code: 
  
  
  
  
  
  
  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 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     List[str]										= parent
            UpperCAmelCase__			:     List[Any]										= 13
            UpperCAmelCase__			:     Any										= 7
            UpperCAmelCase__			:     Tuple										= True
            UpperCAmelCase__			:     Any										= True
            UpperCAmelCase__			:     Union[str, Any]										= True
            UpperCAmelCase__			:     Tuple										= True
            UpperCAmelCase__			:     Optional[int]										= True
            UpperCAmelCase__			:     Tuple										= False
            UpperCAmelCase__			:     Union[str, Any]										= False
            UpperCAmelCase__			:     List[Any]										= False
            UpperCAmelCase__			:     Union[str, Any]										= 2
            UpperCAmelCase__			:     Any										= 99
            UpperCAmelCase__			:     str										= 0
            UpperCAmelCase__			:     Optional[Any]										= 32
            UpperCAmelCase__			:     Optional[Any]										= 2
            UpperCAmelCase__			:     int										= 4
            UpperCAmelCase__			:     Union[str, Any]										= 0.1
            UpperCAmelCase__			:     List[Any]										= 0.1
            UpperCAmelCase__			:     List[Any]										= 512
            UpperCAmelCase__			:     Tuple										= 16
            UpperCAmelCase__			:     Tuple										= 2
            UpperCAmelCase__			:     List[Any]										= 0.0_2
            UpperCAmelCase__			:     Optional[int]										= 3
            UpperCAmelCase__			:     Optional[int]										= 4
            UpperCAmelCase__			:     List[Any]										= """last"""
            UpperCAmelCase__			:     List[str]										= True
            UpperCAmelCase__			:     Any										= None
            UpperCAmelCase__			:     Any										= 0
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     int										= ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size		)
            UpperCAmelCase__			:     Tuple										= random_attention_mask([self.batch_size, self.seq_length]  ,	dtype=tf.floataa		)
  
            UpperCAmelCase__			:     Dict										= None
            if self.use_input_lengths:
                 UpperCAmelCase__			:     Tuple										= (
                     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__			:     str										= ids_tensor([self.batch_size, self.seq_length]  ,	self.n_langs		)
  
            UpperCAmelCase__			:     Tuple										= None
            UpperCAmelCase__			:     Dict										= None
            UpperCAmelCase__			:     List[Any]										= None
            if self.use_labels:
                 UpperCAmelCase__			:     Optional[int]										= ids_tensor([self.batch_size]  ,	self.type_sequence_label_size		)
                 UpperCAmelCase__			:     int										= ids_tensor([self.batch_size, self.seq_length]  ,	self.num_labels		)
                 UpperCAmelCase__			:     Dict										= ids_tensor([self.batch_size]  ,	2  ,	dtype=tf.floataa		)
                 UpperCAmelCase__			:     List[str]										= ids_tensor([self.batch_size]  ,	self.num_choices		)
  
            UpperCAmelCase__			:     Tuple										= 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  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     Union[str, Any]										= TFXLMModel(config=_lowerCAmelCase		)
            UpperCAmelCase__			:     Optional[Any]										= {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
            UpperCAmelCase__			:     List[Any]										= model(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     str										= [input_ids, input_mask]
            UpperCAmelCase__			:     str										= 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  ,	):
            UpperCAmelCase__			:     Optional[Any]										= TFXLMWithLMHeadModel(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     int										= {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
            UpperCAmelCase__			:     List[str]										= model(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     str										= outputs
  
            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  ,	):
            UpperCAmelCase__			:     Tuple										= TFXLMForQuestionAnsweringSimple(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Dict										= {"""input_ids""": input_ids, """lengths""": input_lengths}
  
            UpperCAmelCase__			:     Union[str, 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      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     List[Any]										= TFXLMForSequenceClassification(_lowerCAmelCase		)
  
            UpperCAmelCase__			:     Tuple										= {"""input_ids""": input_ids, """lengths""": input_lengths}
  
