


import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.bert.modeling_flax_bert import (
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForNextSentencePrediction,
           FlaxBertForPreTraining,
           FlaxBertForQuestionAnswering,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertModel,
       )





class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    def __init__(	self				:   List[str]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Union[str, Any]=13       ,   __snake_case				:   Any=7       ,   __snake_case				:   Optional[Any]=True       ,   __snake_case				:   Optional[Any]=True       ,   __snake_case				:   Any=True       ,   __snake_case				:   Optional[Any]=True       ,   __snake_case				:   List[str]=99       ,   __snake_case				:   Optional[int]=32       ,   __snake_case				:   List[Any]=5       ,   __snake_case				:   List[str]=4       ,   __snake_case				:   Any=37       ,   __snake_case				:   str="gelu"       ,   __snake_case				:   Optional[Any]=0.1       ,   __snake_case				:   List[str]=0.1       ,   __snake_case				:   Any=512       ,   __snake_case				:   List[Any]=16       ,   __snake_case				:   Any=2       ,   __snake_case				:   Union[str, Any]=0.02       ,   __snake_case				:   str=4       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   parent
       UpperCAmelCase_							:      Any						=   batch_size
       UpperCAmelCase_							:      Optional[int]						=   seq_length
       UpperCAmelCase_							:      Union[str, Any]						=   is_training
       UpperCAmelCase_							:      List[Any]						=   use_attention_mask
       UpperCAmelCase_							:      int						=   use_token_type_ids
       UpperCAmelCase_							:      int						=   use_labels
       UpperCAmelCase_							:      Tuple						=   vocab_size
       UpperCAmelCase_							:      Optional[Any]						=   hidden_size
       UpperCAmelCase_							:      Union[str, Any]						=   num_hidden_layers
       UpperCAmelCase_							:      Optional[Any]						=   num_attention_heads
       UpperCAmelCase_							:      List[Any]						=   intermediate_size
       UpperCAmelCase_							:      Optional[int]						=   hidden_act
       UpperCAmelCase_							:      List[str]						=   hidden_dropout_prob
       UpperCAmelCase_							:      List[Any]						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      Optional[Any]						=   max_position_embeddings
       UpperCAmelCase_							:      Optional[int]						=   type_vocab_size
       UpperCAmelCase_							:      Tuple						=   type_sequence_label_size
       UpperCAmelCase_							:      List[Any]						=   initializer_range
       UpperCAmelCase_							:      Optional[int]						=   num_choices





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size	)

       UpperCAmelCase_							:      Any						=   None
       if self.use_attention_mask:
          UpperCAmelCase_							:      Any						=   random_attention_mask([self.batch_size, self.seq_length]	)

       UpperCAmelCase_							:      Any						=   None
       if self.use_token_type_ids:
          UpperCAmelCase_							:      Optional[Any]						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.type_vocab_size	)

       UpperCAmelCase_							:      str						=   BertConfig(
           vocab_size=self.vocab_size       ,   hidden_size=self.hidden_size       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   max_position_embeddings=self.max_position_embeddings       ,   type_vocab_size=self.type_vocab_size       ,   is_decoder=__snake_case       ,   initializer_range=self.initializer_range       ,   )

       return config, input_ids, token_type_ids, attention_mask





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   self.prepare_config_and_inputs()
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   config_and_inputs
       UpperCAmelCase_							:      Tuple						=   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self.prepare_config_and_inputs()
       UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      List[Any]						=   config_and_inputs

       UpperCAmelCase_							:      Dict						=   True
       UpperCAmelCase_							:      Any						=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
       UpperCAmelCase_							:      List[Any]						=   ids_tensor([self.batch_size, self.seq_length]       ,   vocab_size=2	)

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )





@require_flax
class  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Any			= True

    A_  :     Dict			= (
        (
            FlaxBertModel,
            FlaxBertForPreTraining,
            FlaxBertForMaskedLM,
            FlaxBertForMultipleChoice,
            FlaxBertForQuestionAnswering,
            FlaxBertForNextSentencePrediction,
            FlaxBertForSequenceClassification,
            FlaxBertForTokenClassification,
            FlaxBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   FlaxBertModelTester(self	)





