

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


A__						=							"true"




def 					_lowercase     (				a_				:					Dict					,a_				:					Tuple=8_2					,a_				:					Union[str, Any]=1_6				)				->	Any:




					'''simple docstring'''







					set_seed(4_2				)
					__magic_name__							=    RegressionModel()
					__magic_name__							=    deepcopy(a_				)
					__magic_name__							=    RegressionDataset(length=a_				)
					__magic_name__							=    DataLoader(a_					,batch_size=a_				)
					model.to(accelerator.device				)
					__magic_name__,	__magic_name__							=    accelerator.prepare(a_					,a_				)
					return model, ddp_model, dataloader




def 					_lowercase     (				a_				:					Accelerator					,a_				:					Any=False				)				->	List[Any]:




					'''simple docstring'''







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

					def tokenize_function(a_				:					Union[str, Any]				):
										__magic_name__							=    tokenizer(examples['sentence1']					,examples['sentence2']					,truncation=a_					,max_length=a_				)
										return outputs

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

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

					def collate_fn(a_				:					str				):
										if use_longest:
															return tokenizer.pad(a_					,padding='longest'					,return_tensors='pt'				)
										return tokenizer.pad(a_					,padding='max_length'					,max_length=1_2_8					,return_tensors='pt'				)

					return DataLoader(a_					,shuffle=a_					,collate_fn=a_					,batch_size=1_6				)




def 					_lowercase     (				a_				:					Optional[int]					,a_				:					List[Any]				)				->	Optional[int]:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Tuple					,a_				:					Optional[Any]					,a_				:					List[str]				)				->	Tuple:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Accelerator					,a_				:					int=8_2					,a_				:					Optional[Any]=False					,a_				:					Dict=False					,a_				:					List[Any]=1_6				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__,	__magic_name__,	__magic_name__							=    get_basic_setup(a_					,a_					,a_				)
					__magic_name__,	__magic_name__							=    generate_predictions(a_					,a_					,a_				)
					assert (
					    len(a_				) == num_samples
					), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(a_				)}'




def 					_lowercase     (				a_				:					bool = False					,a_				:					bool = False				)				->	Union[str, Any]:




					'''simple docstring'''







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

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




					'''simple docstring'''







					__magic_name__							=    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]:
															__magic_name__							=    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_					,9_9				)
															accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print('**Test last batch is not dropped when perfectly divisible**'				)
					__magic_name__							=    Accelerator()
					test_torch_metrics(a_					,5_1_2				)
					accelerator.state._reset_state()




def 					_lowercase     (				a_				:					List[Any]				)				->	Optional[Any]:




					'''simple docstring'''







					main()


if __name__ == "__main__":
						main()







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    'laion/clap-htsat-unfused'
						__magic_name__							=    tempfile.mkdtemp()







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_tokenizer()
						__magic_name__							=    self.get_feature_extractor()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						processor.save_pretrained(self.tmpdirname      )
						__magic_name__							=    ClapProcessor.from_pretrained(self.tmpdirname      )

						self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer.get_vocab()      )
						self.assertIsInstance(processor.tokenizer  , __UpperCamelCase      )

						self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor.to_json_string()      )
						self.assertIsInstance(processor.feature_extractor  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    ClapProcessor(tokenizer=self.get_tokenizer()  , feature_extractor=self.get_feature_extractor()      )
						processor.save_pretrained(self.tmpdirname      )

						__magic_name__							=    self.get_tokenizer(bos_token='(BOS)'  , eos_token='(EOS)'      )
						__magic_name__							=    self.get_feature_extractor(do_normalize=__UpperCamelCase  , padding_value=1.0      )

						__magic_name__							=    ClapProcessor.from_pretrained(
						    self.tmpdirname  , bos_token='(BOS)'  , eos_token='(EOS)'  , do_normalize=__UpperCamelCase  , padding_value=1.0      )

						self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer_add_kwargs.get_vocab()      )
						self.assertIsInstance(processor.tokenizer  , __UpperCamelCase      )

						self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor_add_kwargs.to_json_string()      )
						self.assertIsInstance(processor.feature_extractor  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    floats_list((3, 10_00)      )

						__magic_name__							=    feature_extractor(__UpperCamelCase  , return_tensors='np'      )
						__magic_name__							=    processor(audios=__UpperCamelCase  , return_tensors='np'      )

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()  , input_processor[key].sum()  , delta=1E-2      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    'This is a test string'

						__magic_name__							=    processor(text=__UpperCamelCase      )

						__magic_name__							=    tokenizer(__UpperCamelCase      )

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]  , encoded_processor[key]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__magic_name__							=    processor.batch_decode(__UpperCamelCase      )
						__magic_name__							=    tokenizer.batch_decode(__UpperCamelCase      )

						self.assertListEqual(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						self.assertListEqual(
						    processor.model_input_names[2:]  , feature_extractor.model_input_names  , msg='`processor` and `feature_extractor` model input names do not match'  , )




