
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  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							tempfile.mkdtemp()

					A__  :	Union[str, Any]         =							BlipImageProcessor()
					A__  :	Union[str, Any]         =							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''		)

					A__  :	Any         =							BlipaProcessor(UpperCamelCase__				,       UpperCamelCase__		)

					processor.save_pretrained(self.tmpdirname		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).tokenizer





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).image_processor





	def   __snake_case				(			self		):
					shutil.rmtree(self.tmpdirname		)





	def   __snake_case				(			self		):
					A__  :	int         =							[np.random.randint(255				,       size=(3, 30, 400)				,       dtype=np.uinta		)]

					A__  :	Dict         =							[Image.fromarray(np.moveaxis(UpperCamelCase__				,       0				,       -1		)		) for x in image_inputs]

					return image_inputs





	def   __snake_case				(			self		):
					A__  :	Any         =							BlipaProcessor(tokenizer=self.get_tokenizer()				,       image_processor=self.get_image_processor()		)
					processor.save_pretrained(self.tmpdirname		)

					A__  :	Union[str, Any]         =							self.get_tokenizer(bos_token='''(BOS)'''				,       eos_token='''(EOS)'''		)
					A__  :	Any         =							self.get_image_processor(do_normalize=UpperCamelCase__				,       padding_value=1.0		)

					A__  :	int         =							BlipaProcessor.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.image_processor.to_json_string()				,       image_processor_add_kwargs.to_json_string()		)
					self.assertIsInstance(processor.image_processor				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.get_image_processor()
					A__  :	List[str]         =							self.get_tokenizer()

					A__  :	List[Any]         =							BlipaProcessor(tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__		)

					A__  :	Any         =							self.prepare_image_inputs()

					A__  :	Dict         =							image_processor(UpperCamelCase__				,       return_tensors='''np'''		)
					A__  :	Tuple         =							processor(images=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   __snake_case				(			self		):
					A__  :	str         =							self.get_image_processor()
					A__  :	Tuple         =							self.get_tokenizer()

					A__  :	Any         =							BlipaProcessor(tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__		)

					A__  :	List[Any]         =							'''lower newer'''

					A__  :	Tuple         =							processor(text=UpperCamelCase__		)

					A__  :	Union[str, Any]         =							tokenizer(UpperCamelCase__				,       return_token_type_ids=UpperCamelCase__		)

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





	def   __snake_case				(			self		):
					A__  :	Any         =							self.get_image_processor()
					A__  :	int         =							self.get_tokenizer()

					A__  :	List[str]         =							BlipaProcessor(tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__		)

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

					A__  :	Optional[int]         =							processor(text=UpperCamelCase__				,       images=UpperCamelCase__		)

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

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





	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							self.get_image_processor()
					A__  :	Optional[Any]         =							self.get_tokenizer()

					A__  :	Tuple         =							BlipaProcessor(tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__		)

					A__  :	Optional[int]         =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A__  :	Union[str, Any]         =							processor.batch_decode(UpperCamelCase__		)
					A__  :	Dict         =							tokenizer.batch_decode(UpperCamelCase__		)

					self.assertListEqual(UpperCamelCase__				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.get_image_processor()
					A__  :	List[Any]         =							self.get_tokenizer()

					A__  :	List[Any]         =							BlipaProcessor(tokenizer=UpperCamelCase__				,       image_processor=UpperCamelCase__		)

					A__  :	str         =							'''lower newer'''
					A__  :	List[str]         =							self.prepare_image_inputs()

					A__  :	List[Any]         =							processor(text=UpperCamelCase__				,       images=UpperCamelCase__		)

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

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment







@require_huggingface_suite
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Dict         =							inspect.getfile(accelerate.test_utils		)
					A__  :	Any         =							os.path.sep.join(
					    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

					from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

					A__  :	Tuple         =							test_metrics





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main				,       num_processes=1		)





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main		)





	@require_single_gpu
	def   __snake_case				(			self		):
					self.test_metrics.main()





	@require_multi_gpu
	def   __snake_case				(			self		):
					print(F"Found {torch.cuda.device_count()} devices."		)
					A__  :	int         =							['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
					with patch_environment(omp_num_threads=1		):
									execute_subprocess_async(UpperCamelCase__				,       env=os.environ.copy()		)