    @slow
    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       # Only check this for base model, not necessary for all model classes.
       # This will also help speed-up tests.
       UpperCAmelCase_							:      Union[str, Any]						=   FlaxBertModel.from_pretrained('''bert-base-cased'''	)
       UpperCAmelCase_							:      Optional[int]						=   model(np.ones((1, 1)	)	)
       self.assertIsNotNone(__snake_case	)



import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           XLMForMultipleChoice,
           XLMForQuestionAnswering,
           XLMForQuestionAnsweringSimple,
           XLMForSequenceClassification,
           XLMForTokenClassification,
           XLMModel,
           XLMWithLMHeadModel,
       )
       from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST





class  lowerCAmelCase__:






    '''simple docstring'''





    def __init__(	self				:   int       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[Any]=13       ,   __snake_case				:   str=7       ,   __snake_case				:   Union[str, Any]=True       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   str=True       ,   __snake_case				:   Optional[int]=True       ,   __snake_case				:   Optional[int]=True       ,   __snake_case				:   List[str]=False       ,   __snake_case				:   List[str]=False       ,   __snake_case				:   Tuple=False       ,   __snake_case				:   List[str]=2       ,   __snake_case				:   Optional[int]=99       ,   __snake_case				:   Tuple=0       ,   __snake_case				:   int=32       ,   __snake_case				:   Optional[int]=5       ,   __snake_case				:   str=4       ,   __snake_case				:   str=0.1       ,   __snake_case				:   Optional[int]=0.1       ,   __snake_case				:   List[str]=512       ,   __snake_case				:   Tuple=2       ,   __snake_case				:   List[Any]=0.02       ,   __snake_case				:   Any=2       ,   __snake_case				:   Optional[int]=4       ,   __snake_case				:   Optional[Any]="last"       ,   __snake_case				:   Dict=True       ,   __snake_case				:   Any=None       ,   __snake_case				:   str=0       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   parent
       UpperCAmelCase_							:      Optional[Any]						=   batch_size
       UpperCAmelCase_							:      Union[str, Any]						=   seq_length
       UpperCAmelCase_							:      List[Any]						=   is_training
       UpperCAmelCase_							:      List[Any]						=   use_input_lengths
       UpperCAmelCase_							:      Dict						=   use_token_type_ids
       UpperCAmelCase_							:      Union[str, Any]						=   use_labels
       UpperCAmelCase_							:      int						=   gelu_activation
       UpperCAmelCase_							:      str						=   sinusoidal_embeddings
       UpperCAmelCase_							:      List[str]						=   causal
       UpperCAmelCase_							:      Tuple						=   asm
       UpperCAmelCase_							:      List[Any]						=   n_langs
       UpperCAmelCase_							:      Union[str, Any]						=   vocab_size
       UpperCAmelCase_							:      Any						=   n_special
       UpperCAmelCase_							:      Dict						=   hidden_size
       UpperCAmelCase_							:      Dict						=   num_hidden_layers
       UpperCAmelCase_							:      str						=   num_attention_heads
       UpperCAmelCase_							:      List[str]						=   hidden_dropout_prob
       UpperCAmelCase_							:      Any						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      Tuple						=   max_position_embeddings
       UpperCAmelCase_							:      Union[str, Any]						=   type_sequence_label_size
       UpperCAmelCase_							:      Union[str, Any]						=   initializer_range
       UpperCAmelCase_							:      Tuple						=   num_labels
       UpperCAmelCase_							:      List[Any]						=   num_choices
       UpperCAmelCase_							:      Any						=   summary_type
       UpperCAmelCase_							:      Optional[int]						=   use_proj
       UpperCAmelCase_							:      List[str]						=   scope
       UpperCAmelCase_							:      List[str]						=   bos_token_id





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size	)
       UpperCAmelCase_							:      Union[str, Any]						=   random_attention_mask([self.batch_size, self.seq_length]	)

       UpperCAmelCase_							:      Tuple						=   None
       if self.use_input_lengths:
          UpperCAmelCase_							:      List[str]						=   (
              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_							:      List[str]						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.n_langs	)

       UpperCAmelCase_							:      Optional[int]						=   None
       UpperCAmelCase_							:      Union[str, Any]						=   None
       UpperCAmelCase_							:      str						=   None
       if self.use_labels:
          UpperCAmelCase_							:      Any						=   ids_tensor([self.batch_size]       ,   self.type_sequence_label_size	)
          UpperCAmelCase_							:      List[str]						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.num_labels	)
          UpperCAmelCase_							:      List[Any]						=   ids_tensor([self.batch_size]       ,   2	).float()
          UpperCAmelCase_							:      Optional[Any]						=   ids_tensor([self.batch_size]       ,   self.num_choices	)

