






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


__snake_case    :    str         =     """true"""







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Optional[Any]=82    ,       UpperCamelCase_       :					str=16		)  ->   str:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Accelerator    ,       UpperCamelCase_       :					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(UpperCamelCase_       :					int		):
												lowerCAmelCase__    						=						tokenizer(examples['sentence1']    ,       examples['sentence2']    ,       truncation=UpperCamelCase_    ,       max_length=UpperCamelCase_		)
												return outputs

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

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

						def collate_fn(UpperCamelCase_       :					List[str]		):
												if use_longest:
																		return tokenizer.pad(UpperCamelCase_    ,       padding='longest'    ,       return_tensors='pt'		)
												return tokenizer.pad(UpperCamelCase_    ,       padding='max_length'    ,       max_length=128    ,       return_tensors='pt'		)

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Dict		)  ->   Dict:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					Optional[int]		)  ->   str:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						for batch in dataloader:
												lowerCAmelCase__						,       lowerCAmelCase__    						=						batch.values()
												with torch.no_grad():
																		lowerCAmelCase__    						=						model(UpperCamelCase_		)
																		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(UpperCamelCase_		)
												targs.append(UpperCamelCase_		)
						lowerCAmelCase__						,       lowerCAmelCase__    						=						torch.cat(UpperCamelCase_		), torch.cat(UpperCamelCase_		)
						return logits, targs







def 	_UpperCamelCase   (    UpperCamelCase_       :					Accelerator    ,       UpperCamelCase_       :					List[Any]=82    ,       UpperCamelCase_       :					Optional[Any]=False    ,       UpperCamelCase_       :					List[Any]=False    ,       UpperCamelCase_       :					List[str]=16		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						get_basic_setup(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
						lowerCAmelCase__						,       lowerCAmelCase__    						=						generate_predictions(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
						assert (
						    len(UpperCamelCase_		) == num_samples
						), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase_		)}"







def 	_UpperCamelCase   (    UpperCamelCase_       :					bool = False    ,       UpperCamelCase_       :					bool = False		)  ->   Any:

						"""simple docstring"""



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

						"""simple docstring"""



						lowerCAmelCase__    						=						Accelerator(split_batches=UpperCamelCase_    ,       dispatch_batches=UpperCamelCase_		)
						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(UpperCamelCase_    ,       UpperCamelCase_		)
																								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=UpperCamelCase_    ,       dispatch_batches=UpperCamelCase_		)
																		if accelerator.is_local_main_process:
																								print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"		)
																		test_torch_metrics(UpperCamelCase_    ,       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(UpperCamelCase_    ,       512		)
						accelerator.state._reset_state()







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]		)  ->   Dict:

						"""simple docstring"""



						main()


if __name__ == "__main__":
					main()













from typing import TYPE_CHECKING

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


__snake_case    :    Tuple         =     {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Dict         =     ["""GLPNFeatureExtractor"""]
					__snake_case    :    Optional[Any]         =     ["""GLPNImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     [
					    """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GLPNForDepthEstimation""",
					    """GLPNLayer""",
					    """GLPNModel""",
					    """GLPNPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





