



"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowerCAmelCase__      =    '''
Human: <<task>>

Assistant: '''


lowerCAmelCase__      =    '''huggingface-tools/default-prompts'''
lowerCAmelCase__      =    {'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def 	snake_case_  (				A_ :		Optional[Any],    A_ :		List[str],    A_ :		int="run"						):


       '''simple docstring'''





       if prompt_or_repo_id is None:
              _lowerCamelCase						:    Dict         =			DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search('''\\s''',    A_						) is not None:
              return prompt_or_repo_id

       _lowerCamelCase						:    Union[str, Any]         =			cached_file(
           A_,    PROMPT_FILES[mode],    repo_type='''dataset''',    user_agent={'''agent''': agent_name}						)
       with open(A_,    '''r''',    encoding='''utf-8'''						) as f:
              return f.read()







"""simple docstring"""






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 	__snake_case	(     _lowercase):




  def __init__(			self					:						List[Any]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						List[Any]=1_3						, __lowerCAmelCase					:						Any=7						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Union[str, Any]=True						, __lowerCAmelCase					:						List[Any]=True						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Union[str, Any]=False						, __lowerCAmelCase					:						List[Any]=False						, __lowerCAmelCase					:						int=False						, __lowerCAmelCase					:						Tuple=2						, __lowerCAmelCase					:						Dict=9_9						, __lowerCAmelCase					:						str=0						, __lowerCAmelCase					:						Optional[Any]=3_2						, __lowerCAmelCase					:						Tuple=5						, __lowerCAmelCase					:						Tuple=4						, __lowerCAmelCase					:						List[str]=0.1						, __lowerCAmelCase					:						Union[str, Any]=0.1						, __lowerCAmelCase					:						Optional[int]=5_1_2						, __lowerCAmelCase					:						Any=1_2						, __lowerCAmelCase					:						Optional[int]=2						, __lowerCAmelCase					:						Any=0.02						, __lowerCAmelCase					:						str=3						, __lowerCAmelCase					:						Optional[int]=4						, __lowerCAmelCase					:						Optional[int]="last"						, __lowerCAmelCase					:						str=None						, __lowerCAmelCase					:						int=None						, ):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size		)
         _lowerCamelCase						:    Optional[Any]         =			random_attention_mask([self.batch_size, self.seq_length]		)

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

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

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

         _lowerCamelCase						:    Tuple         =			self.get_config()

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """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 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						Tuple						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Tuple						, ):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			FlaubertModel(config=__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()
         _lowerCamelCase						:    Union[str, Any]         =			model(__lowerCAmelCase						, lengths=__lowerCAmelCase						, langs=__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			model(__lowerCAmelCase						, langs=__lowerCAmelCase		)
         _lowerCamelCase						:    List[str]         =			model(__lowerCAmelCase		)
         self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						Tuple						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						str						, ):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			FlaubertWithLMHeadModel(__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()

         _lowerCamelCase						:    str         =			model(__lowerCAmelCase						, token_type_ids=__lowerCAmelCase						, labels=__lowerCAmelCase		)
         self.parent.assertEqual(result.loss.shape						, ()		)
         self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Tuple						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Union[str, Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			FlaubertForQuestionAnsweringSimple(__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()

         _lowerCamelCase						:    List[str]         =			model(__lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			model(__lowerCAmelCase						, start_positions=__lowerCAmelCase						, end_positions=__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 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						int						, ):






         """simple docstring"""






         _lowerCamelCase						:    str         =			FlaubertForQuestionAnswering(__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()

         _lowerCamelCase						:    Optional[Any]         =			model(__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			model(
             __lowerCAmelCase						, start_positions=__lowerCAmelCase						, end_positions=__lowerCAmelCase						, cls_index=__lowerCAmelCase						, is_impossible=__lowerCAmelCase						, p_mask=__lowerCAmelCase						, )

         _lowerCamelCase						:    List[str]         =			model(
             __lowerCAmelCase						, start_positions=__lowerCAmelCase						, end_positions=__lowerCAmelCase						, cls_index=__lowerCAmelCase						, is_impossible=__lowerCAmelCase						, )

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

         _lowerCamelCase						:    Optional[Any]         =			model(__lowerCAmelCase						, start_positions=__lowerCAmelCase						, end_positions=__lowerCAmelCase		)

         ((_lowerCamelCase)      ,   )						:    Union[str, Any]         =			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 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						str						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						str						, ):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			FlaubertForSequenceClassification(__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()

         _lowerCamelCase						:    str         =			model(__lowerCAmelCase		)
         _lowerCamelCase						:    Tuple         =			model(__lowerCAmelCase						, labels=__lowerCAmelCase		)

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




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






         """simple docstring"""






