'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a=	{
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=	['''CamembertTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=	['''CamembertTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=	[
      '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''CamembertForCausalLM''',
      '''CamembertForMaskedLM''',
      '''CamembertForMultipleChoice''',
      '''CamembertForQuestionAnswering''',
      '''CamembertForSequenceClassification''',
      '''CamembertForTokenClassification''',
      '''CamembertModel''',
      '''CamembertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=	[
      '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFCamembertForCausalLM''',
      '''TFCamembertForMaskedLM''',
      '''TFCamembertForMultipleChoice''',
      '''TFCamembertForQuestionAnswering''',
      '''TFCamembertForSequenceClassification''',
      '''TFCamembertForTokenClassification''',
      '''TFCamembertModel''',
      '''TFCamembertPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_camembert import CamembertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_camembert_fast import CamembertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_camembert import (
        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CamembertForCausalLM,
        CamembertForMaskedLM,
        CamembertForMultipleChoice,
        CamembertForQuestionAnswering,
        CamembertForSequenceClassification,
        CamembertForTokenClassification,
        CamembertModel,
        CamembertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_camembert import (
        TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCamembertForCausalLM,
        TFCamembertForMaskedLM,
        TFCamembertForMultipleChoice,
        TFCamembertForQuestionAnswering,
        TFCamembertForSequenceClassification,
        TFCamembertForTokenClassification,
        TFCamembertModel,
        TFCamembertPreTrainedModel,
    )

else:
  import sys

  a=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMModel,
      TFXLMWithLMHeadModel,
      XLMConfig,
  )






class       __lowercase		:







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						str		     =	parent
         __UpperCamelCase						:						Tuple		     =	1_3
         __UpperCamelCase						:						str		     =	7
         __UpperCamelCase						:						List[str]		     =	True
         __UpperCamelCase						:						Optional[Any]		     =	True
         __UpperCamelCase						:						Tuple		     =	True
         __UpperCamelCase						:						Tuple		     =	True
         __UpperCamelCase						:						int		     =	True
         __UpperCamelCase						:						int		     =	False
         __UpperCamelCase						:						Optional[Any]		     =	False
         __UpperCamelCase						:						Any		     =	False
         __UpperCamelCase						:						Any		     =	2
         __UpperCamelCase						:						str		     =	9_9
         __UpperCamelCase						:						int		     =	0
         __UpperCamelCase						:						Any		     =	3_2
         __UpperCamelCase						:						Tuple		     =	2
         __UpperCamelCase						:						Optional[int]		     =	4
         __UpperCamelCase						:						List[str]		     =	0.1
         __UpperCamelCase						:						List[Any]		     =	0.1
         __UpperCamelCase						:						List[str]		     =	5_1_2
         __UpperCamelCase						:						int		     =	1_6
         __UpperCamelCase						:						Any		     =	2
         __UpperCamelCase						:						Dict		     =	0.0_2
         __UpperCamelCase						:						Any		     =	3
         __UpperCamelCase						:						str		     =	4
         __UpperCamelCase						:						Any		     =	'last'
         __UpperCamelCase						:						int		     =	True
         __UpperCamelCase						:						List[str]		     =	None
         __UpperCamelCase						:						Union[str, Any]		     =	0







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size     )
         __UpperCamelCase						:						str		     =	random_attention_mask([self.batch_size, self.seq_length]  ,      dtype=tf.floataa     )

         __UpperCamelCase						:						Optional[Any]		     =	None
         if self.use_input_lengths:
             __UpperCamelCase						:						Optional[int]		     =	(
                 ids_tensor([self.batch_size]  ,      vocab_size=2     ) + self.seq_length - 2
             )  # small variation of seq_length

         __UpperCamelCase						:						int		     =	None
         if self.use_token_type_ids:
             __UpperCamelCase						:						Union[str, Any]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.n_langs     )

         __UpperCamelCase						:						Optional[Any]		     =	None
         __UpperCamelCase						:						Optional[Any]		     =	None
         __UpperCamelCase						:						Tuple		     =	None
         if self.use_labels:
             __UpperCamelCase						:						Tuple		     =	ids_tensor([self.batch_size]  ,      self.type_sequence_label_size     )
             __UpperCamelCase						:						Dict		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels     )
             __UpperCamelCase						:						Optional[int]		     =	ids_tensor([self.batch_size]  ,      2  ,      dtype=tf.floataa     )
             __UpperCamelCase						:						int		     =	ids_tensor([self.batch_size]  ,      self.num_choices     )

         __UpperCamelCase						:						int		     =	XLMConfig(
             vocab_size=self.vocab_size  ,      n_special=self.n_special  ,      emb_dim=self.hidden_size  ,      n_layers=self.num_hidden_layers  ,      n_heads=self.num_attention_heads  ,      dropout=self.hidden_dropout_prob  ,      attention_dropout=self.attention_probs_dropout_prob  ,      gelu_activation=self.gelu_activation  ,      sinusoidal_embeddings=self.sinusoidal_embeddings  ,      asm=self.asm  ,      causal=self.causal  ,      n_langs=self.n_langs  ,      max_position_embeddings=self.max_position_embeddings  ,      initializer_range=self.initializer_range  ,      summary_type=self.summary_type  ,      use_proj=self.use_proj  ,      bos_token_id=self.bos_token_id  ,      )

         return (
             config,
             input_ids,
             token_type_ids,
             input_lengths,
             sequence_labels,
             token_labels,
             is_impossible_labels,
             choice_labels,
             input_mask,
         )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						str		     =	TFXLMModel(config=_lowerCamelCase     )
         __UpperCamelCase						:						Optional[Any]		     =	{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
         __UpperCamelCase						:						str		     =	model(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	[input_ids, input_mask]
         __UpperCamelCase						:						Tuple		     =	model(_lowerCamelCase     )
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						int		     =	TFXLMWithLMHeadModel(_lowerCamelCase     )

