  
          effective_batch_size = inputs.shape[0]
          mask_token = tf.fill((effective_batch_size, 1), 1) * mask_token_id
          inputs = tf.concat([inputs, mask_token], axis=1)
  
          if lang_id is not None:
              langs = tf.ones_like(inputs) * lang_id
          else:
              langs = None
          return {"input_ids": inputs, "langs": langs}
  
      @unpack_inputs
      @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING)
      @add_code_sample_docstrings(
          processor_class=_TOKENIZER_FOR_DOC,
          checkpoint=_CHECKPOINT_FOR_DOC,
          output_type=TFFlaubertWithLMHeadModelOutput,
          config_class=_CONFIG_FOR_DOC,
      )
      def call(
          self,
          input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
          token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
          lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
          cache: Optional[Dict[str, tf.Tensor]] = None,
          head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
          inputs_embeds: Optional[tf.Tensor] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          training: Optional[bool] = False,
      ) -> Union[Tuple, TFFlaubertWithLMHeadModelOutput]:
  
          transformer_outputs = self.transformer(
              input_ids=input_ids,
              attention_mask=attention_mask,
              langs=langs,
              token_type_ids=token_type_ids,
              position_ids=position_ids,
              lengths=lengths,
              cache=cache,
              head_mask=head_mask,
              inputs_embeds=inputs_embeds,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              training=training,
          )
          output = transformer_outputs[0]
          outputs = self.pred_layer(output)
  
          if not return_dict:
              return (outputs,) + transformer_outputs[1:]
  
          return TFFlaubertWithLMHeadModelOutput(
              logits=outputs, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions
          )
  
      def serving_output(self, output):
          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 TFFlaubertWithLMHeadModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
  
  
  @add_start_docstrings(
      """
      Flaubert Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
      e.g. for GLUE tasks.
      """,
      FLAUBERT_START_DOCSTRING,
  )
  class TFFlaubertForSequenceClassification(TFXLMForSequenceClassification):
      config_class = FlaubertConfig
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.transformer = TFFlaubertMainLayer(config, name="transformer")
  
  
  @add_start_docstrings(
      """
      Flaubert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
      layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
      """,
      FLAUBERT_START_DOCSTRING,
  )
  class TFFlaubertForQuestionAnsweringSimple(TFXLMForQuestionAnsweringSimple):
      config_class = FlaubertConfig
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.transformer = TFFlaubertMainLayer(config, name="transformer")
  
  
  @add_start_docstrings(
      """
      Flaubert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
      Named-Entity-Recognition (NER) tasks.
      """,
      FLAUBERT_START_DOCSTRING,
  )
  class TFFlaubertForTokenClassification(TFXLMForTokenClassification):
      config_class = FlaubertConfig
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.transformer = TFFlaubertMainLayer(config, name="transformer")
  
  
  @add_start_docstrings(
      """
      Flaubert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
      softmax) e.g. for RocStories/SWAG tasks.
      """,
      FLAUBERT_START_DOCSTRING,
  )
  class TFFlaubertForMultipleChoice(TFXLMForMultipleChoice):
      config_class = FlaubertConfig
  
      def __init__(self, config, *inputs, **kwargs):
          super().__init__(config, *inputs, **kwargs)
          self.transformer = TFFlaubertMainLayer(config, name="transformer")
  
apis: tensorflow.expand_dims tensorflow.convert_to_tensor
apis: tensorflow.reshape tensorflow.math.rsqrt tensorflow.matmul tensorflow.name_scope tensorflow.convert_to_tensor tensorflow.concat tensorflow.executing_eagerly tensorflow.nn.softmax tensorflow.constant tensorflow.transpose tensorflow.keras.layers.Dropout tensorflow.ones_like tensorflow.expand_dims tensorflow.cast tensorflow.not_equal tensorflow.tile tensorflow.keras.layers.LayerNormalization tensorflow.range tensorflow.fill tensorflow.gather