

"""simple docstring"""





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Tuple  				=	tempfile.mkdtemp()

						lowerCAmelCase__	:						List[Any]  				=	BlipImageProcessor()
						lowerCAmelCase__	:						Dict  				=	GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''		)

						lowerCAmelCase__	:						Optional[Any]  				=	BlipaProcessor(lowercase_   ,lowercase_		)

						processor.save_pretrained(self.tmpdirname		)







					def        __lowerCAmelCase (       self			:				Dict   ,**lowercase_			:				Dict		):
						return AutoProcessor.from_pretrained(self.tmpdirname   ,**lowercase_		).tokenizer







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,**lowercase_			:				Tuple		):
						return AutoProcessor.from_pretrained(self.tmpdirname   ,**lowercase_		).image_processor







					def        __lowerCAmelCase (       self			:				List[Any]		):
						shutil.rmtree(self.tmpdirname		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	[np.random.randint(2_5_5   ,size=(3, 3_0, 4_0_0)   ,dtype=np.uinta		)]

						lowerCAmelCase__	:						Any  				=	[Image.fromarray(np.moveaxis(lowercase_   ,0   ,-1		)		) for x in image_inputs]

						return image_inputs







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	BlipaProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						lowerCAmelCase__	:						str  				=	self.get_tokenizer(bos_token='''(BOS)'''   ,eos_token='''(EOS)'''		)
						lowerCAmelCase__	:						Optional[int]  				=	self.get_image_processor(do_normalize=lowercase_   ,padding_value=1.0		)

						lowerCAmelCase__	:						List[str]  				=	BlipaProcessor.from_pretrained(
						    self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=lowercase_   ,padding_value=1.0		)

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

						self.assertEqual(processor.image_processor.to_json_string()   ,image_processor_add_kwargs.to_json_string()		)
						self.assertIsInstance(processor.image_processor   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.get_image_processor()
						lowerCAmelCase__	:						List[Any]  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Tuple  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						int  				=	self.prepare_image_inputs()

						lowerCAmelCase__	:						Dict  				=	image_processor(lowercase_   ,return_tensors='''np'''		)
						lowerCAmelCase__	:						Dict  				=	processor(images=lowercase_   ,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        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.get_image_processor()
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Optional[Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						Any  				=	'''lower newer'''

						lowerCAmelCase__	:						Optional[Any]  				=	processor(text=lowercase_		)

						lowerCAmelCase__	:						Dict  				=	tokenizer(lowercase_   ,return_token_type_ids=lowercase_		)

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







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						int  				=	self.get_image_processor()
						lowerCAmelCase__	:						Optional[Any]  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Optional[Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						Any  				=	'''lower newer'''
						lowerCAmelCase__	:						Optional[int]  				=	self.prepare_image_inputs()

						lowerCAmelCase__	:						List[Any]  				=	processor(text=lowercase_   ,images=lowercase_		)

						self.assertListEqual(list(inputs.keys()		)   ,['''pixel_values''', '''input_ids''', '''attention_mask''']		)

						# test if it raises when no input is passed
						with pytest.raises(lowercase_		):
							processor()







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.get_image_processor()
						lowerCAmelCase__	:						Tuple  				=	self.get_tokenizer()

						lowerCAmelCase__	:						List[Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__	:						Dict  				=	processor.batch_decode(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	tokenizer.batch_decode(lowercase_		)

						self.assertListEqual(lowercase_   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_image_processor()
						lowerCAmelCase__	:						Dict  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Union[str, Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	'''lower newer'''
						lowerCAmelCase__	:						Tuple  				=	self.prepare_image_inputs()

						lowerCAmelCase__	:						List[Any]  				=	processor(text=lowercase_   ,images=lowercase_		)

						# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
						self.assertListEqual(list(inputs.keys()		)   ,['''pixel_values''', '''input_ids''', '''attention_mask''']		)







"""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


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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=16 ):
	set_seed(42 )
	lowerCAmelCase__	:						Union[str, Any]  				=	RegressionModel()
	lowerCAmelCase__	:						Optional[int]  				=	deepcopy(A_ )
	lowerCAmelCase__	:						Any  				=	RegressionDataset(length=A_ )
	lowerCAmelCase__	:						List[str]  				=	DataLoader(A_						,     batch_size=A_ )
	model.to(accelerator.device )
	lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	accelerator.prepare(A_						,     A_ )
	return model, ddp_model, dataloader

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						List[str]  				=	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
	lowerCAmelCase__	:						List[str]  				=	load_dataset('''glue'''						,     '''mrpc'''						,     split='''validation''' )

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

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

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

	def collate_fn(A_ ):
		if use_longest:
			return tokenizer.pad(A_						,     padding='''longest'''						,     return_tensors='''pt''' )
		return tokenizer.pad(A_						,     padding='''max_length'''						,     max_length=1_28						,     return_tensors='''pt''' )

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

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	[]
	for batch in dataloader:
		lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	batch.values()
		with torch.no_grad():
			lowerCAmelCase__	:						List[str]  				=	model(A_ )
			lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	accelerator.gather_for_metrics((logit, target) )
			logits_and_targets.append((logit, target) )
	lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	[], []
	for logit, targ in logits_and_targets:
		logits.append(A_ )
		targs.append(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	torch.cat(A_ ), torch.cat(A_ )
	return logits, targs

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=False						,     A_=False						,     A_=16 ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	get_basic_setup(A_						,     A_						,     A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	generate_predictions(A_						,     A_						,     A_ )
	assert (
	    len(A_ ) == num_samples
	), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_ )}'

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

	# Then do distributed
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	setup['''ddp''']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowerCAmelCase__	:						Union[str, Any]  				=	model(**A_ )
		lowerCAmelCase__	:						int  				=	outputs.logits.argmax(dim=-1 )
		lowerCAmelCase__	:						int  				=	batch['''labels''']
		lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	accelerator.gather_for_metrics((preds, references) )
		metric.add_batch(predictions=A_						,     references=A_ )
	lowerCAmelCase__	:						List[Any]  				=	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   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	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]:
			lowerCAmelCase__	:						Optional[Any]  				=	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_						,     99 )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**''' )
	lowerCAmelCase__	:						List[str]  				=	Accelerator()
	test_torch_metrics(A_						,     5_12 )
	accelerator.state._reset_state()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()