       UpperCAmelCase_							:      List[Any]						=   self.get_config()

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





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       return 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       ,   num_labels=self.num_labels       ,   bos_token_id=self.bos_token_id       ,   )





    def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   int       ,   __snake_case				:   int       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   XLMModel(config=__snake_case	)
       model.to(__snake_case	)
       model.eval()
       UpperCAmelCase_							:      Optional[int]						=   model(__snake_case       ,   lengths=__snake_case       ,   langs=__snake_case	)
       UpperCAmelCase_							:      Any						=   model(__snake_case       ,   langs=__snake_case	)
       UpperCAmelCase_							:      Any						=   model(__snake_case	)
       self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Dict       ,   __snake_case				:   Any       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   str       ,   __snake_case				:   Tuple       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   int       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   XLMWithLMHeadModel(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Optional[Any]						=   model(__snake_case       ,   token_type_ids=__snake_case       ,   labels=__snake_case	)
       self.parent.assertEqual(result.loss.shape       ,   ()	)
       self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size)	)





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   __snake_case				:   Tuple       ,   __snake_case				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[str]       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[Any]						=   XLMForQuestionAnsweringSimple(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Optional[int]						=   model(__snake_case	)

       UpperCAmelCase_							:      Any						=   model(__snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case	)
       UpperCAmelCase_							:      Optional[Any]						=   outputs
       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				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Tuple       ,   __snake_case				:   str       ,   __snake_case				:   Tuple       ,   __snake_case				:   int       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   XLMForQuestionAnswering(__snake_case	)
       model.to(__snake_case	)
       model.eval()

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

       UpperCAmelCase_							:      List[str]						=   model(
           __snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case       ,   cls_index=__snake_case       ,   is_impossible=__snake_case       ,   p_mask=__snake_case       ,   )

       UpperCAmelCase_							:      Optional[Any]						=   model(
           __snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case       ,   cls_index=__snake_case       ,   is_impossible=__snake_case       ,   )

       ((UpperCAmelCase_)		,      )							:      Union[str, Any]						=   result_with_labels.to_tuple()

       UpperCAmelCase_							:      Optional[int]						=   model(__snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case	)

       ((UpperCAmelCase_)		,      )							:      str						=   result_with_labels.to_tuple()

       self.parent.assertEqual(result_with_labels.loss.shape       ,   ()	)
       self.parent.assertEqual(result.start_top_log_probs.shape       ,   (self.batch_size, model.config.start_n_top)	)
       self.parent.assertEqual(result.start_top_index.shape       ,   (self.batch_size, model.config.start_n_top)	)
       self.parent.assertEqual(
           result.end_top_log_probs.shape       ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
       self.parent.assertEqual(
           result.end_top_index.shape       ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
       self.parent.assertEqual(result.cls_logits.shape       ,   (self.batch_size,)	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Dict       ,   __snake_case				:   str       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Dict       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Any       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   XLMForSequenceClassification(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Union[str, Any]						=   model(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   model(__snake_case       ,   labels=__snake_case	)
       self.parent.assertEqual(result.loss.shape       ,   ()	)
       self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size)	)





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Any       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Tuple       ,   __snake_case				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Tuple       ,   __snake_case				:   int       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   self.num_labels
       UpperCAmelCase_							:      Optional[int]						=   XLMForTokenClassification(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      List[str]						=   model(__snake_case       ,   attention_mask=__snake_case       ,   labels=__snake_case	)
       self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.num_labels)	)





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[str]       ,   __snake_case				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   Any       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   Dict       ,   __snake_case				:   Optional[int]       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   self.num_choices
       UpperCAmelCase_							:      int						=   XLMForMultipleChoice(config=__snake_case	)
       model.to(__snake_case	)
       model.eval()
       UpperCAmelCase_							:      int						=   input_ids.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
       UpperCAmelCase_							:      int						=   token_type_ids.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
       UpperCAmelCase_							:      List[Any]						=   input_mask.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
       UpperCAmelCase_							:      Any						=   model(
           __snake_case       ,   attention_mask=__snake_case       ,   token_type_ids=__snake_case       ,   labels=__snake_case       ,   )
       self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_choices)	)





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      
       )							:      Union[str, Any]						=   config_and_inputs
       UpperCAmelCase_							:      str						=   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
       return config, inputs_dict





