


'''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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast





@require_vision
class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tempfile.mkdtemp()

											_SCREAMING_SNAKE_CASE			:	Any							=				CLIPImageProcessor()
											_SCREAMING_SNAKE_CASE			:	Tuple							=				BertTokenizer.from_pretrained(
											    """hf-internal-testing/tiny-random-BertModel"""   , model_input_names=["""input_ids""", """attention_mask"""]  )

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				GitProcessor(__SCREAMING_SNAKE_CASE   , __SCREAMING_SNAKE_CASE  )

											processor.save_pretrained(self.tmpdirname  )






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






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






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






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				[np.random.randint(255   , size=(3, 30, 400)   , dtype=np.uinta  )]

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE   , 0   , -1  )  ) for x in image_inputs]

											return image_inputs






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				GitProcessor(tokenizer=self.get_tokenizer()   , image_processor=self.get_image_processor()  )
											processor.save_pretrained(self.tmpdirname  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.get_tokenizer(bos_token="""(BOS)"""   , eos_token="""(EOS)"""  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE   , padding_value=1.0  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				GitProcessor.from_pretrained(
											    self.tmpdirname   , bos_token="""(BOS)"""   , eos_token="""(EOS)"""   , do_normalize=__SCREAMING_SNAKE_CASE   , padding_value=1.0  )

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

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






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.get_image_processor()
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_tokenizer()

											_SCREAMING_SNAKE_CASE			:	Dict							=				GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE   , image_processor=__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	int							=				self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE			:	Any							=				image_processor(__SCREAMING_SNAKE_CASE   , return_tensors="""np"""  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				processor(images=__SCREAMING_SNAKE_CASE   , 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  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_image_processor()
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_tokenizer()

											_SCREAMING_SNAKE_CASE			:	int							=				GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE   , image_processor=__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	int							=				"""lower newer"""

											_SCREAMING_SNAKE_CASE			:	str							=				processor(text=__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer(__SCREAMING_SNAKE_CASE   , return_token_type_ids=__SCREAMING_SNAKE_CASE  )

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






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				self.get_image_processor()
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.get_tokenizer()

											_SCREAMING_SNAKE_CASE			:	Dict							=				GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE   , image_processor=__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	Dict							=				"""lower newer"""
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE			:	Any							=				processor(text=__SCREAMING_SNAKE_CASE   , images=__SCREAMING_SNAKE_CASE  )

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

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






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.get_image_processor()
											_SCREAMING_SNAKE_CASE			:	int							=				self.get_tokenizer()

											_SCREAMING_SNAKE_CASE			:	int							=				GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE   , image_processor=__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				processor.batch_decode(__SCREAMING_SNAKE_CASE  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  )

											self.assertListEqual(__SCREAMING_SNAKE_CASE   , __SCREAMING_SNAKE_CASE  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.get_image_processor()
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.get_tokenizer()

											_SCREAMING_SNAKE_CASE			:	Any							=				GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE   , image_processor=__SCREAMING_SNAKE_CASE  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				"""lower newer"""
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.prepare_image_inputs()

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				processor(text=__SCREAMING_SNAKE_CASE   , images=__SCREAMING_SNAKE_CASE  )

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








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				1
				while b > 0:
								if b & 1:
												_SCREAMING_SNAKE_CASE			:	Tuple							=				((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




