





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_							=				"""true"""





def  __magic_name__  (				lowercase				,       lowercase=82				,       lowercase=16				)				-> List[str]:

							"""simple docstring"""



							set_seed(42				)
							lowercase_	:   Optional[int]  				=     RegressionModel()
							lowercase_	:   Union[str, Any]  				=     deepcopy(lowercase				)
							lowercase_	:   List[str]  				=     RegressionDataset(length=lowercase				)
							lowercase_	:   List[Any]  				=     DataLoader(lowercase				,       batch_size=lowercase				)
							model.to(accelerator.device				)
							lowercase_				,		lowercase_	:   List[str]  				=     accelerator.prepare(lowercase				,       lowercase				)
							return model, ddp_model, dataloader





def  __magic_name__  (				lowercase				,       lowercase=False				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   List[str]  				=     AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""				)
							lowercase_	:   str  				=     load_dataset("""glue"""				,       """mrpc"""				,       split="""validation"""				)

							def tokenize_function(lowercase				):
														lowercase_	:   List[str]  				=     tokenizer(examples["""sentence1"""]				,       examples["""sentence2"""]				,       truncation=lowercase				,       max_length=lowercase				)
														return outputs

							with accelerator.main_process_first():
														lowercase_	:   List[str]  				=     dataset.map(
														    lowercase				,       batched=lowercase				,       remove_columns=["""idx""", """sentence1""", """sentence2"""]				,       )

							lowercase_	:   Optional[int]  				=     tokenized_datasets.rename_column("""label"""				,       """labels"""				)

							def collate_fn(lowercase				):
														if use_longest:
																					return tokenizer.pad(lowercase				,       padding="""longest"""				,       return_tensors="""pt"""				)
														return tokenizer.pad(lowercase				,       padding="""max_length"""				,       max_length=128				,       return_tensors="""pt"""				)

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





def  __magic_name__  (				lowercase				,       lowercase				)				-> int:

							"""simple docstring"""



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





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Any:

							"""simple docstring"""



							lowercase_	:   Any  				=     []
							for batch in dataloader:
														lowercase_				,		lowercase_	:   Optional[Any]  				=     batch.values()
														with torch.no_grad():
																					lowercase_	:   List[Any]  				=     model(lowercase				)
																					lowercase_				,		lowercase_	:   Tuple  				=     accelerator.gather_for_metrics((logit, target)				)
																					logits_and_targets.append((logit, target)				)
							lowercase_				,		lowercase_	:   Any  				=     [], []
							for logit, targ in logits_and_targets:
														logits.append(lowercase				)
														targs.append(lowercase				)
							lowercase_				,		lowercase_	:   Any  				=     torch.cat(lowercase				), torch.cat(lowercase				)
							return logits, targs





def  __magic_name__  (				lowercase				,       lowercase=82				,       lowercase=False				,       lowercase=False				,       lowercase=16				)				-> Union[str, Any]:

							"""simple docstring"""



							lowercase_				,		lowercase_				,		lowercase_	:   Any  				=     get_basic_setup(lowercase				,       lowercase				,       lowercase				)
							lowercase_				,		lowercase_	:   int  				=     generate_predictions(lowercase				,       lowercase				,       lowercase				)
							assert (
							    len(lowercase				) == num_samples
							), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase				)}"""





def  __magic_name__  (				lowercase = False				,       lowercase = False				)				-> Optional[Any]:

							"""simple docstring"""



							lowercase_	:   List[str]  				=     evaluate.load("""glue"""				,       """mrpc"""				)
							lowercase_				,		lowercase_	:   int  				=     get_mrpc_setup(lowercase				,       lowercase				)
							# First do baseline
							lowercase_				,		lowercase_				,		lowercase_	:   Dict  				=     setup["""no"""]
							model.to(lowercase				)
							model.eval()
							for batch in dataloader:
														batch.to(lowercase				)
														with torch.inference_mode():
																					lowercase_	:   Any  				=     model(**lowercase				)
														lowercase_	:   List[str]  				=     outputs.logits.argmax(dim=-1				)
														metric.add_batch(predictions=lowercase				,       references=batch["""labels"""]				)
							lowercase_	:   Optional[int]  				=     metric.compute()

							# Then do distributed
							lowercase_				,		lowercase_				,		lowercase_	:   List[Any]  				=     setup["""ddp"""]
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					lowercase_	:   Union[str, Any]  				=     model(**lowercase				)
														lowercase_	:   Optional[Any]  				=     outputs.logits.argmax(dim=-1				)
														lowercase_	:   Tuple  				=     batch["""labels"""]
														lowercase_				,		lowercase_	:   Any  				=     accelerator.gather_for_metrics((preds, references)				)
														metric.add_batch(predictions=lowercase				,       references=lowercase				)
							lowercase_	:   Dict  				=     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  __magic_name__  (				)				-> Union[str, Any]:

							"""simple docstring"""



							lowercase_	:   List[Any]  				=     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]:
																					lowercase_	:   str  				=     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**"""				)
							lowercase_	:   str  				=     Accelerator()
							test_torch_metrics(lowercase				,       512				)
							accelerator.state._reset_state()





def  __magic_name__  (				lowercase				)				-> List[str]:

							"""simple docstring"""



							main()


if __name__ == "__main__":
							main()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_							=				{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """M2M100ForConditionalGeneration""",
							    """M2M100Model""",
							    """M2M100PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
							from .tokenization_mam_aaa import MaMaaaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mam_aaa import (
														    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaMaaaForConditionalGeneration,
														    MaMaaaModel,
														    MaMaaaPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)