@require_torch
class  lowerCAmelCase__(    snake_case__    ,    snake_case__    ,    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Dict			= (
        (
            XLMModel,
            XLMWithLMHeadModel,
            XLMForQuestionAnswering,
            XLMForSequenceClassification,
            XLMForQuestionAnsweringSimple,
            XLMForTokenClassification,
            XLMForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    A_  :     List[str]			= (
        (XLMWithLMHeadModel,) if is_torch_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    A_  :     Optional[int]			= (
        {
            'feature-extraction': XLMModel,
            'fill-mask': XLMWithLMHeadModel,
            'question-answering': XLMForQuestionAnsweringSimple,
            'text-classification': XLMForSequenceClassification,
            'text-generation': XLMWithLMHeadModel,
            'token-classification': XLMForTokenClassification,
            'zero-shot': XLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Optional[Any]	):





       '''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 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   Any       ,   __snake_case				:   List[Any]       ,   __snake_case				:   str=False	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   super()._prepare_for_class(__snake_case       ,   __snake_case       ,   return_labels=__snake_case	)

       if return_labels:
          if model_class.__name__ == "XLMForQuestionAnswering":
             UpperCAmelCase_							:      List[str]						=   torch.zeros(
                 self.model_tester.batch_size       ,   dtype=torch.long       ,   device=__snake_case	)
             UpperCAmelCase_							:      Dict						=   torch.zeros(
                 self.model_tester.batch_size       ,   dtype=torch.long       ,   device=__snake_case	)

       return inputs_dict





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   XLMModelTester(self	)
       UpperCAmelCase_							:      List[Any]						=   ConfigTester(self       ,   config_class=__snake_case       ,   emb_dim=37	)





    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





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





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*__snake_case	)





    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*__snake_case	)





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_simple_qa(*__snake_case	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*__snake_case	)





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_token_classif(*__snake_case	)





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   str       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]=False       ,   __snake_case				:   Optional[Any]=1	):





       '''simple docstring'''
       self.assertIsInstance(__snake_case       ,   __snake_case	)
       self.assertListEqual(
           [isinstance(__snake_case       ,   __snake_case	) for iter_attentions in attentions]       ,   [True] * len(__snake_case	)	)
       self.assertEqual(len(__snake_case	)       ,   (max_length - min_length) * num_beam_groups	)

       for idx, iter_attentions in enumerate(__snake_case	):
          # adds PAD dummy token
          UpperCAmelCase_							:      Dict						=   min_length + idx + 1
          UpperCAmelCase_							:      List[Any]						=   min_length + idx + 1

          UpperCAmelCase_							:      Optional[int]						=   (
              batch_size * num_beam_groups,
              config.num_attention_heads,
              tgt_len,
              src_len,
          )
          # check attn size
          self.assertListEqual(
              [layer_attention.shape for layer_attention in iter_attentions]       ,   [expected_shape] * len(__snake_case	)	)





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   int       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Dict       ,   __snake_case				:   Any       ,   __snake_case				:   Optional[Any]=False       ,   __snake_case				:   str=1	):





       '''simple docstring'''
       self.assertIsInstance(__snake_case       ,   __snake_case	)
       self.assertListEqual(
           [isinstance(__snake_case       ,   __snake_case	) for iter_hidden_states in hidden_states]       ,   [True] * len(__snake_case	)       ,   )
       self.assertEqual(len(__snake_case	)       ,   (max_length - min_length) * num_beam_groups	)

       for idx, iter_hidden_states in enumerate(__snake_case	):
          # adds PAD dummy token
          UpperCAmelCase_							:      str						=   min_length + idx + 1
          UpperCAmelCase_							:      int						=   (batch_size * num_beam_groups, seq_len, config.hidden_size)
          # check hidden size
          self.assertListEqual(
              [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]       ,   [expected_shape] * len(__snake_case	)       ,   )
       pass





    @slow
    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCAmelCase_							:      Any						=   XLMModel.from_pretrained(__snake_case	)
          self.assertIsNotNone(__snake_case	)





@require_torch
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    @slow
    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''	)
       model.to(__snake_case	)
       UpperCAmelCase_							:      str						=   torch.tensor([[14, 447]]       ,   dtype=torch.long       ,   device=__snake_case	)  # the president
       UpperCAmelCase_							:      Union[str, Any]						=   [
           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_							:      Dict						=   model.generate(__snake_case       ,   do_sample=__snake_case	)
       self.assertListEqual(output_ids[0].cpu().numpy().tolist()       ,   __snake_case	)