         __UpperCamelCase						:						List[str]		     =	{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
         __UpperCamelCase						:						List[str]		     =	model(_lowerCamelCase     )

         __UpperCamelCase						:						Tuple		     =	outputs

         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						Tuple		     =	TFXLMForQuestionAnsweringSimple(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	{'input_ids': input_ids, 'lengths': input_lengths}

         __UpperCamelCase						:						Optional[Any]		     =	model(_lowerCamelCase     )

         self.parent.assertEqual(result.start_logits.shape  ,      (self.batch_size, self.seq_length)     )
         self.parent.assertEqual(result.end_logits.shape  ,      (self.batch_size, self.seq_length)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						Optional[Any]		     =	TFXLMForSequenceClassification(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	{'input_ids': input_ids, 'lengths': input_lengths}

         __UpperCamelCase						:						Dict		     =	model(_lowerCamelCase     )

         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						int		     =	self.num_labels
         __UpperCamelCase						:						int		     =	TFXLMForTokenClassification(config=_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         __UpperCamelCase						:						List[str]		     =	model(_lowerCamelCase     )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						Any		     =	self.num_choices
         __UpperCamelCase						:						List[Any]		     =	TFXLMForMultipleChoice(config=_lowerCamelCase     )
         __UpperCamelCase						:						Tuple		     =	tf.tile(tf.expand_dims(_lowerCamelCase  ,      1     )  ,      (1, self.num_choices, 1)     )
         __UpperCamelCase						:						Optional[int]		     =	tf.tile(tf.expand_dims(_lowerCamelCase  ,      1     )  ,      (1, self.num_choices, 1)     )
         __UpperCamelCase						:						Union[str, Any]		     =	tf.tile(tf.expand_dims(_lowerCamelCase  ,      1     )  ,      (1, self.num_choices, 1)     )
         __UpperCamelCase						:						str		     =	{
             'input_ids': multiple_choice_inputs_ids,
             'attention_mask': multiple_choice_input_mask,
             'token_type_ids': multiple_choice_token_type_ids,
         }
         __UpperCamelCase						:						Dict		     =	model(_lowerCamelCase     )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_choices)     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       
         )						:						Any		     =	config_and_inputs
         __UpperCamelCase						:						Union[str, Any]		     =	{
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'langs': token_type_ids,
             'lengths': input_lengths,
         }
         return config, inputs_dict






@require_tf
class       __lowercase		(    _lowerCamelCase      ,				_lowerCamelCase      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    (
         (
             TFXLMModel,
             TFXLMWithLMHeadModel,
             TFXLMForSequenceClassification,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForTokenClassification,
             TFXLMForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__		 =    (
         (TFXLMWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     SCREAMING_SNAKE_CASE__		 =    (
         {
             '''feature-extraction''': TFXLMModel,
             '''fill-mask''': TFXLMWithLMHeadModel,
             '''question-answering''': TFXLMForQuestionAnsweringSimple,
             '''text-classification''': TFXLMForSequenceClassification,
             '''text-generation''': TFXLMWithLMHeadModel,
             '''token-classification''': TFXLMForTokenClassification,
             '''zero-shot''': TFXLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    False







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         if (
             pipeline_test_casse_name == "QAPipelineTests"
             and tokenizer_name is not None
             and not tokenizer_name.endswith('Fast'     )
         ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

         return False







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	TFXLMModelTester(self     )
         __UpperCamelCase						:						Union[str, Any]		     =	ConfigTester(self  ,      config_class=_lowerCamelCase  ,      emb_dim=3_7     )







     def lowerCAmelCase  (		self     ):
         self.config_tester.run_common_tests()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_model(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_lm_head(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_qa(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_sequence_classif(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_token_classification(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_multiple_choice(*_lowerCamelCase     )







     @slow
     def lowerCAmelCase  (		self     ):
         for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase						:						List[Any]		     =	TFXLMModel.from_pretrained(_lowerCamelCase     )
             self.assertIsNotNone(_lowerCamelCase     )






@require_tf
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @slow
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Union[str, Any]		     =	TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'     )
         __UpperCamelCase						:						Union[str, Any]		     =	tf.convert_to_tensor([[1_4, 4_4_7]]  ,      dtype=tf.intaa     )  # the president
         __UpperCamelCase						:						List[Any]		     =	[
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
         ]  # the president the president the president the president the president the president the president the president the president the president
         # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
         __UpperCamelCase						:						str		     =	model.generate(_lowerCamelCase  ,      do_sample=_lowerCamelCase     )
         self.assertListEqual(output_ids[0].numpy().tolist()  ,      _lowerCamelCase     )



