






import argparse
import json
import os

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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCAmelCase			:		Union[str, Any]   				=    16
_UpperCAmelCase			:		Optional[int]   				=    32





def 	lowerCAmelCase_		(lowercase__						:       Accelerator     ,					lowercase__						:       int = 16     ,					lowercase__						:       str = "bert-base-cased"       )		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						AutoTokenizer.from_pretrained(lowercase__       )
				lowerCAmelCase__      			=						load_dataset('''glue'''     ,					'''mrpc'''       )

				def tokenize_function(lowercase__						:       List[Any]       ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__      			=						tokenizer(examples['''sentence1''']     ,					examples['''sentence2''']     ,					truncation=lowercase__     ,					max_length=lowercase__       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				lowerCAmelCase__      			=						datasets.map(
				    lowercase__     ,					batched=lowercase__     ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,					load_from_cache_file=lowercase__       )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCAmelCase__      			=						tokenized_datasets.rename_column('''label'''     ,					'''labels'''       )

				def collate_fn(lowercase__						:       List[str]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowercase__     ,					padding='''max_length'''     ,					max_length=1_28     ,					return_tensors='''pt'''       )
								return tokenizer.pad(lowercase__     ,					padding='''longest'''     ,					return_tensors='''pt'''       )

				# Instantiate dataloaders.
				lowerCAmelCase__      			=						DataLoader(
				    tokenized_datasets['''train''']     ,					shuffle=lowercase__     ,					collate_fn=lowercase__     ,					batch_size=lowercase__       )
				lowerCAmelCase__      			=						DataLoader(
				    tokenized_datasets['''validation''']     ,					shuffle=lowercase__     ,					collate_fn=lowercase__     ,					batch_size=lowercase__       )

				return train_dataloader, eval_dataloader





def 	lowerCAmelCase_		(lowercase__						:       Any     ,					lowercase__						:       Tuple       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase__      			=						config['''lr''']
				lowerCAmelCase__      			=						int(config['''num_epochs''']       )
				lowerCAmelCase__      			=						int(config['''seed''']       )
				lowerCAmelCase__      			=						int(config['''batch_size''']       )
				lowerCAmelCase__      			=						args.model_name_or_path

				set_seed(lowercase__       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						get_dataloaders(lowercase__     ,					lowercase__     ,					lowercase__       )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase__      			=						AutoModelForSequenceClassification.from_pretrained(lowercase__     ,					return_dict=lowercase__       )

				# Instantiate optimizer
				lowerCAmelCase__      			=						(
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				lowerCAmelCase__      			=						optimizer_cls(params=model.parameters()     ,					lr=lowercase__       )

				if accelerator.state.deepspeed_plugin is not None:
								lowerCAmelCase__      			=						accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								lowerCAmelCase__      			=						1
				lowerCAmelCase__      			=						(len(lowercase__       ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								lowerCAmelCase__      			=						get_linear_schedule_with_warmup(
								    optimizer=lowercase__     ,					num_warmup_steps=0     ,					num_training_steps=lowercase__     ,					)
				else:
								lowerCAmelCase__      			=						DummyScheduler(lowercase__     ,					total_num_steps=lowercase__     ,					warmup_num_steps=0       )

				# 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.
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.prepare(
				    lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__       )

				# We need to keep track of how many total steps we have iterated over
				lowerCAmelCase__      			=						0
				# We also need to keep track of the stating epoch so files are named properly
				lowerCAmelCase__      			=						0

				# Now we train the model
				lowerCAmelCase__      			=						evaluate.load('''glue'''     ,					'''mrpc'''       )
				lowerCAmelCase__      			=						0
				lowerCAmelCase__      			=						{}
				for epoch in range(lowercase__     ,					lowercase__       ):
								model.train()
								for step, batch in enumerate(lowercase__       ):
												lowerCAmelCase__      			=						model(**lowercase__       )
												lowerCAmelCase__      			=						outputs.loss
												lowerCAmelCase__      			=						loss / gradient_accumulation_steps
												accelerator.backward(lowercase__       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

								model.eval()
								lowerCAmelCase__      			=						0
								for step, batch in enumerate(lowercase__       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																lowerCAmelCase__      			=						model(**lowercase__       )
												lowerCAmelCase__      			=						outputs.logits.argmax(dim=-1       )
												# It is slightly faster to call this once, than multiple times
												lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather(
												    (predictions, batch['''labels'''])       )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																if step == len(lowercase__       ) - 1:
																				lowerCAmelCase__      			=						predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				lowerCAmelCase__      			=						references[: len(eval_dataloader.dataset       ) - samples_seen]
																else:
																				samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=lowercase__     ,					references=lowercase__     ,					)

								lowerCAmelCase__      			=						metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'epoch {epoch}:'     ,					lowercase__       )
								lowerCAmelCase__      			=						eval_metric['''accuracy''']

								if best_performance < eval_metric["accuracy"]:
												lowerCAmelCase__      			=						eval_metric['''accuracy''']

