



'''simple docstring'''



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


_lowercase    :    int			     =  """true"""


def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Union[str, Any]=82   ,     A     :    Optional[int]=16  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    Accelerator   ,     A     :    Tuple=False  ):




						'''simple docstring'''






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

						def tokenize_function(A     :    Any  ):
												UpperCAmelCase         =			tokenizer(examples['''sentence1''']   ,     examples['''sentence2''']   ,     truncation=A   ,     max_length=A  )
												return outputs

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

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

						def collate_fn(A     :    Tuple  ):
												if use_longest:
																		return tokenizer.pad(A   ,     padding='''longest'''   ,     return_tensors='''pt'''  )
												return tokenizer.pad(A   ,     padding='''max_length'''   ,     max_length=1_28   ,     return_tensors='''pt'''  )

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


def    lowerCamelCase__   ( A     :    Any   ,     A     :    List[Any]  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    str   ,     A     :    List[Any]   ,     A     :    Optional[int]  ):




						'''simple docstring'''






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


def    lowerCamelCase__   ( A     :    Accelerator   ,     A     :    Dict=82   ,     A     :    Dict=False   ,     A     :    Dict=False   ,     A     :    List[Any]=16  ):




						'''simple docstring'''






						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			get_basic_setup(A   ,     A   ,     A  )
						UpperCAmelCase       ,       UpperCAmelCase         =			generate_predictions(A   ,     A   ,     A  )
						assert (
						    len(A  ) == num_samples
						), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A  )}"""


def    lowerCamelCase__   ( A     :    bool = False   ,     A     :    bool = False  ):




						'''simple docstring'''






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

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




						'''simple docstring'''






						UpperCAmelCase         =			Accelerator(split_batches=A   ,     dispatch_batches=A  )
						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(A   ,     A  )
																								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]:
																		UpperCAmelCase         =			Accelerator(split_batches=A   ,     dispatch_batches=A  )
																		if accelerator.is_local_main_process:
																								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""  )
																		test_torch_metrics(A   ,     99  )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''  )
						UpperCAmelCase         =			Accelerator()
						test_torch_metrics(A   ,     5_12  )
						accelerator.state._reset_state()


def    lowerCamelCase__   ( A     :    Any  ):




						'''simple docstring'''






						main()


if __name__ == "__main__":
	main()









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTBigCodeForSequenceClassification""",
	    """GPTBigCodeForTokenClassification""",
	    """GPTBigCodeForCausalLM""",
	    """GPTBigCodeModel""",
	    """GPTBigCodePreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_bigcode import (
		    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTBigCodeForCausalLM,
		    GPTBigCodeForSequenceClassification,
		    GPTBigCodeForTokenClassification,
		    GPTBigCodeModel,
		    GPTBigCodePreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