            UpperCAmelCase__			:     Tuple										= 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  ,	):
            UpperCAmelCase__			:     Union[str, Any]										= self.num_labels
            UpperCAmelCase__			:     Optional[Any]										= TFXLMForTokenClassification(config=_lowerCAmelCase		)
            UpperCAmelCase__			:     str										= {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            UpperCAmelCase__			:     Union[str, Any]										= 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  ,	):
            UpperCAmelCase__			:     List[Any]										= self.num_choices
            UpperCAmelCase__			:     Optional[int]										= TFXLMForMultipleChoice(config=_lowerCAmelCase		)
            UpperCAmelCase__			:     Tuple										= tf.tile(tf.expand_dims(_lowerCAmelCase  ,	1		)  ,	(1, self.num_choices, 1)		)
            UpperCAmelCase__			:     List[Any]										= tf.tile(tf.expand_dims(_lowerCAmelCase  ,	1		)  ,	(1, self.num_choices, 1)		)
            UpperCAmelCase__			:     Optional[Any]										= tf.tile(tf.expand_dims(_lowerCAmelCase  ,	1		)  ,	(1, self.num_choices, 1)		)
            UpperCAmelCase__			:     Union[str, Any]										= {
                """input_ids""": multiple_choice_inputs_ids,
                """attention_mask""": multiple_choice_input_mask,
                """token_type_ids""": multiple_choice_token_type_ids,
            }
            UpperCAmelCase__			:     Optional[Any]										= model(_lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_choices)		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     int										= self.prepare_config_and_inputs()
            (
                (
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		(
                UpperCAmelCase__
            )			,		
            )			:     Dict										= config_and_inputs
            UpperCAmelCase__			:     str										= {
                """input_ids""": input_ids,
                """token_type_ids""": token_type_ids,
                """langs""": token_type_ids,
                """lengths""": input_lengths,
            }
            return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class 							UpperCAmelCase_     (		__lowerCamelCase       ,			__lowerCamelCase       ,			unittest.TestCase		):
       __lowerCamelCase										=     (
           (
               TFXLMModel,
               TFXLMWithLMHeadModel,
               TFXLMForSequenceClassification,
               TFXLMForQuestionAnsweringSimple,
               TFXLMForTokenClassification,
               TFXLMForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       __lowerCamelCase										=     (
           (TFXLMWithLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Check other models whether language generation is also applicable
       __lowerCamelCase										=     (
           {
               '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 {}
       )
       __lowerCamelCase										=     False
       __lowerCamelCase										=     False
  
  
  
  
       def      __UpperCAmelCase     (							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      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[Any]										= TFXLMModelTester(self		)
            UpperCAmelCase__			:     Optional[Any]										= ConfigTester(self  ,	config_class=_lowerCAmelCase  ,	emb_dim=37		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.config_tester.run_common_tests()
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Tuple										= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_model(*_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     str										= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_lm_head(*_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[Any]										= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_qa(*_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Tuple										= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_sequence_classif(*_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     int										= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_token_classification(*_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Dict										= self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_multiple_choice(*_lowerCAmelCase		)
  
  
  
  
       @slow
       def      __UpperCAmelCase     (							self		):
            for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCAmelCase__			:     int										= TFXLMModel.from_pretrained(_lowerCAmelCase		)
                 self.assertIsNotNone(_lowerCAmelCase		)
  
  
  
  
  
  
  
  @require_tf
  class 							UpperCAmelCase_     (		unittest.TestCase		):
  
  
  
  
       @slow
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[int]										= TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""		)
            UpperCAmelCase__			:     Dict										= tf.convert_to_tensor([[14, 447]]  ,	dtype=tf.intaa		)  # the president
            UpperCAmelCase__			:     str										= [
                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__			:     List[str]										= model.generate(_lowerCAmelCase  ,	do_sample=_lowerCAmelCase		)
            self.assertListEqual(output_ids[0].numpy().tolist()  ,	_lowerCAmelCase		)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            warnings.warn(
                """The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use ViltImageProcessor instead."""  ,	_lowerCAmelCase  ,	)
            super().__init__(*_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
  
  
  