






'''simple docstring'''







from __future__ import annotations
def 						A__			(				__lowerCAmelCase		:			int = 4						):
						lowerCamelCase__         =  abs(__lowerCAmelCase						) or 4
						return [[1 + x + y * row_size for x in range(__lowerCAmelCase						)] for y in range(__lowerCAmelCase						)]
def 						A__			(				__lowerCAmelCase		:			list[list[int]]						):
						return reverse_row(transpose(__lowerCAmelCase						)						)
						# OR.. transpose(reverse_column(matrix))
def 						A__			(				__lowerCAmelCase		:			list[list[int]]						):
						return reverse_row(reverse_column(__lowerCAmelCase						)						)
						# OR.. reverse_column(reverse_row(matrix))
def 						A__			(				__lowerCAmelCase		:			list[list[int]]						):
						return reverse_column(transpose(__lowerCAmelCase						)						)
						# OR.. transpose(reverse_row(matrix))
def 						A__			(				__lowerCAmelCase		:			list[list[int]]						):
						lowerCamelCase__         =  [list(__lowerCAmelCase						) for x in zip(*__lowerCAmelCase						)]
						return matrix
def 						A__			(				__lowerCAmelCase		:			list[list[int]]						):
						lowerCamelCase__         =  matrix[::-1]
						return matrix
def 						A__			(				__lowerCAmelCase		:			list[list[int]]						):
						lowerCamelCase__         =  [x[::-1] for x in matrix]
						return matrix
def 						A__			(				__lowerCAmelCase		:			list[list[int]]						):
						for i in matrix:
												print(*__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      Any  			=		make_matrix()
							print('\norigin:\n')
							print_matrix(matrix)
							print('\nrotate 90 counterclockwise:\n')
							print_matrix(rotate_aa(matrix))

							UpperCamelCase    :      int  			=		make_matrix()
							print('\norigin:\n')
							print_matrix(matrix)
							print('\nrotate 180:\n')
							print_matrix(rotate_aaa(matrix))

							UpperCamelCase    :      Dict  			=		make_matrix()
							print('\norigin:\n')
							print_matrix(matrix)
							print('\nrotate 270 counterclockwise:\n')
							print_matrix(rotate_aaa(matrix))









'''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, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  tempfile.mkdtemp()

								lowerCamelCase__         =  BlipImageProcessor()
								lowerCamelCase__         =  BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""       )

								lowerCamelCase__         =  BlipProcessor(_lowerCAmelCase   ,_lowerCAmelCase       )

								processor.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).tokenizer






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).image_processor






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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [np.random.randint(2_55   ,size=(3, 30, 4_00)   ,dtype=np.uinta       )]

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

								return image_inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  BlipProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

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

								lowerCamelCase__         =  BlipProcessor.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 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  image_processor(_lowerCAmelCase   ,return_tensors="""np"""       )
								lowerCamelCase__         =  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 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""

								lowerCamelCase__         =  processor(text=_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer(_lowerCAmelCase   ,return_token_type_ids=_lowerCAmelCase       )

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

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

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

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

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

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

								lowerCamelCase__         =  processor.batch_decode(_lowerCAmelCase       )
								lowerCamelCase__         =  tokenizer.batch_decode(_lowerCAmelCase       )

								self.assertListEqual(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

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

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

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

