
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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class 							lowercase   (						unittest.TestCase  ):



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	tempfile.mkdtemp()

				UpperCamelCase            =	CLIPImageProcessor()
				UpperCamelCase            =	BertTokenizer.from_pretrained(
				    'hf-internal-testing/tiny-random-BertModel'							, model_input_names=['input_ids', 'attention_mask']			)

				UpperCamelCase            =	GitProcessor(A_							, A_			)

				processor.save_pretrained(self.tmpdirname			)



		def __UpperCamelCase					(					self							, **A_			)    ->   int:
				"""simple docstring"""
				return AutoProcessor.from_pretrained(self.tmpdirname							, **A_			).tokenizer



		def __UpperCamelCase					(					self							, **A_			)    ->   List[str]:
				"""simple docstring"""
				return AutoProcessor.from_pretrained(self.tmpdirname							, **A_			).image_processor



		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname			)



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	[np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta			)]

				UpperCamelCase            =	[Image.fromarray(np.moveaxis(A_							, 0							, -1			)			) for x in image_inputs]

				return image_inputs



		def __UpperCamelCase					(					self			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	GitProcessor(tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()			)
				processor.save_pretrained(self.tmpdirname			)

				UpperCamelCase            =	self.get_tokenizer(bos_token='(BOS)'							, eos_token='(EOS)'			)
				UpperCamelCase            =	self.get_image_processor(do_normalize=A_							, padding_value=1.0			)

				UpperCamelCase            =	GitProcessor.from_pretrained(
				    self.tmpdirname							, bos_token='(BOS)'							, eos_token='(EOS)'							, do_normalize=A_							, padding_value=1.0			)

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

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



		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=A_							, image_processor=A_			)

				UpperCamelCase            =	self.prepare_image_inputs()

				UpperCamelCase            =	image_processor(A_							, return_tensors='np'			)
				UpperCamelCase            =	processor(images=A_							, 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 __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=A_							, image_processor=A_			)

				UpperCamelCase            =	'lower newer'

				UpperCamelCase            =	processor(text=A_			)

				UpperCamelCase            =	tokenizer(A_							, return_token_type_ids=A_			)

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



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=A_							, image_processor=A_			)

				UpperCamelCase            =	'lower newer'
				UpperCamelCase            =	self.prepare_image_inputs()

				UpperCamelCase            =	processor(text=A_							, images=A_			)

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

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



		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=A_							, image_processor=A_			)

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

				UpperCamelCase            =	processor.batch_decode(A_			)
				UpperCamelCase            =	tokenizer.batch_decode(A_			)

				self.assertListEqual(A_							, A_			)



		def __UpperCamelCase					(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.get_image_processor()
				UpperCamelCase            =	self.get_tokenizer()

				UpperCamelCase            =	GitProcessor(tokenizer=A_							, image_processor=A_			)

				UpperCamelCase            =	'lower newer'
				UpperCamelCase            =	self.prepare_image_inputs()

				UpperCamelCase            =	processor(text=A_							, images=A_			)

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


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
