
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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						tempfile.mkdtemp()

							UpperCamelCase_:							List[Any]	=						BlipImageProcessor()
							UpperCamelCase_:							List[Any]	=						GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""      )

							UpperCamelCase_:							Dict	=						BlipaProcessor(snake_case_						,	snake_case_      )

							processor.save_pretrained(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Dict      ):
							return AutoProcessor.from_pretrained(self.tmpdirname						,	**snake_case_      ).tokenizer

			def       lowerCAmelCase__	(       self :       List[Any]						,	**snake_case_ :       Any      ):
							return AutoProcessor.from_pretrained(self.tmpdirname						,	**snake_case_      ).image_processor

			def       lowerCAmelCase__	(       self :       str      ):
							shutil.rmtree(self.tmpdirname      )

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

							UpperCamelCase_:							List[str]	=						[Image.fromarray(np.moveaxis(snake_case_						,	0						,	-1      )      ) for x in image_inputs]

							return image_inputs

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							int	=						BlipaProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()      )
							processor.save_pretrained(self.tmpdirname      )

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

							UpperCamelCase_:							int	=						BlipaProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

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

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

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[str]	=						self.get_image_processor()
							UpperCamelCase_:							List[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Any	=						BlipaProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							List[str]	=						self.prepare_image_inputs()

							UpperCamelCase_:							Dict	=						image_processor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							Optional[int]	=						processor(images=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       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Any	=						self.get_image_processor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						BlipaProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""lower newer"""

							UpperCamelCase_:							List[str]	=						processor(text=snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						tokenizer(snake_case_						,	return_token_type_ids=snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Tuple	=						self.get_image_processor()
							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer()

							UpperCamelCase_:							List[str]	=						BlipaProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

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

							UpperCamelCase_:							Dict	=						processor(text=snake_case_						,	images=snake_case_      )

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

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

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_image_processor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Union[str, Any]	=						BlipaProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							int	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							List[str]	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							int	=						self.get_image_processor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							str	=						BlipaProcessor(tokenizer=snake_case_						,	image_processor=snake_case_      )

							UpperCamelCase_:							List[Any]	=						"""lower newer"""
							UpperCamelCase_:							Optional[int]	=						self.prepare_image_inputs()

							UpperCamelCase_:							Dict	=						processor(text=snake_case_						,	images=snake_case_      )

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








import random







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = False						)  ->	dict:
				UpperCamelCase_:							dict	=						{i: [] for i in range(lowerCamelCase						)}

				# if probability is greater or equal than 1, then generate a complete graph
				if probability >= 1:
								return complete_graph(lowerCamelCase						)
				# if probability is lower or equal than 0, then return a graph without edges
				if probability <= 0:
								return graph

				# for each couple of nodes, add an edge from u to v
				# if the number randomly generated is greater than probability probability
				for i in range(lowerCamelCase						):
								for j in range(i + 1     ,    lowerCamelCase						):
												if random.random() < probability:
																graph[i].append(lowerCamelCase						)
																if not directed:
																				# if the graph is undirected, add an edge in from j to i, either
																				graph[j].append(lowerCamelCase						)
				return graph







def        A__					(						lowerCamelCase						)  ->	dict:
				return {
				    i: [j for j in range(lowerCamelCase						) if i != j] for i in range(lowerCamelCase						)
				}


if __name__ == "__main__":
							import doctest

							doctest.testmod()






