

"""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 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= tempfile.mkdtemp()

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

						SCREAMING_SNAKE_CASE__									= GitProcessor(__UpperCAmelCase      ,			__UpperCAmelCase    )

						processor.save_pretrained(self.tmpdirname    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			**__UpperCAmelCase			:  Tuple    )				->      Tuple:
						return AutoProcessor.from_pretrained(self.tmpdirname      ,			**__UpperCAmelCase    ).tokenizer

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			**__UpperCAmelCase			:  str    )				->      str:
						return AutoProcessor.from_pretrained(self.tmpdirname      ,			**__UpperCAmelCase    ).image_processor

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Tuple:
						shutil.rmtree(self.tmpdirname    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= [np.random.randint(2_5_5      ,			size=(3, 3_0, 4_0_0)      ,			dtype=np.uinta    )]

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

						return image_inputs

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=self.get_tokenizer()      ,			image_processor=self.get_image_processor()    )
						processor.save_pretrained(self.tmpdirname    )

						SCREAMING_SNAKE_CASE__									= self.get_tokenizer(bos_token="""(BOS)"""      ,			eos_token="""(EOS)"""    )
						SCREAMING_SNAKE_CASE__									= self.get_image_processor(do_normalize=__UpperCAmelCase      ,			padding_value=1.0    )

						SCREAMING_SNAKE_CASE__									= GitProcessor.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    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__									= image_processor(__UpperCAmelCase      ,			return_tensors="""np"""    )
						SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= "lower newer"

						SCREAMING_SNAKE_CASE__									= processor(text=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= tokenizer(__UpperCAmelCase      ,			return_token_type_ids=__UpperCAmelCase    )

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

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      int:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= "lower newer"
						SCREAMING_SNAKE_CASE__									= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__									= processor(text=__UpperCAmelCase      ,			images=__UpperCAmelCase    )

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

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

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

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

						SCREAMING_SNAKE_CASE__									= processor.batch_decode(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= tokenizer.batch_decode(__UpperCAmelCase    )

						self.assertListEqual(__UpperCAmelCase      ,			__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= self.get_image_processor()
						SCREAMING_SNAKE_CASE__									= self.get_tokenizer()

						SCREAMING_SNAKE_CASE__									= GitProcessor(tokenizer=__UpperCAmelCase      ,			image_processor=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= "lower newer"
						SCREAMING_SNAKE_CASE__									= self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE__									= processor(text=__UpperCAmelCase      ,			images=__UpperCAmelCase    )

						# 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 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if start == end:
						return start if lst[start] > item else start + 1
			if start > end:
						return start

			SCREAMING_SNAKE_CASE__									= (start + end) // 2
			if lst[mid] < item:
						return binary_search(snake_case__						,			snake_case__						,			mid + 1						,			snake_case__      )
			elif lst[mid] > item:
						return binary_search(snake_case__						,			snake_case__						,			snake_case__						,			mid - 1      )
			else:
						return mid

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )

			for index in range(1						,			snake_case__      ):
						SCREAMING_SNAKE_CASE__									= lst[index]
						SCREAMING_SNAKE_CASE__									= binary_search(snake_case__						,			snake_case__						,			0						,			index - 1      )
						SCREAMING_SNAKE_CASE__									= lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

			return lst

def 		A    (       snake_case__						,			snake_case__      ):




			'''simple docstring'''


			if not left:
						return right

			if not right:
						return left

			if left[0] < right[0]:
						return [left[0], *merge(left[1:]						,			snake_case__      )]

			return [right[0], *merge(snake_case__						,			right[1:]      )]

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= len(snake_case__      )
			SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= [], []
			SCREAMING_SNAKE_CASE__									= [lst[0]]
			SCREAMING_SNAKE_CASE__									= []
			SCREAMING_SNAKE_CASE__									= 1
			while i < length:
						if lst[i] < lst[i - 1]:
									runs.append(snake_case__      )
									SCREAMING_SNAKE_CASE__									= [lst[i]]
						else:
									new_run.append(lst[i]      )
						i += 1
			runs.append(snake_case__      )

			for run in runs:
						sorted_runs.append(insertion_sort(snake_case__      )      )
			for run in sorted_runs:
						SCREAMING_SNAKE_CASE__									= merge(snake_case__						,			snake_case__      )

			return sorted_array

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
			SCREAMING_SNAKE_CASE__									= tim_sort(snake_case__      )
			print(snake_case__      )


if __name__ == "__main__":
						main()


