

import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
       import tensorflow as tf







class 						__magic_name__			(							enum.Enum							):

   '''simple docstring'''




   lowerCamelCase__ :	int    =   0
   lowerCamelCase__ :	Any    =   1
   lowerCamelCase__ :	Optional[Any]    =   2







@add_end_docstrings(lowerCamelCase__							)
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	Union[str, Any]    =   '\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '



   def __init__(				self,    *lowercase_,    **lowercase_       )							->    int:






     """simple docstring"""






     super().__init__(*lowercase_,    **lowercase_       )
     self.check_model_type(
         TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING       )
     if "prefix" not in self._preprocess_params:
       # This is very specific. The logic is quite complex and needs to be done
       # as a "default".
       # It also defines both some preprocess_kwargs and generate_kwargs
       # which is why we cannot put them in their respective methods.
       a__  =None
       if self.model.config.prefix is not None:
         a__  =self.model.config.prefix
       if prefix is None and self.model.__class__.__name__ in [
           "XLNetLMHeadModel",
           "TransfoXLLMHeadModel",
           "TFXLNetLMHeadModel",
           "TFTransfoXLLMHeadModel",
       ]:
         # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
         a__  =self.XL_PREFIX
       if prefix is not None:
         # Recalculate some generate_kwargs linked to prefix.
         a__,      a__,      a__  =self._sanitize_parameters(prefix=lowercase_,    **self._forward_params       )
         a__  ={**self._preprocess_params, **preprocess_params}
         a__  ={**self._forward_params, **forward_params}



   def  _UpperCAmelCase (				self,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_=None,    lowercase_=None,    **lowercase_,    )							->    List[Any]:






     """simple docstring"""






     a__  ={}
     if prefix is not None:
       a__  =prefix
     if prefix:
       a__  =self.tokenizer(
           lowercase_,    padding=lowercase_,    add_special_tokens=lowercase_,    return_tensors=self.framework       )
       a__  =prefix_inputs['''input_ids'''].shape[-1]

     if handle_long_generation is not None:
       if handle_long_generation not in {"hole"}:
         raise ValueError(
             F"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
             ''' [None, \'hole\']'''       )
       a__  =handle_long_generation

     preprocess_params.update(lowercase_       )
     a__  =generate_kwargs

     a__  ={}
     if return_full_text is not None and return_type is None:
       if return_text is not None:
         raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''       )
       if return_tensors is not None:
         raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''       )
       a__  =ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
     if return_tensors is not None and return_type is None:
       if return_text is not None:
         raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''       )
       a__  =ReturnType.TENSORS
     if return_type is not None:
       a__  =return_type
     if clean_up_tokenization_spaces is not None:
       a__  =clean_up_tokenization_spaces

     if stop_sequence is not None:
       a__  =self.tokenizer.encode(lowercase_,    add_special_tokens=lowercase_       )
       if len(lowercase_       ) > 1:
         warnings.warn(
             '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
             ''' the stop sequence will be used as the stop sequence string in the interim.'''       )
       a__  =stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params



   def  _UpperCAmelCase (				self,    *lowercase_,    **lowercase_       )							->    Dict:






     """simple docstring"""






     if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
       kwargs.update({'''add_space_before_punct_symbol''': True}       )

     return super()._parse_and_tokenize(*lowercase_,    **lowercase_       )



   def __call__(				self,    lowercase_,    **lowercase_       )							->    Optional[Any]:






     """simple docstring"""






     return super().__call__(lowercase_,    **lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_="",    lowercase_=None,    **lowercase_       )							->    int:






