






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_UpperCAmelCase			:		Tuple   				=    "true"





def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       Union[str, Any]=82     ,					lowercase__						:       Union[str, Any]=16       )		->    Dict:


				'''simple docstring'''




				set_seed(42       )
				lowerCAmelCase__      			=						RegressionModel()
				lowerCAmelCase__      			=						deepcopy(lowercase__       )
				lowerCAmelCase__      			=						RegressionDataset(length=lowercase__       )
				lowerCAmelCase__      			=						DataLoader(lowercase__     ,					batch_size=lowercase__       )
				model.to(accelerator.device       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.prepare(lowercase__     ,					lowercase__       )
				return model, ddp_model, dataloader





def 	lowerCAmelCase_		(lowercase__						:       Accelerator     ,					lowercase__						:       Optional[int]=False       )		->    Dict:


				'''simple docstring'''




				lowerCAmelCase__      			=						AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''       )
				lowerCAmelCase__      			=						load_dataset('''glue'''     ,					'''mrpc'''     ,					split='''validation'''       )

				def tokenize_function(lowercase__						:       Union[str, Any]       ):
								lowerCAmelCase__      			=						tokenizer(examples['''sentence1''']     ,					examples['''sentence2''']     ,					truncation=lowercase__     ,					max_length=lowercase__       )
								return outputs

				with accelerator.main_process_first():
								lowerCAmelCase__      			=						dataset.map(
								    lowercase__     ,					batched=lowercase__     ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,					)

				lowerCAmelCase__      			=						tokenized_datasets.rename_column('''label'''     ,					'''labels'''       )

				def collate_fn(lowercase__						:       Optional[Any]       ):
								if use_longest:
												return tokenizer.pad(lowercase__     ,					padding='''longest'''     ,					return_tensors='''pt'''       )
								return tokenizer.pad(lowercase__     ,					padding='''max_length'''     ,					max_length=1_28     ,					return_tensors='''pt'''       )

				return DataLoader(lowercase__     ,					shuffle=lowercase__     ,					collate_fn=lowercase__     ,					batch_size=16       )





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


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator(dispatch_batches=lowercase__     ,					split_batches=lowercase__       )
				lowerCAmelCase__      			=						get_dataloader(lowercase__     ,					not dispatch_batches       )
				lowerCAmelCase__      			=						AutoModelForSequenceClassification.from_pretrained(
				    '''hf-internal-testing/mrpc-bert-base-cased'''     ,					return_dict=lowercase__       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.prepare(lowercase__     ,					lowercase__       )
				return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





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


				'''simple docstring'''




				lowerCAmelCase__      			=						[]
				for batch in dataloader:
								lowerCAmelCase__			,		lowerCAmelCase__      			=						batch.values()
								with torch.no_grad():
												lowerCAmelCase__      			=						model(lowercase__       )
												lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather_for_metrics((logit, target)       )
												logits_and_targets.append((logit, target)       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						[], []
				for logit, targ in logits_and_targets:
								logits.append(lowercase__       )
								targs.append(lowercase__       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						torch.cat(lowercase__       ), torch.cat(lowercase__       )
				return logits, targs





def 	lowerCAmelCase_		(lowercase__						:       Accelerator     ,					lowercase__						:       Optional[Any]=82     ,					lowercase__						:       Dict=False     ,					lowercase__						:       str=False     ,					lowercase__						:       List[str]=16       )		->    Any:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						get_basic_setup(lowercase__     ,					lowercase__     ,					lowercase__       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						generate_predictions(lowercase__     ,					lowercase__     ,					lowercase__       )
				assert (
				    len(lowercase__       ) == num_samples
				), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase__       )}'





def 	lowerCAmelCase_		(lowercase__						:       bool = False     ,					lowercase__						:       bool = False       )		->    Optional[int]:


				'''simple docstring'''




				lowerCAmelCase__      			=						evaluate.load('''glue'''     ,					'''mrpc'''       )
				lowerCAmelCase__			,		lowerCAmelCase__      			=						get_mrpc_setup(lowercase__     ,					lowercase__       )
				# First do baseline
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						setup['''no''']
				model.to(lowercase__       )
				model.eval()
				for batch in dataloader:
								batch.to(lowercase__       )
								with torch.inference_mode():
												lowerCAmelCase__      			=						model(**lowercase__       )
								lowerCAmelCase__      			=						outputs.logits.argmax(dim=-1       )
								metric.add_batch(predictions=lowercase__     ,					references=batch['''labels''']       )
				lowerCAmelCase__      			=						metric.compute()

				# Then do distributed
				lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						setup['''ddp''']
				model.eval()
				for batch in dataloader:
								with torch.inference_mode():
												lowerCAmelCase__      			=						model(**lowercase__       )
								lowerCAmelCase__      			=						outputs.logits.argmax(dim=-1       )
								lowerCAmelCase__      			=						batch['''labels''']
								lowerCAmelCase__			,		lowerCAmelCase__      			=						accelerator.gather_for_metrics((preds, references)       )
								metric.add_batch(predictions=lowercase__     ,					references=lowercase__       )
				lowerCAmelCase__      			=						metric.compute()

				for key in "accuracy f1".split():
								assert math.isclose(
								    baseline[key]     ,					distributed[key]       ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'





def 	lowerCAmelCase_		()		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						Accelerator(split_batches=lowercase__     ,					dispatch_batches=lowercase__       )
				if accelerator.is_local_main_process:
								datasets.utils.logging.set_verbosity_warning()
								transformers.utils.logging.set_verbosity_warning()
				else:
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				# These are a bit slower so they should only be ran on the GPU or TPU
				if torch.cuda.is_available() or is_tpu_available():
								if accelerator.is_local_main_process:
												print('''**Testing gather_for_metrics**'''       )
								for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																if accelerator.is_local_main_process:
																				print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'       )
																test_mrpc(lowercase__     ,					lowercase__       )
																accelerator.state._reset_state()
				if accelerator.is_local_main_process:
								print('''**Test torch metrics**'''       )
				for split_batches in [True, False]:
								for dispatch_batches in [True, False]:
												lowerCAmelCase__      			=						Accelerator(split_batches=lowercase__     ,					dispatch_batches=lowercase__       )
												if accelerator.is_local_main_process:
																print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'       )
												test_torch_metrics(lowercase__     ,					99       )
												accelerator.state._reset_state()
				if accelerator.is_local_main_process:
								print('''**Test last batch is not dropped when perfectly divisible**'''       )
				lowerCAmelCase__      			=						Accelerator()
				test_torch_metrics(lowercase__     ,					5_12       )
				accelerator.state._reset_state()





def 	lowerCAmelCase_		(lowercase__						:       Dict       )		->    Tuple:


				'''simple docstring'''




				main()


if __name__ == "__main__":
			main()













from ...configuration_utils import PretrainedConfig
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:List[str]		      =    'bert-generation'




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[str]=50_358 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=1_024 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=24 ,							SCREAMING_SNAKE_CASE_     :   Any=16 ,							SCREAMING_SNAKE_CASE_     :   Tuple=4_096 ,							SCREAMING_SNAKE_CASE_     :   str="gelu" ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=512 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=1e-12 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=2 ,							SCREAMING_SNAKE_CASE_     :   int=1 ,							SCREAMING_SNAKE_CASE_     :   str="absolute" ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,							bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						layer_norm_eps
											lowerCAmelCase__      			=						position_embedding_type
											lowerCAmelCase__      			=						use_cache





