






import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__				     =			16
SCREAMING_SNAKE_CASE__				     =			32

def        UpperCAmelCase__		(						lowerCamelCase_       :       Accelerator   ,  lowerCamelCase_       :       int = 1_6				):
			__a						:  Tuple									=     AutoTokenizer.from_pretrained('bert-base-cased'				)
			__a						:  int									=     load_dataset('glue'   ,  'mrpc'				)

			def tokenize_function(lowerCamelCase_       :       Union[str, Any]				):
						# max_length=None => use the model max length (it's actually the default)
						__a						:  int									=     tokenizer(examples['sentence1']   ,  examples['sentence2']   ,  truncation=lowerCamelCase_   ,  max_length=lowerCamelCase_				)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						__a						:  Optional[Any]									=     datasets.map(
						    lowerCamelCase_   ,  batched=lowerCamelCase_   ,  remove_columns=['idx', 'sentence1', 'sentence2']   ,  )

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			__a						:  Optional[int]									=     tokenized_datasets.rename_column('label'   ,  'labels'				)

			def collate_fn(lowerCamelCase_       :       Dict				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__a						:  Optional[int]									=     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									__a						:  List[Any]									=     1_6
						elif accelerator.mixed_precision != "no":
									__a						:  Union[str, Any]									=     8
						else:
									__a						:  List[str]									=     None

						return tokenizer.pad(
						    lowerCamelCase_   ,  padding='longest'   ,  max_length=lowerCamelCase_   ,  pad_to_multiple_of=lowerCamelCase_   ,  return_tensors='pt'   ,  )

			# Instantiate dataloaders.
			__a						:  List[str]									=     DataLoader(
			    tokenized_datasets['train']   ,  shuffle=lowerCamelCase_   ,  collate_fn=lowerCamelCase_   ,  batch_size=lowerCamelCase_   ,  drop_last=lowerCamelCase_				)
			__a						:  List[str]									=     DataLoader(
			    tokenized_datasets['validation']   ,  shuffle=lowerCamelCase_   ,  collate_fn=lowerCamelCase_   ,  batch_size=lowerCamelCase_   ,  drop_last=(accelerator.mixed_precision == 'fp8')   ,  )

			return train_dataloader, eval_dataloader

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       List[str]				):
			# Initialize accelerator
			__a						:  Tuple									=     Accelerator(cpu=args.cpu   ,  mixed_precision=args.mixed_precision				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a						:  List[str]									=     config['lr']
			__a						:  Tuple									=     int(config['num_epochs']				)
			__a						:  Tuple									=     int(config['seed']				)
			__a						:  Union[str, Any]									=     int(config['batch_size']				)

			__a						:  str									=     evaluate.load('glue'   ,  'mrpc'				)

			# If the batch size is too big we use gradient accumulation
			__a						:  int									=     1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__a						:  str									=     batch_size // MAX_GPU_BATCH_SIZE
						__a						:  Optional[int]									=     MAX_GPU_BATCH_SIZE

			set_seed(lowerCamelCase_				)
			__a						,					__a						:  List[str]									=     get_dataloaders(lowerCamelCase_   ,  lowerCamelCase_				)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a						:  Dict									=     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   ,  return_dict=lowerCamelCase_				)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			__a						:  int									=     model.to(accelerator.device				)
			# Instantiate optimizer
			__a						:  Any									=     AdamW(params=model.parameters()   ,  lr=lowerCamelCase_				)

			# Instantiate scheduler
			__a						:  Optional[int]									=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase_   ,  num_warmup_steps=1_0_0   ,  num_training_steps=(len(lowerCamelCase_				) * num_epochs) // gradient_accumulation_steps   ,  )

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.

			__a						,					__a						,					__a						,					__a						,					__a						:  Dict									=     accelerator.prepare(
			    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			# Now we train the model
			for epoch in range(lowerCamelCase_				):
						model.train()
						for step, batch in enumerate(lowerCamelCase_				):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device				)
									__a						:  int									=     model(**lowerCamelCase_				)
									__a						:  Optional[Any]									=     outputs.loss
									__a						:  Optional[int]									=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase_				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(lowerCamelCase_				):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device				)
									with torch.no_grad():
												__a						:  List[str]									=     model(**lowerCamelCase_				)
									__a						:  Any									=     outputs.logits.argmax(dim=-1				)
									__a						,					__a						:  int									=     accelerator.gather_for_metrics((predictions, batch['labels'])				)
									metric.add_batch(
									    predictions=lowerCamelCase_   ,  references=lowerCamelCase_   ,  )

						__a						:  Optional[Any]									=     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''   ,  lowerCamelCase_				)

def        UpperCAmelCase__		(						):
			__a						:  Optional[int]									=     argparse.ArgumentParser(description='Simple example of training script.'				)
			parser.add_argument(
			    '--mixed_precision'   ,  type=lowerCamelCase_   ,  default=lowerCamelCase_   ,  choices=['no', 'fp16', 'bf16', 'fp8']   ,  help='Whether to use mixed precision. Choose'
			    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
			    'and an Nvidia Ampere GPU.'   ,  )
			parser.add_argument('--cpu'   ,  action='store_true'   ,  help='If passed, will train on the CPU.'				)
			__a						:  List[Any]									=     parser.parse_args()
			__a						:  Optional[int]									=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
			training_function(lowerCamelCase_   ,  lowerCamelCase_				)


if __name__ == "__main__":
							main()







