

'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowerCamelCase    =  {
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    =  ['FlavaFeatureExtractor']
							_lowerCamelCase    =  ['FlavaImageProcessor']
							_lowerCamelCase    =  ['FlavaProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase    =  [
							    'FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'FlavaForPreTraining',
							    'FlavaImageCodebook',
							    'FlavaImageModel',
							    'FlavaModel',
							    'FlavaMultimodalModel',
							    'FlavaPreTrainedModel',
							    'FlavaTextModel',
							]

if TYPE_CHECKING:
							from .configuration_flava import (
							    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    FlavaConfig,
							    FlavaImageCodebookConfig,
							    FlavaImageConfig,
							    FlavaMultimodalConfig,
							    FlavaTextConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_flava import FlavaFeatureExtractor
														from .image_processing_flava import FlavaImageProcessor
														from .processing_flava import FlavaProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flava import (
														    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlavaForPreTraining,
														    FlavaImageCodebook,
														    FlavaImageModel,
														    FlavaModel,
														    FlavaMultimodalModel,
														    FlavaPreTrainedModel,
														    FlavaTextModel,
														)

else:
							import sys

							_lowerCamelCase    =  _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 A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       parent
     UpperCAmelCase_   :   Any     	=       13
     UpperCAmelCase_   :   Dict     	=       7
     UpperCAmelCase_   :   str     	=       True
     UpperCAmelCase_   :   Optional[int]     	=       True
     UpperCAmelCase_   :   Optional[Any]     	=       True
     UpperCAmelCase_   :   Tuple     	=       True
     UpperCAmelCase_   :   Tuple     	=       True
     UpperCAmelCase_   :   str     	=       False
     UpperCAmelCase_   :   Optional[Any]     	=       False
     UpperCAmelCase_   :   Any     	=       False
     UpperCAmelCase_   :   Tuple     	=       2
     UpperCAmelCase_   :   str     	=       99
     UpperCAmelCase_   :   str     	=       0
     UpperCAmelCase_   :   Dict     	=       32
     UpperCAmelCase_   :   Dict     	=       2
     UpperCAmelCase_   :   Union[str, Any]     	=       4
     UpperCAmelCase_   :   Union[str, Any]     	=       0.1
     UpperCAmelCase_   :   Dict     	=       0.1
     UpperCAmelCase_   :   Optional[Any]     	=       512
     UpperCAmelCase_   :   Any     	=       16
     UpperCAmelCase_   :   Tuple     	=       2
     UpperCAmelCase_   :   int     	=       0.02
     UpperCAmelCase_   :   Union[str, Any]     	=       3
     UpperCAmelCase_   :   str     	=       4
     UpperCAmelCase_   :   Union[str, Any]     	=       "last"
     UpperCAmelCase_   :   Optional[int]     	=       True
     UpperCAmelCase_   :   str     	=       None
     UpperCAmelCase_   :   Dict     	=       0







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size    )
     UpperCAmelCase_   :   Any     	=       random_attention_mask([self.batch_size, self.seq_length]							,  dtype=tf.floataa    )

     UpperCAmelCase_   :   Union[str, Any]     	=       None
     if self.use_input_lengths:
        UpperCAmelCase_   :   Dict     	=       (
            ids_tensor([self.batch_size]							,  vocab_size=2    ) + self.seq_length - 2
        )  # small variation of seq_length

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

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

     UpperCAmelCase_   :   Optional[Any]     	=       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 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       TFXLMModel(config=lowercase_    )
     UpperCAmelCase_   :   Any     	=       {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
     UpperCAmelCase_   :   Tuple     	=       model(lowercase_    )

     UpperCAmelCase_   :   Any     	=       [input_ids, input_mask]
     UpperCAmelCase_   :   Union[str, Any]     	=       model(lowercase_    )
     self.parent.assertEqual(result.last_hidden_state.shape							,  (self.batch_size, self.seq_length, self.hidden_size)    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       TFXLMWithLMHeadModel(lowercase_    )

     UpperCAmelCase_   :   List[Any]     	=       {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
     UpperCAmelCase_   :   Dict     	=       model(lowercase_    )

     UpperCAmelCase_   :   Optional[int]     	=       outputs

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       TFXLMForQuestionAnsweringSimple(lowercase_    )

     UpperCAmelCase_   :   Any     	=       {"input_ids": input_ids, "lengths": input_lengths}

     UpperCAmelCase_   :   Union[str, Any]     	=       model(lowercase_    )

     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							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       TFXLMForSequenceClassification(lowercase_    )

     UpperCAmelCase_   :   Optional[int]     	=       {"input_ids": input_ids, "lengths": input_lengths}

     UpperCAmelCase_   :   Any     	=       model(lowercase_    )

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







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       self.num_labels
     UpperCAmelCase_   :   Any     	=       TFXLMForTokenClassification(config=lowercase_    )
     UpperCAmelCase_   :   Any     	=       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     UpperCAmelCase_   :   List[Any]     	=       model(lowercase_    )
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels)    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       self.num_choices
     UpperCAmelCase_   :   Any     	=       TFXLMForMultipleChoice(config=lowercase_    )
     UpperCAmelCase_   :   Union[str, Any]     	=       tf.tile(tf.expand_dims(lowercase_							,  1    )							,  (1, self.num_choices, 1)    )
     UpperCAmelCase_   :   Any     	=       tf.tile(tf.expand_dims(lowercase_							,  1    )							,  (1, self.num_choices, 1)    )
     UpperCAmelCase_   :   int     	=       tf.tile(tf.expand_dims(lowercase_							,  1    )							,  (1, self.num_choices, 1)    )
     UpperCAmelCase_   :   Any     	=       {
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
         "token_type_ids": multiple_choice_token_type_ids,
     }
     UpperCAmelCase_   :   int     	=       model(lowercase_    )
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_choices)    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  (
         UpperCAmelCase_
     )	,  
     )   :   Dict     	=       config_and_inputs
     UpperCAmelCase_   :   Optional[int]     	=       {
         "input_ids": input_ids,
         "token_type_ids": token_type_ids,
         "langs": token_type_ids,
         "lengths": input_lengths,
     }
     return config, inputs_dict




@require_tf
class A_      (lowercase__							,lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Any    	=						(
      (
          TFXLMModel,
          TFXLMWithLMHeadModel,
          TFXLMForSequenceClassification,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForTokenClassification,
          TFXLMForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						(
      (TFXLMWithLMHeadModel,) if is_tf_available() else ()
  )  # TODO (PVP): Check other models whether language generation is also applicable
  SCREAMING_SNAKE_CASE__     :		Any    	=						(
      {
          """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__     :		List[str]    	=						False
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						False







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     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    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       TFXLMModelTester(self    )
     UpperCAmelCase_   :   List[str]     	=       ConfigTester(self							,  config_class=lowercase_							,  emb_dim=37    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_model(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_lm_head(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_qa(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_sequence_classif(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_for_token_classification(*lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase_    )







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_   :   Tuple     	=       TFXLMModel.from_pretrained(lowercase_    )
        self.assertIsNotNone(lowercase_    )




@require_tf
class A_      (unittest.TestCase ):




  '''simple docstring'''







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"    )
     UpperCAmelCase_   :   Optional[Any]     	=       tf.convert_to_tensor([[14, 447]]							,  dtype=tf.intaa    )  # the president
     UpperCAmelCase_   :   Optional[int]     	=       [
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
         14,
         447,
     ]  # 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(lowercase_							,  do_sample=lowercase_    )
     self.assertListEqual(output_ids[0].numpy().tolist()							,  lowercase_    )


