






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		)














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		)






