



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 CLIPImageProcessor, CLIPProcessor


@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     tempfile.mkdtemp()

								# fmt: off
								A__	  =     ['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__	  =     dict(zip(_snake_case					,       range(len(_snake_case       )       )       )       )
								A__	  =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
								A__	  =     {'unk_token': '<unk>'}

								A__	  =     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['vocab_file']       )
								A__	  =     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(_snake_case       ) + '\n'       )
								with open(self.merges_file					,       'w'					,       encoding='utf-8'       ) as fp:
											fp.write('\n'.join(_snake_case       )       )

								A__	  =     {
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 18,
								    'do_normalize': True,
								    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
								    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
								}
								A__	  =     os.path.join(self.tmpdirname					,       _snake_case       )
								with open(self.image_processor_file					,       'w'					,       encoding='utf-8'       ) as fp:
											json.dump(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Optional[int]					,       **_snake_case       :							str       ):






								"""simple docstring"""


								return CLIPTokenizer.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							Union[str, Any]					,       **_snake_case       :							str       ):






								"""simple docstring"""


								return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							int					,       **_snake_case       :							List[str]       ):






								"""simple docstring"""


								return CLIPImageProcessor.from_pretrained(self.tmpdirname					,       **_snake_case       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


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

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

								return image_inputs



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     self.get_tokenizer()
								A__	  =     self.get_rust_tokenizer()
								A__	  =     self.get_image_processor()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_slow.save_pretrained(self.tmpdirname       )
								A__	  =     CLIPProcessor.from_pretrained(self.tmpdirname					,       use_fast=_snake_case       )

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_fast.save_pretrained(self.tmpdirname       )
								A__	  =     CLIPProcessor.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					,       _snake_case       )
								self.assertIsInstance(processor_fast.tokenizer					,       _snake_case       )

								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					,       _snake_case       )
								self.assertIsInstance(processor_fast.image_processor					,       _snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     CLIPProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								A__	  =     self.get_tokenizer(bos_token='(BOS)'					,       eos_token='(EOS)'       )
								A__	  =     self.get_image_processor(do_normalize=_snake_case					,       padding_value=1.0       )

								A__	  =     CLIPProcessor.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      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     self.prepare_image_inputs()

								A__	  =     image_processor(_snake_case					,       return_tensors='np'       )
								A__	  =     processor(images=_snake_case					,       return_tensors='np'       )

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()					,       input_processor[key].sum()					,       delta=1E-2       )



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'

								A__	  =     processor(text=_snake_case       )

								A__	  =     tokenizer(_snake_case       )

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



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(text=_snake_case					,       images=_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(_snake_case       ):
											processor()



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

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

								A__	  =     processor.batch_decode(_snake_case       )
								A__	  =     tokenizer.batch_decode(_snake_case       )

								self.assertListEqual(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(text=_snake_case					,       images=_snake_case       )

								self.assertListEqual(list(inputs.keys()       )					,       processor.model_input_names       )









from __future__ import annotations


def A    (							__UpperCamelCase = 4       )    ->   list[list[int]]:
			A__	  =     abs(__UpperCamelCase       ) or 4
			return [[1 + x + y * row_size for x in range(__UpperCamelCase       )] for y in range(__UpperCamelCase       )]


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_column(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(reverse_column(__UpperCamelCase       )       )
			# OR.. reverse_column(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_column(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [list(__UpperCamelCase       ) for x in zip(*__UpperCamelCase       )]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     matrix[::-1]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [x[::-1] for x in matrix]
			return matrix


def A    (							__UpperCamelCase       )    ->   None:
			for i in matrix:
						print(*__UpperCamelCase       )


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

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

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




