

'''simple docstring'''



__snake_case			:     List[Any]										=      {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__snake_case			:     List[str]										=      ['a', 'b', 'c', 'd', 'e']
def 						__lowerCamelCase	(       __snake_case					:		Union[str, Any],		__snake_case					:		Optional[int],		__snake_case					:		List[Any]	)	->							Optional[int]:






						"""simple docstring"""





						A__					:       List[Any]     =start
						# add current to visited
						visited.append(__snake_case	)
						A__					:       Any     =edges[current]
						for neighbor in neighbors:
												# if neighbor not in visited, visit
												if neighbor not in visited:
																		A__					:       List[str]     =topological_sort(__snake_case,		__snake_case,		__snake_case	)
    # if all neighbors visited add current to sort
						sort.append(__snake_case	)
						# if all vertices haven't been visited select a new one to visit
						if len(__snake_case	) != len(__snake_case	):
												for vertice in vertices:
																		if vertice not in visited:
																								A__					:       str     =topological_sort(__snake_case,		__snake_case,		__snake_case	)
    # return sort
						return sort


if __name__ == "__main__":
		__snake_case			:     Tuple										=      topological_sort('a', [], [])
		print(sort)





'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[int]			)      ->		int:





									'''simple docstring'''
									A__					:       int     =tempfile.mkdtemp()

									# fmt: off
									A__					:       Optional[int]     =["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
									# fmt: on
									A__					:       List[Any]     =dict(zip(lowerCAmelCase_		,      range(len(lowerCAmelCase_			)			)			)			)
									A__					:       Tuple     =["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
									A__					:       int     ={"""unk_token""": """<unk>"""}

									A__					:       Optional[Any]     =os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]			)
									A__					:       int     =os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""merges_file"""]			)
									with open(self.vocab_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															fp.write(json.dumps(lowerCAmelCase_			) + """\n"""			)
									with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															fp.write("""\n""".join(lowerCAmelCase_			)			)

									A__					:       Dict     ={
									    """do_resize""": True,
									    """size""": 20,
									    """do_center_crop""": True,
									    """crop_size""": 18,
									    """do_normalize""": True,
									    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
									    """image_std""": [0.26862954, 0.26130258, 0.27577711],
									}
									A__					:       Dict     =os.path.join(self.tmpdirname		,      lowerCAmelCase_			)
									with open(self.image_processor_file		,      """w"""		,      encoding="""utf-8"""			) as fp:
															json.dump(lowerCAmelCase_		,      lowerCAmelCase_			)


			def lowercase__						(				self	:						Optional[Any]		,      **lowerCAmelCase_	:						Union[str, Any]			)      ->		int:





									'''simple docstring'''
									return CLIPTokenizer.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Optional[Any]		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]		,      **lowerCAmelCase_	:						str			)      ->		Union[str, Any]:





									'''simple docstring'''
									return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						Dict			)      ->		str:





									'''simple docstring'''
									shutil.rmtree(self.tmpdirname			)


			def lowercase__						(				self	:						List[str]			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Dict     =[np.random.randint(2_55		,      size=(3, 30, 4_00)		,      dtype=np.uinta			)]

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

									return image_inputs


			def lowercase__						(				self	:						Dict			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       int     =self.get_tokenizer()
									A__					:       Optional[int]     =self.get_rust_tokenizer()
									A__					:       Any     =self.get_image_processor()

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)
									processor_slow.save_pretrained(self.tmpdirname			)
									A__					:       Dict     =CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase_			)

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)
									processor_fast.save_pretrained(self.tmpdirname			)
									A__					:       Optional[int]     =CLIPSegProcessor.from_pretrained(self.tmpdirname			)

									self.assertEqual(processor_slow.tokenizer.get_vocab()		,      tokenizer_slow.get_vocab()			)
									self.assertEqual(processor_fast.tokenizer.get_vocab()		,      tokenizer_fast.get_vocab()			)
									self.assertEqual(tokenizer_slow.get_vocab()		,      tokenizer_fast.get_vocab()			)
									self.assertIsInstance(processor_slow.tokenizer		,      lowerCAmelCase_			)
									self.assertIsInstance(processor_fast.tokenizer		,      lowerCAmelCase_			)

									self.assertEqual(processor_slow.image_processor.to_json_string()		,      image_processor.to_json_string()			)
									self.assertEqual(processor_fast.image_processor.to_json_string()		,      image_processor.to_json_string()			)
									self.assertIsInstance(processor_slow.image_processor		,      lowerCAmelCase_			)
									self.assertIsInstance(processor_fast.image_processor		,      lowerCAmelCase_			)


			def lowercase__						(				self	:						str			)      ->		List[str]:





									'''simple docstring'''
									A__					:       List[str]     =CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()			)
									processor.save_pretrained(self.tmpdirname			)

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

									A__					:       Optional[int]     =CLIPSegProcessor.from_pretrained(
									    self.tmpdirname		,      bos_token="""(BOS)"""		,      eos_token="""(EOS)"""		,      do_normalize=lowerCAmelCase_		,      padding_value=1.0			)

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

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


			def lowercase__						(				self	:						str			)      ->		str:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.get_image_processor()
									A__					:       int     =self.get_tokenizer()

									A__					:       Optional[int]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Dict     =self.prepare_image_inputs()

									A__					:       List[Any]     =image_processor(lowerCAmelCase_		,      return_tensors="""np"""			)
									A__					:       List[Any]     =processor(images=lowerCAmelCase_		,      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 lowercase__						(				self	:						Optional[int]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Any     =self.get_image_processor()
									A__					:       Optional[Any]     =self.get_tokenizer()

									A__					:       int     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Any     ="""lower newer"""

									A__					:       Optional[int]     =processor(text=lowerCAmelCase_			)

									A__					:       Optional[int]     =tokenizer(lowerCAmelCase_			)

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


			def lowercase__						(				self	:						List[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =self.get_image_processor()
									A__					:       Optional[Any]     =self.get_tokenizer()

									A__					:       Union[str, Any]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Optional[Any]     ="""lower newer"""
									A__					:       List[str]     =self.prepare_image_inputs()

									A__					:       Optional[Any]     =processor(text=lowerCAmelCase_		,      images=lowerCAmelCase_			)

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

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


			def lowercase__						(				self	:						Any			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.get_image_processor()
									A__					:       List[str]     =self.get_tokenizer()

									A__					:       Optional[int]     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       Tuple     =self.prepare_image_inputs()
									A__					:       str     =self.prepare_image_inputs()

									A__					:       int     =processor(images=lowerCAmelCase_		,      visual_prompt=lowerCAmelCase_			)

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

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



			def lowercase__						(				self	:						Optional[Any]			)      ->		str:





									'''simple docstring'''
									A__					:       List[str]     =self.get_image_processor()
									A__					:       Optional[int]     =self.get_tokenizer()

									A__					:       Any     =CLIPSegProcessor(tokenizer=lowerCAmelCase_		,      image_processor=lowerCAmelCase_			)

									A__					:       List[Any]     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A__					:       Tuple     =processor.batch_decode(lowerCAmelCase_			)
									A__					:       List[Any]     =tokenizer.batch_decode(lowerCAmelCase_			)

									self.assertListEqual(lowerCAmelCase_		,      lowerCAmelCase_			)

