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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast





@require_vision
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							BlipImageProcessor()
												SCREAMING_SNAKE_CASE							:    Any							=							BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							BlipProcessor(UpperCamelCase__    ,		UpperCamelCase__						)

												processor.save_pretrained(self.tmpdirname						)


						def        __A       (       self	:				Dict    ,		**UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Union[str, Any]    ,		**UpperCamelCase__	:				List[Any]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												shutil.rmtree(self.tmpdirname						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							[np.random.randint(255    ,		size=(3, 30, 400)    ,		dtype=np.uinta						)]

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

												return image_inputs


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							BlipProcessor(tokenizer=self.get_tokenizer()    ,		image_processor=self.get_image_processor()						)
												processor.save_pretrained(self.tmpdirname						)

												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_tokenizer(bos_token='''(BOS)'''    ,		eos_token='''(EOS)'''						)
												SCREAMING_SNAKE_CASE							:    int							=							self.get_image_processor(do_normalize=UpperCamelCase__    ,		padding_value=1.0						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							BlipProcessor.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        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:    Tuple							=							BlipProcessor(tokenizer=UpperCamelCase__    ,		image_processor=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							image_processor(UpperCamelCase__    ,		return_tensors='''np'''						)
												SCREAMING_SNAKE_CASE							:    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        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:    Any							=							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:    int							=							BlipProcessor(tokenizer=UpperCamelCase__    ,		image_processor=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Any							=							'''lower newer'''

												SCREAMING_SNAKE_CASE							:    Dict							=							processor(text=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer(UpperCamelCase__    ,		return_token_type_ids=UpperCamelCase__						)

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


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:    int							=							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:    Tuple							=							BlipProcessor(tokenizer=UpperCamelCase__    ,		image_processor=UpperCamelCase__						)

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

												SCREAMING_SNAKE_CASE							:    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        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							BlipProcessor(tokenizer=UpperCamelCase__    ,		image_processor=UpperCamelCase__						)

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

												SCREAMING_SNAKE_CASE							:    List[Any]							=							processor.batch_decode(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer.batch_decode(UpperCamelCase__						)

												self.assertListEqual(UpperCamelCase__    ,		UpperCamelCase__						)



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							BlipProcessor(tokenizer=UpperCamelCase__    ,		image_processor=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''lower newer'''
												SCREAMING_SNAKE_CASE							:    Tuple							=							self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE							:    Tuple							=							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''']						)






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
					import accelerate
def       A	(    _lowercase						):
						if not is_accelerate_available():
												return method
						SCREAMING_SNAKE_CASE							:    int							=							version.parse(accelerate.__version__						).base_version
						if version.parse(_lowercase						) < version.parse('''0.17.0'''						):
												return method

						def wrapper(self		,  *_lowercase		,  **_lowercase						):
												if hasattr(self		,  '''_hf_hook'''						) and hasattr(self._hf_hook		,  '''pre_forward'''						):
																		self._hf_hook.pre_forward(self						)
												return method(self		,  *_lowercase		,  **_lowercase						)

						return wrapper





