




"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
  import tensorflow as tf

  from transformers import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertConfig,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertWithLMHeadModel,
  )

class 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					)				->   List[Any]:
       UpperCamelCase              =	parent
       UpperCamelCase              =	1_3
       UpperCamelCase              =	7
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	True
       UpperCamelCase              =	False
       UpperCamelCase              =	False
       UpperCamelCase              =	False
       UpperCamelCase              =	2
       UpperCamelCase              =	9_9
       UpperCamelCase              =	0
       UpperCamelCase              =	3_2
       UpperCamelCase              =	2
       UpperCamelCase              =	4
       UpperCamelCase              =	0.1
       UpperCamelCase              =	0.1
       UpperCamelCase              =	5_1_2
       UpperCamelCase              =	1_6
       UpperCamelCase              =	2
       UpperCamelCase              =	0.02
       UpperCamelCase              =	3
       UpperCamelCase              =	4
       UpperCamelCase              =	'''last'''
       UpperCamelCase              =	True
       UpperCamelCase              =	None
       UpperCamelCase              =	0







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size)
       UpperCamelCase              =	random_attention_mask([self.batch_size, self.seq_length]       ,					dtype=tf.floataa)

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

       UpperCamelCase              =	None
       if self.use_token_type_ids:
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.n_langs)

       UpperCamelCase              =	None
       UpperCamelCase              =	None
       UpperCamelCase              =	None
       if self.use_labels:
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.type_sequence_label_size)
           UpperCamelCase              =	ids_tensor([self.batch_size, self.seq_length]       ,					self.num_labels)
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					2       ,					dtype=tf.floataa)
           UpperCamelCase              =	ids_tensor([self.batch_size]       ,					self.num_choices)

       UpperCamelCase              =	FlaubertConfig(
           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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	TFFlaubertModel(config=lowerCamelCase_)
       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
       UpperCamelCase              =	model(lowerCamelCase_)

       UpperCamelCase              =	[input_ids, input_mask]
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.last_hidden_state.shape       ,					(self.batch_size, self.seq_length, self.hidden_size))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Tuple:
       UpperCamelCase              =	TFFlaubertWithLMHeadModel(lowerCamelCase_)

       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
       UpperCamelCase              =	model(lowerCamelCase_)

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







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	TFFlaubertForQuestionAnsweringSimple(lowerCamelCase_)

       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths}

       UpperCamelCase              =	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   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   str:
       UpperCamelCase              =	TFFlaubertForSequenceClassification(lowerCamelCase_)

       UpperCamelCase              =	{'''input_ids''': input_ids, '''lengths''': input_lengths}

       UpperCamelCase              =	model(lowerCamelCase_)

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







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	self.num_labels
       UpperCamelCase              =	TFFlaubertForTokenClassification(config=lowerCamelCase_)
       UpperCamelCase              =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.seq_length, self.num_labels))







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					)				->   Optional[Any]:
       UpperCamelCase              =	self.num_choices
       UpperCamelCase              =	TFFlaubertForMultipleChoice(config=lowerCamelCase_)
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	tf.tile(tf.expand_dims(lowerCamelCase_       ,					1)       ,					(1, self.num_choices, 1))
       UpperCamelCase              =	{
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
           '''token_type_ids''': multiple_choice_token_type_ids,
       }
       UpperCamelCase              =	model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_choices))







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	(
           UpperCamelCase
       )       ,	
       )              =	config_and_inputs
       UpperCamelCase              =	{
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''langs''': token_type_ids,
           '''lengths''': input_lengths,
       }
       return config, inputs_dict

@require_tf
class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       (
       (
           TFFlaubertModel,
           TFFlaubertWithLMHeadModel,
           TFFlaubertForSequenceClassification,
           TFFlaubertForQuestionAnsweringSimple,
           TFFlaubertForTokenClassification,
           TFFlaubertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   A_				      =       (
       (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   A_				      =       (
       {
           '''feature-extraction''': TFFlaubertModel,
           '''fill-mask''': TFFlaubertWithLMHeadModel,
           '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
           '''text-classification''': TFFlaubertForSequenceClassification,
           '''token-classification''': TFFlaubertForTokenClassification,
           '''zero-shot''': TFFlaubertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_				      =       False
   A_				      =       False







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   List[str]:
       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   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	TFFlaubertModelTester(self)
       UpperCamelCase              =	ConfigTester(self       ,					config_class=lowerCamelCase_       ,					emb_dim=3_7)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       self.config_tester.run_common_tests()







   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_model(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_lm_head(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_qa(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_sequence_classif(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_token_classification(*lowerCamelCase_)







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowerCamelCase_)







   @slow
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase              =	TFFlaubertModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)

@require_tf
@require_sentencepiece
@require_tokenizers
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   @slow
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       UpperCamelCase              =	TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased''')

       UpperCamelCase              =	tf.convert_to_tensor(
           [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]]       ,					dtype=tf.intaa       ,					)  # "J'aime flaubert !"

       UpperCamelCase              =	model(lowerCamelCase_)[0]
       UpperCamelCase              =	tf.TensorShape((1, 8, 5_1_2))
       self.assertEqual(output.shape       ,					lowerCamelCase_)
       # compare the actual values for a slice.
       UpperCamelCase              =	tf.convert_to_tensor(
           [
               [
                   [-1.876_8773, -1.56_6555, 0.2707_2418],
                   [-1.692_0038, -0.587_3505, 1.932_9599],
                   [-2.956_3985, -1.699_3835, 1.797_2052],
               ]
           ]       ,					dtype=tf.floataa       ,					)

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,					expected_slice.numpy()       ,					atol=1e-4))





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__  :  List[str]					=       {
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Union[str, Any]					=       [
          """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """M2M100ForConditionalGeneration""",
          """M2M100Model""",
          """M2M100PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  List[str]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