"""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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							lowercase_=2 ,							lowercase_=9_9 ,							lowercase_=0 ,							lowercase_=3_2 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=5_1_2 ,							lowercase_=1_2 ,							lowercase_=2 ,							lowercase_=0.0_2 ,							lowercase_=3 ,							lowercase_=4 ,							lowercase_="last" ,							lowercase_=None ,							lowercase_=None ,							)    ->       str:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       seq_length
          UpperCAmelCase          =       is_training
          UpperCAmelCase          =       use_input_lengths
          UpperCAmelCase          =       use_token_type_ids
          UpperCAmelCase          =       use_labels
          UpperCAmelCase          =       gelu_activation
          UpperCAmelCase          =       sinusoidal_embeddings
          UpperCAmelCase          =       causal
          UpperCAmelCase          =       asm
          UpperCAmelCase          =       n_langs
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       n_special
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       type_vocab_size
          UpperCAmelCase          =       type_sequence_label_size
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       num_labels
          UpperCAmelCase          =       num_choices
          UpperCAmelCase          =       summary_type
          UpperCAmelCase          =       use_proj
          UpperCAmelCase          =       scope




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size				)
          UpperCAmelCase          =       random_attention_mask([self.batch_size, self.seq_length]				)

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

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

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

          UpperCAmelCase          =       self.get_config()

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




      def      a_   (				self				)    ->       Union[str, Any]:
          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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       FlaubertModel(config=lowercase_				)
          model.to(lowercase_				)
          model.eval()
          UpperCAmelCase          =       model(lowercase_ ,							lengths=lowercase_ ,							langs=lowercase_				)
          UpperCAmelCase          =       model(lowercase_ ,							langs=lowercase_				)
          UpperCAmelCase          =       model(lowercase_				)
          self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Any:
          UpperCAmelCase          =       FlaubertWithLMHeadModel(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       FlaubertForQuestionAnsweringSimple(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)

          UpperCAmelCase          =       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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       int:
          UpperCAmelCase          =       FlaubertForQuestionAnswering(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)

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

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

          ((UpperCAmelCase)      ,				)          =       result_with_labels.to_tuple()

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

          ((UpperCAmelCase)      ,				)          =       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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       Dict:
          UpperCAmelCase          =       FlaubertForSequenceClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_				)
          UpperCAmelCase          =       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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							)    ->       List[str]:
          UpperCAmelCase          =       self.num_labels
          UpperCAmelCase          =       FlaubertForTokenClassification(lowercase_				)
          model.to(lowercase_				)
          model.eval()

          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							labels=lowercase_				)
          self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)				)




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




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				(
              UpperCAmelCase
          )      ,				
          )          =       config_and_inputs
          UpperCAmelCase          =       {
              'input_ids': input_ids,
              'token_type_ids': token_type_ids,
              'lengths': input_lengths,
              'attention_mask': input_mask,
          }
          return config, inputs_dict

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




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Union[str, Any]:
          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      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=False				)    ->       List[str]:
          UpperCAmelCase          =       super()._prepare_for_class(lowercase_ ,							lowercase_ ,							return_labels=lowercase_				)

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

          return inputs_dict




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       FlaubertModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=lowercase_ ,							emb_dim=3_7				)




      def      a_   (				self				)    ->       Optional[Any]:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       Dict:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_model(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_lm_head(*lowercase_				)




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_simple_qa(*lowercase_				)




      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_qa(*lowercase_				)




      def      a_   (				self				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_				)




      def      a_   (				self				)    ->       Tuple:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_token_classif(*lowercase_				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_multiple_choice(*lowercase_				)




      @slow
      def      a_   (				self				)    ->       List[str]:
          for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase          =       FlaubertModel.from_pretrained(lowercase_				)
              self.assertIsNotNone(lowercase_				)




      @slow
      @require_torch_gpu
      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase      ,				UpperCAmelCase          =       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

              UpperCAmelCase          =       True
              UpperCAmelCase          =       model_class(config=lowercase_				)

              UpperCAmelCase          =       self._prepare_for_class(lowercase_ ,							lowercase_				)
              UpperCAmelCase          =       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'				)				)
                  UpperCAmelCase          =       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   _UpperCAmelCase				(		unittest.TestCase				):




      @slow
      def      a_   (				self				)    ->       List[Any]:
          UpperCAmelCase          =       FlaubertModel.from_pretrained('flaubert/flaubert_base_cased'				)
          UpperCAmelCase          =       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():
              UpperCAmelCase          =       model(lowercase_				)[0]
          UpperCAmelCase          =       torch.Size((1, 1_1, 7_6_8)				)
          self.assertEqual(output.shape ,							lowercase_				)
          UpperCAmelCase          =       torch.tensor(
              [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]				)

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



