






"""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						:						List[str]					   =       '''true'''



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



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

				def tokenize_function(lowerCAmelCase__				):
								A__							 =       tokenizer(examples['sentence1']       ,examples['sentence2']       ,truncation=lowerCAmelCase__       ,max_length=lowerCAmelCase__				)
								return outputs

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

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



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



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=82       ,lowerCAmelCase__=False       ,lowerCAmelCase__=False       ,lowerCAmelCase__=16				):
				A__							 =       get_basic_setup(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				A__							 =       generate_predictions(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				assert (
				    len(lowerCAmelCase__				) == num_samples
				), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase__				)}'''



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

				# Then do distributed
				A__							 =       setup['ddp']
				model.eval()
				for batch in dataloader:
								with torch.inference_mode():
												A__							 =       model(**lowerCAmelCase__				)
								A__							 =       outputs.logits.argmax(dim=-1				)
								A__							 =       batch['labels']
								A__							 =       accelerator.gather_for_metrics((preds, references)				)
								metric.add_batch(predictions=lowerCAmelCase__       ,references=lowerCAmelCase__				)
				A__							 =       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					(					):
				A__							 =       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]:
												A__							 =       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**'				)
				A__							 =       Accelerator()
				test_torch_metrics(lowerCAmelCase__       ,512				)
				accelerator.state._reset_state()



def  __lowerCamelCase					(					lowerCAmelCase__				):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()












"""simple docstring"""






from ...processing_utils import ProcessorMixin





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					"""WhisperFeatureExtractor"""
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""WhisperTokenizer"""


	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					super().__init__(__a  ,					__a						)
					A__							 =       self.feature_extractor
					A__							 =       False


	def       _UpperCAmelCase						(      self  ,					__a=None  ,					__a=None  ,					__a=True						):


					"""simple docstring"""

					return self.tokenizer.get_decoder_prompt_ids(task=__a  ,					language=__a  ,					no_timestamps=__a						)


	def __call__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					if self._in_target_context_manager:
									return self.current_processor(*__a  ,					**__a						)

					A__							 =       kwargs.pop('audio'  ,					__a						)
					A__							 =       kwargs.pop('sampling_rate'  ,					__a						)
					A__							 =       kwargs.pop('text'  ,					__a						)
					if len(__a						) > 0:
									A__							 =       args[0]
									A__							 =       args[1:]

					if audio is None and text is None:
									raise ValueError('You need to specify either an `audio` or `text` input to process.'						)

					if audio is not None:
									A__							 =       self.feature_extractor(__a  ,					*__a  ,					sampling_rate=__a  ,					**__a						)
					if text is not None:
									A__							 =       self.tokenizer(__a  ,					**__a						)

					if text is None:
									return inputs

					elif audio is None:
									return encodings
					else:
									A__							 =       encodings['input_ids']
									return inputs


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					return self.tokenizer.batch_decode(*__a  ,					**__a						)


	def       _UpperCAmelCase						(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					return self.tokenizer.decode(*__a  ,					**__a						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a="np"						):


					"""simple docstring"""

					return self.tokenizer.get_prompt_ids(__a  ,					return_tensors=__a						)