         _lowerCamelCase						:    Any         =			self.num_labels
         _lowerCamelCase						:    List[str]         =			FlaubertForTokenClassification(__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()

         _lowerCamelCase						:    Union[str, Any]         =			model(__lowerCAmelCase						, attention_mask=__lowerCAmelCase						, labels=__lowerCAmelCase		)
         self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.num_labels)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			self.num_choices
         _lowerCamelCase						:    Any         =			FlaubertForMultipleChoice(config=__lowerCAmelCase		)
         model.to(__lowerCAmelCase		)
         model.eval()
         _lowerCamelCase						:    List[str]         =			input_ids.unsqueeze(1		).expand(-1						, self.num_choices						, -1		).contiguous()
         _lowerCamelCase						:    List[str]         =			token_type_ids.unsqueeze(1		).expand(-1						, self.num_choices						, -1		).contiguous()
         _lowerCamelCase						:    List[Any]         =			input_mask.unsqueeze(1		).expand(-1						, self.num_choices						, -1		).contiguous()
         _lowerCamelCase						:    int         =			model(
             __lowerCAmelCase						, attention_mask=__lowerCAmelCase						, token_type_ids=__lowerCAmelCase						, labels=__lowerCAmelCase						, )
         self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_choices)		)




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






         """simple docstring"""






         _lowerCamelCase						:    Any         =			self.prepare_config_and_inputs()
         (
             (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   (
             _lowerCamelCase
         )      ,   
         )						:    Optional[Any]         =			config_and_inputs
         _lowerCamelCase						:    int         =			{
             '''input_ids''': input_ids,
             '''token_type_ids''': token_type_ids,
             '''lengths''': input_lengths,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict






@require_torch
class 	__snake_case	(     _lowercase  ,					_lowercase  ,					unittest.TestCase):
  snake_case__    :       List[str]        =						(
      (
          FlaubertModel,
          FlaubertWithLMHeadModel,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  snake_case__    :       List[Any]        =						(
      {
          "feature-extraction": FlaubertModel,
          "fill-mask": FlaubertWithLMHeadModel,
          "question-answering": FlaubertForQuestionAnsweringSimple,
          "text-classification": FlaubertForSequenceClassification,
          "token-classification": FlaubertForTokenClassification,
          "zero-shot": FlaubertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						Tuple		):






         """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 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Optional[int]=False		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			super()._prepare_for_class(__lowerCAmelCase						, __lowerCAmelCase						, return_labels=__lowerCAmelCase		)

         if return_labels:
                if model_class.__name__ == "FlaubertForQuestionAnswering":
                       _lowerCamelCase						:    Dict         =			torch.zeros(
                           self.model_tester.batch_size						, dtype=torch.long						, device=__lowerCAmelCase		)
                       _lowerCamelCase						:    Any         =			torch.zeros(
                           self.model_tester.batch_size						, dtype=torch.long						, device=__lowerCAmelCase		)

         return inputs_dict




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			FlaubertModelTester(self		)
         _lowerCamelCase						:    str         =			ConfigTester(self						, config_class=__lowerCAmelCase						, emb_dim=3_7		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         self.config_tester.run_common_tests()




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_simple_qa(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_token_classif(*__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_multiple_choice(*__lowerCAmelCase		)




  @slow
  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _lowerCamelCase						:    Dict         =			FlaubertModel.from_pretrained(__lowerCAmelCase		)
                self.assertIsNotNone(__lowerCAmelCase		)




  @slow
  @require_torch_gpu
  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase      ,   _lowerCamelCase						:    str         =			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

                _lowerCamelCase						:    Any         =			True
                _lowerCamelCase						:    int         =			model_class(config=__lowerCAmelCase		)

                _lowerCamelCase						:    List[str]         =			self._prepare_for_class(__lowerCAmelCase						, __lowerCAmelCase		)
                _lowerCamelCase						:    int         =			torch.jit.trace(
                    __lowerCAmelCase						, (inputs_dict['''input_ids'''].to('''cpu'''		), inputs_dict['''attention_mask'''].to('''cpu'''		))		)

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






@require_torch
class 	__snake_case	(     unittest.TestCase):




  @slow
  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			FlaubertModel.from_pretrained('''flaubert/flaubert_base_cased'''		)
         _lowerCamelCase						:    Any         =			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]		)
         with torch.no_grad():
                _lowerCamelCase						:    Any         =			model(__lowerCAmelCase		)[0]
         _lowerCamelCase						:    Optional[Any]         =			torch.Size((1, 1_1, 7_6_8)		)
         self.assertEqual(output.shape						, __lowerCAmelCase		)
         _lowerCamelCase						:    Optional[int]         =			torch.tensor(
             [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]]		)

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