     """simple docstring"""






     a__  =self.tokenizer(
         prefix + prompt_text,    padding=lowercase_,    add_special_tokens=lowercase_,    return_tensors=self.framework       )
     a__  =prompt_text

     if handle_long_generation == "hole":
       a__  =inputs['''input_ids'''].shape[-1]
       if "max_new_tokens" in generate_kwargs:
         a__  =generate_kwargs['''max_new_tokens''']
       else:
         a__  =generate_kwargs.get('''max_length''',    self.model.config.max_length       ) - cur_len
         if new_tokens < 0:
           raise ValueError('''We cannot infer how many new tokens are expected'''       )
       if cur_len + new_tokens > self.tokenizer.model_max_length:
         a__  =self.tokenizer.model_max_length - new_tokens
         if keep_length <= 0:
           raise ValueError(
               '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
               ''' models max length'''       )

         a__  =inputs['''input_ids'''][:, -keep_length:]
         if "attention_mask" in inputs:
           a__  =inputs['''attention_mask'''][:, -keep_length:]

     return inputs



   def  _UpperCAmelCase (				self,    lowercase_,    **lowercase_       )							->    Optional[int]:






     """simple docstring"""






     a__  =model_inputs['''input_ids''']
     a__  =model_inputs.get('''attention_mask''',    lowercase_       )
     # Allow empty prompts
     if input_ids.shape[1] == 0:
       a__  =None
       a__  =None
       a__  =1
     else:
       a__  =input_ids.shape[0]
     a__  =model_inputs.pop('''prompt_text'''       )

     # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
     # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
     a__  =generate_kwargs.pop('''prefix_length''',    0       )
     if prefix_length > 0:
       a__  ='''max_new_tokens''' in generate_kwargs or (
           '''generation_config''' in generate_kwargs
           and generate_kwargs['''generation_config'''].max_new_tokens is not None
       )
       if not has_max_new_tokens:
         a__  =generate_kwargs.get('''max_length'''       ) or self.model.config.max_length
         generate_kwargs["max_length"] += prefix_length
       a__  ='''min_new_tokens''' in generate_kwargs or (
           '''generation_config''' in generate_kwargs
           and generate_kwargs['''generation_config'''].min_new_tokens is not None
       )
       if not has_min_new_tokens and "min_length" in generate_kwargs:
         generate_kwargs["min_length"] += prefix_length

        # BS x SL
     a__  =self.model.generate(input_ids=lowercase_,    attention_mask=lowercase_,    **lowercase_       )
     a__  =generated_sequence.shape[0]
     if self.framework == "pt":
       a__  =generated_sequence.reshape(lowercase_,    out_b // in_b,    *generated_sequence.shape[1:]       )
     elif self.framework == "tf":
       a__  =tf.reshape(lowercase_,    (in_b, out_b // in_b, *generated_sequence.shape[1:])       )
     return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=ReturnType.FULL_TEXT,    lowercase_=True       )							->    Optional[int]:






     """simple docstring"""






     a__  =model_outputs['''generated_sequence'''][0]
     a__  =model_outputs['''input_ids''']
     a__  =model_outputs['''prompt_text''']
     a__  =generated_sequence.numpy().tolist()
     a__  =[]
     for sequence in generated_sequence:
       if return_type == ReturnType.TENSORS:
         a__  ={'''generated_token_ids''': sequence}
       elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
         # Decode text
         a__  =self.tokenizer.decode(
             lowercase_,    skip_special_tokens=lowercase_,    clean_up_tokenization_spaces=lowercase_,    )

         # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
         if input_ids is None:
           a__  =0
         else:
           a__  =len(
               self.tokenizer.decode(
                   input_ids[0],    skip_special_tokens=lowercase_,    clean_up_tokenization_spaces=lowercase_,    )       )

         if return_type == ReturnType.FULL_TEXT:
           a__  =prompt_text + text[prompt_length:]
         else:
           a__  =text[prompt_length:]

         a__  ={'''generated_text''': all_text}
       records.append(lowercase_       )

     return records



import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
           FlaubertForMultipleChoice,
           FlaubertForQuestionAnswering,
           FlaubertForQuestionAnsweringSimple,
           FlaubertForSequenceClassification,
           FlaubertForTokenClassification,
           FlaubertModel,
           FlaubertWithLMHeadModel,
       )
       from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_=13,    lowercase_=7,    lowercase_=True,    lowercase_=True,    lowercase_=True,    lowercase_=True,    lowercase_=True,    lowercase_=False,    lowercase_=False,    lowercase_=False,    lowercase_=2,    lowercase_=99,    lowercase_=0,    lowercase_=32,    lowercase_=5,    lowercase_=4,    lowercase_=0.1,    lowercase_=0.1,    lowercase_=512,    lowercase_=12,    lowercase_=2,    lowercase_=0.02,    lowercase_=3,    lowercase_=4,    lowercase_="last",    lowercase_=None,    lowercase_=None,    )							->    List[Any]:






     """simple docstring"""






     a__  =parent
     a__  =batch_size
     a__  =seq_length
     a__  =is_training
     a__  =use_input_lengths
     a__  =use_token_type_ids
     a__  =use_labels
     a__  =gelu_activation
     a__  =sinusoidal_embeddings
     a__  =causal
     a__  =asm
     a__  =n_langs
     a__  =vocab_size
     a__  =n_special
     a__  =hidden_size
     a__  =num_hidden_layers
     a__  =num_attention_heads
     a__  =hidden_dropout_prob
     a__  =attention_probs_dropout_prob
     a__  =max_position_embeddings
     a__  =type_vocab_size
     a__  =type_sequence_label_size
     a__  =initializer_range
     a__  =num_labels
     a__  =num_choices
     a__  =summary_type
     a__  =use_proj
     a__  =scope



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =ids_tensor([self.batch_size, self.seq_length],    self.vocab_size       )
     a__  =random_attention_mask([self.batch_size, self.seq_length]       )

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

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

     a__  =None
     a__  =None
     a__  =None
     if self.use_labels:
       a__  =ids_tensor([self.batch_size],    self.type_sequence_label_size       )
       a__  =ids_tensor([self.batch_size, self.seq_length],    self.num_labels       )
       a__  =ids_tensor([self.batch_size],    2       ).float()
       a__  =ids_tensor([self.batch_size],    self.num_choices       )

     a__  =self.get_config()

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



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     return 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,    )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )							->    Dict:






     """simple docstring"""






     a__  =FlaubertModel(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    lengths=lowercase_,    langs=lowercase_       )
     a__  =model(lowercase_,    langs=lowercase_       )
     a__  =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_,    )							->    str:






     """simple docstring"""






     a__  =FlaubertWithLMHeadModel(lowercase_       )
     model.to(lowercase_       )
     model.eval()

     a__  =model(lowercase_,    token_type_ids=lowercase_,    labels=lowercase_       )
     self.parent.assertEqual(result.loss.shape,    ()       )
     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_,    )							->    Dict:






     """simple docstring"""






     a__  =FlaubertForQuestionAnsweringSimple(lowercase_       )
     model.to(lowercase_       )
     model.eval()

     a__  =model(lowercase_       )

     a__  =model(lowercase_,    start_positions=lowercase_,    end_positions=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_,    )							->    Optional[Any]:






     """simple docstring"""






     a__  =FlaubertForQuestionAnswering(lowercase_       )
     model.to(lowercase_       )
     model.eval()

     a__  =model(lowercase_       )

     a__  =model(
         lowercase_,    start_positions=lowercase_,    end_positions=lowercase_,    cls_index=lowercase_,    is_impossible=lowercase_,    p_mask=lowercase_,    )

     a__  =model(
         lowercase_,    start_positions=lowercase_,    end_positions=lowercase_,    cls_index=lowercase_,    is_impossible=lowercase_,    )

     ((a__),      )  =result_with_labels.to_tuple()

     a__  =model(lowercase_,    start_positions=lowercase_,    end_positions=lowercase_       )

     ((a__),      )  =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  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )							->    Optional[Any]:






     """simple docstring"""






     a__  =FlaubertForSequenceClassification(lowercase_       )
     model.to(lowercase_       )
     model.eval()

     a__  =model(lowercase_       )
     a__  =model(lowercase_,    labels=lowercase_       )

     self.parent.assertEqual(result.loss.shape,    ()       )
     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_,    )							->    Optional[int]:






     """simple docstring"""






     a__  =self.num_labels
     a__  =FlaubertForTokenClassification(lowercase_       )
     model.to(lowercase_       )
     model.eval()

     a__  =model(lowercase_,    attention_mask=lowercase_,    labels=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_,    )							->    Dict:






     """simple docstring"""






     a__  =self.num_choices
     a__  =FlaubertForMultipleChoice(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =input_ids.unsqueeze(1       ).expand(-1,    self.num_choices,    -1       ).contiguous()
     a__  =token_type_ids.unsqueeze(1       ).expand(-1,    self.num_choices,    -1       ).contiguous()
     a__  =input_mask.unsqueeze(1       ).expand(-1,    self.num_choices,    -1       ).contiguous()
     a__  =model(
         lowercase_,    attention_mask=lowercase_,    token_type_ids=lowercase_,    labels=lowercase_,    )
     self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_choices)       )



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__  =self.prepare_config_and_inputs()
     (
         (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      (
         a__
     ),      
     )  =config_and_inputs
     a__  ={
         '''input_ids''': input_ids,
         '''token_type_ids''': token_type_ids,
         '''lengths''': input_lengths,
         '''attention_mask''': input_mask,
     }
     return config, inputs_dict







@require_torch
class 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	str    =   (
       (
           FlaubertModel,
           FlaubertWithLMHeadModel,
           FlaubertForQuestionAnswering,
           FlaubertForQuestionAnsweringSimple,
           FlaubertForSequenceClassification,
           FlaubertForTokenClassification,
           FlaubertForMultipleChoice,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase__ :	Dict    =   (
       {
           'feature-extraction': FlaubertModel,
           'fill-mask': FlaubertWithLMHeadModel,
           'question-answering': FlaubertForQuestionAnsweringSimple,
           'text-classification': FlaubertForSequenceClassification,
           'token-classification': FlaubertForTokenClassification,
           'zero-shot': FlaubertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    str:






     """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,    lowercase_,    lowercase_,    lowercase_=False       )							->    str:






     """simple docstring"""






     a__  =super()._prepare_for_class(lowercase_,    lowercase_,    return_labels=lowercase_       )

     if return_labels:
       if model_class.__name__ == "FlaubertForQuestionAnswering":
         a__  =torch.zeros(
             self.model_tester.batch_size,    dtype=torch.long,    device=lowercase_       )
         a__  =torch.zeros(
             self.model_tester.batch_size,    dtype=torch.long,    device=lowercase_       )

     return inputs_dict



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =FlaubertModelTester(self       )
     a__  =ConfigTester(self,    config_class=lowercase_,    emb_dim=37       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     self.config_tester.run_common_tests()



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_model(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_lm_head(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_simple_qa(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_qa(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_token_classif(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_multiple_choice(*lowercase_       )



   @slow
   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






     for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       a__  =FlaubertModel.from_pretrained(lowercase_       )
       self.assertIsNotNone(lowercase_       )



   @slow
   @require_torch_gpu
   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__,      a__  =self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
       # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
       if model_class == FlaubertForMultipleChoice:
         return

       a__  =True
       a__  =model_class(config=lowercase_       )

       a__  =self._prepare_for_class(lowercase_,    lowercase_       )
       a__  =torch.jit.trace(
           lowercase_,    (inputs_dict['''input_ids'''].to('''cpu'''       ), inputs_dict['''attention_mask'''].to('''cpu'''       ))       )

       with tempfile.TemporaryDirectory() as tmp:
         torch.jit.save(lowercase_,    os.path.join(lowercase_,    '''traced_model.pt'''       )       )
         a__  =torch.jit.load(os.path.join(lowercase_,    '''traced_model.pt'''       ),    map_location=lowercase_       )
         loaded(inputs_dict['''input_ids'''].to(lowercase_       ),    inputs_dict['''attention_mask'''].to(lowercase_       )       )







@require_torch
class 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   @slow
   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     a__  =FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''       )
     a__  =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
     with torch.no_grad():
       a__  =model(lowercase_       )[0]
     a__  =torch.Size((1, 11, 768)       )
     self.assertEqual(output.shape,    lowercase_       )
     a__  =torch.tensor(
         [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]       )

     self.assertTrue(torch.allclose(output[:, :3, :3],    lowercase_,    atol=1E-4       )       )