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir     ,					'''all_results.json'''       )     ,					'''w'''       ) as f:
												json.dump(lowercase__     ,					lowercase__       )





def 	lowerCAmelCase_		()		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
				parser.add_argument(
				    '''--model_name_or_path'''     ,					type=lowercase__     ,					default='''bert-base-cased'''     ,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''     ,					required=lowercase__     ,					)
				parser.add_argument(
				    '''--output_dir'''     ,					type=lowercase__     ,					default='''.'''     ,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''     ,					)
				parser.add_argument(
				    '''--performance_lower_bound'''     ,					type=lowercase__     ,					default=lowercase__     ,					help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''     ,					)
				parser.add_argument(
				    '''--num_epochs'''     ,					type=lowercase__     ,					default=3     ,					help='''Number of train epochs.'''     ,					)
				lowerCAmelCase__      			=						parser.parse_args()
				lowerCAmelCase__      			=						{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase__     ,					lowercase__       )


if __name__ == "__main__":
			main()













import unittest

from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import BertGenerationDecoder, BertGenerationEncoder
class  lowerCAmelCase_       :




							def __init__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=13 ,							SCREAMING_SNAKE_CASE_     :   Dict=7 ,							SCREAMING_SNAKE_CASE_     :   Dict=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=True ,							SCREAMING_SNAKE_CASE_     :   List[str]=99 ,							SCREAMING_SNAKE_CASE_     :   str=32 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=5 ,							SCREAMING_SNAKE_CASE_     :   str=4 ,							SCREAMING_SNAKE_CASE_     :   Tuple=37 ,							SCREAMING_SNAKE_CASE_     :   List[Any]="gelu" ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=0.1 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=50 ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.02 ,							SCREAMING_SNAKE_CASE_     :   List[str]=True ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=None ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						seq_length
											lowerCAmelCase__      			=						is_training
											lowerCAmelCase__      			=						use_input_mask
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						use_labels
											lowerCAmelCase__      			=						scope




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size	)

											lowerCAmelCase__      			=						None
											if self.use_input_mask:
															lowerCAmelCase__      			=						random_attention_mask([self.batch_size, self.seq_length]	)

											if self.use_labels:
															lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size	)

											lowerCAmelCase__      			=						self.get_config()

											return config, input_ids, input_mask, token_labels




							def 						__snake_case					(   self     :   List[str]	):
											return BertGenerationConfig(
											    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 ,							is_decoder=SCREAMING_SNAKE_CASE_ ,							initializer_range=self.initializer_range ,							)




							def 						__snake_case					(   self     :   str	):
											(
											    (
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		
											)      			=						self.prepare_config_and_inputs()

											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
											lowerCAmelCase__      			=						ids_tensor([self.batch_size, self.seq_length] ,							vocab_size=2	)

											return (
											    config,
											    input_ids,
											    input_mask,
											    token_labels,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							):
											lowerCAmelCase__      			=						BertGenerationEncoder(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)	)




							def 						__snake_case					(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						BertGenerationEncoder(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							)
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							)
											self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)	)




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							):
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						True
											lowerCAmelCase__      			=						BertGenerationDecoder(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()

											# first forward pass
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							use_cache=SCREAMING_SNAKE_CASE_ ,							)
											lowerCAmelCase__      			=						outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											lowerCAmelCase__      			=						ids_tensor((self.batch_size, 3) ,							config.vocab_size	)
											lowerCAmelCase__      			=						ids_tensor((self.batch_size, 3) ,							vocab_size=2	)

											# append to next input_ids and
											lowerCAmelCase__      			=						torch.cat([input_ids, next_tokens] ,							dim=-1	)
											lowerCAmelCase__      			=						torch.cat([input_mask, next_mask] ,							dim=-1	)

											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							output_hidden_states=SCREAMING_SNAKE_CASE_ ,							)['''hidden_states'''][0]
											lowerCAmelCase__      			=						model(
											    SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,							encoder_attention_mask=SCREAMING_SNAKE_CASE_ ,							past_key_values=SCREAMING_SNAKE_CASE_ ,							output_hidden_states=SCREAMING_SNAKE_CASE_ ,							)['''hidden_states'''][0]

											# select random slice
											lowerCAmelCase__      			=						ids_tensor((1,) ,							output_from_past.shape[-1]	).item()
											lowerCAmelCase__      			=						output_from_no_past[:, -3:, random_slice_idx].detach()
											lowerCAmelCase__      			=						output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-3	)	)




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							*SCREAMING_SNAKE_CASE_     :   Tuple ,							):
											lowerCAmelCase__      			=						BertGenerationDecoder(SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_ ,							attention_mask=SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)	)




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						self.prepare_config_and_inputs()
											lowerCAmelCase__      			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict



@require_torch
class  lowerCAmelCase_       (				snake_case__     ,    snake_case__     ,    snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Dict		      =    (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
							UpperCamelCase_		:str		      =    (BertGenerationDecoder,) if is_torch_available() else ()
							UpperCamelCase_		:List[str]		      =    (
							    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
							    if is_torch_available()
							    else {}
							)




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						BertGenerationEncoderTester(self	)
											lowerCAmelCase__      			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_ ,							hidden_size=37	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											self.config_tester.run_common_tests()




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											lowerCAmelCase__      			=						'''bert'''
											self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Any	):
											# This regression test was failing with PyTorch < 1.3
											(
											    (
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		(
											    lowerCAmelCase__
											)			,		
											)      			=						self.model_tester.prepare_config_and_inputs_for_decoder()

											lowerCAmelCase__      			=						None

											self.model_tester.create_and_check_model_as_decoder(
											    SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_decoder()
											self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_	)




							@slow
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)



@require_torch
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											lowerCAmelCase__      			=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
											with torch.no_grad():
															lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											lowerCAmelCase__      			=						torch.Size([1, 8, 1_024]	)
											self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						torch.tensor(
											    [[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]]	)
											self.assertTrue(torch.allclose(output[:, :3, :3] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)



@require_torch
class  lowerCAmelCase_       (				unittest.TestCase					):




							@slow
							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
											lowerCAmelCase__      			=						torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]	)
											with torch.no_grad():
															lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)[0]
											lowerCAmelCase__      			=						torch.Size([1, 8, 50_358]	)
											self.assertEqual(output.shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						torch.tensor(
											    [[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]]	)
											self.assertTrue(torch.allclose(output[:, :3, :3] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)





