
"""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


SCREAMING_SNAKE_CASE_					     =	'''true'''

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase=82 ,    _lowerCAmelCase=16						):
						set_seed(42						)
						__lowerCAmelCase     =  RegressionModel()
						__lowerCAmelCase     =  deepcopy(_lowerCAmelCase						)
						__lowerCAmelCase     =  RegressionDataset(length=_lowerCAmelCase						)
						__lowerCAmelCase     =  DataLoader(_lowerCAmelCase ,    batch_size=_lowerCAmelCase						)
						model.to(accelerator.device						)
						__lowerCAmelCase   ,     __lowerCAmelCase     =  accelerator.prepare(_lowerCAmelCase ,    _lowerCAmelCase						)
						return model, ddp_model, dataloader

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase=False						):
						__lowerCAmelCase     =  AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""						)
						__lowerCAmelCase     =  load_dataset("""glue""" ,    """mrpc""" ,    split="""validation"""						)

						def tokenize_function(_lowerCAmelCase						):
												__lowerCAmelCase     =  tokenizer(examples["""sentence1"""] ,    examples["""sentence2"""] ,    truncation=_lowerCAmelCase ,    max_length=_lowerCAmelCase						)
												return outputs

						with accelerator.main_process_first():
												__lowerCAmelCase     =  dataset.map(
												    _lowerCAmelCase ,    batched=_lowerCAmelCase ,    remove_columns=["""idx""", """sentence1""", """sentence2"""] ,    )

						__lowerCAmelCase     =  tokenized_datasets.rename_column("""label""" ,    """labels"""						)

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

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

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  []
						for batch in dataloader:
												__lowerCAmelCase   ,     __lowerCAmelCase     =  batch.values()
												with torch.no_grad():
																		__lowerCAmelCase     =  model(_lowerCAmelCase						)
																		__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(_lowerCAmelCase						)
												targs.append(_lowerCAmelCase						)
						__lowerCAmelCase   ,     __lowerCAmelCase     =  torch.cat(_lowerCAmelCase						), torch.cat(_lowerCAmelCase						)
						return logits, targs

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase=82 ,    _lowerCAmelCase=False ,    _lowerCAmelCase=False ,    _lowerCAmelCase=16						):
						__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  get_basic_setup(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
						__lowerCAmelCase   ,     __lowerCAmelCase     =  generate_predictions(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
						assert (
						    len(_lowerCAmelCase						) == num_samples
						), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowerCAmelCase						)}"""

def  lowercase     (_lowerCAmelCase = False ,    _lowerCAmelCase = False						):
						__lowerCAmelCase     =  evaluate.load("""glue""" ,    """mrpc"""						)
						__lowerCAmelCase   ,     __lowerCAmelCase     =  get_mrpc_setup(_lowerCAmelCase ,    _lowerCAmelCase						)
						# First do baseline
						__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  setup["""no"""]
						model.to(_lowerCAmelCase						)
						model.eval()
						for batch in dataloader:
												batch.to(_lowerCAmelCase						)
												with torch.inference_mode():
																		__lowerCAmelCase     =  model(**_lowerCAmelCase						)
												__lowerCAmelCase     =  outputs.logits.argmax(dim=-1						)
												metric.add_batch(predictions=_lowerCAmelCase ,    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(**_lowerCAmelCase						)
												__lowerCAmelCase     =  outputs.logits.argmax(dim=-1						)
												__lowerCAmelCase     =  batch["""labels"""]
												__lowerCAmelCase   ,     __lowerCAmelCase     =  accelerator.gather_for_metrics((preds, references)						)
												metric.add_batch(predictions=_lowerCAmelCase ,    references=_lowerCAmelCase						)
						__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  lowercase     ():
						__lowerCAmelCase     =  Accelerator(split_batches=_lowerCAmelCase ,    dispatch_batches=_lowerCAmelCase						)
						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(_lowerCAmelCase ,    _lowerCAmelCase						)
																								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=_lowerCAmelCase ,    dispatch_batches=_lowerCAmelCase						)
																		if accelerator.is_local_main_process:
																								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""						)
																		test_torch_metrics(_lowerCAmelCase ,    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(_lowerCAmelCase ,    512						)
						accelerator.state._reset_state()

def  lowercase     (_lowerCAmelCase						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
			main()


"""simple docstring"""
import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
SCREAMING_SNAKE_CASE_					     =	[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]

def  lowercase     (_lowerCAmelCase						):
						# Test all the extensions added in the setup
						for file in FILES_TO_FIND:
												if not (transformers_path / file).exists():
																		return False
						return True


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			if args.check_lib:
						SCREAMING_SNAKE_CASE_					     =	importlib.import_module('''transformers''')
						SCREAMING_SNAKE_CASE_					     =	Path(transformers_module.__file__).parent
			else:
						SCREAMING_SNAKE_CASE_					     =	Path.cwd() / '''build/lib/transformers'''
			if not test_custom_files_are_present(transformers_path):
						raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')
