






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  snake_case__      (							unittest.TestCase   ):
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     List[str]     		=      tempfile.mkdtemp()

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

							__magic_name__    :     Tuple     		=      GitProcessor(a_    ,     a_				)

							processor.save_pretrained(self.tmpdirname				)
	def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   int:
							return AutoProcessor.from_pretrained(self.tmpdirname    ,     **a_				).tokenizer
	def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   Any:
							return AutoProcessor.from_pretrained(self.tmpdirname    ,     **a_				).image_processor
	def     __magic_name__       (       self				)							->   Dict:
							shutil.rmtree(self.tmpdirname				)
	def     __magic_name__       (       self				)							->   List[Any]:
							__magic_name__    :     Optional[Any]     		=      [np.random.randint(2_55    ,     size=(3, 30, 4_00)    ,     dtype=np.uinta				)]

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

							return image_inputs
	def     __magic_name__       (       self				)							->   Optional[Any]:
							__magic_name__    :     Any     		=      GitProcessor(tokenizer=self.get_tokenizer()    ,     image_processor=self.get_image_processor()				)
							processor.save_pretrained(self.tmpdirname				)

							__magic_name__    :     str     		=      self.get_tokenizer(bos_token="""(BOS)"""    ,     eos_token="""(EOS)"""				)
							__magic_name__    :     List[str]     		=      self.get_image_processor(do_normalize=a_    ,     padding_value=1.0				)

							__magic_name__    :     Optional[Any]     		=      GitProcessor.from_pretrained(
							    self.tmpdirname    ,     bos_token="""(BOS)"""    ,     eos_token="""(EOS)"""    ,     do_normalize=a_    ,     padding_value=1.0				)

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

							self.assertEqual(processor.image_processor.to_json_string()    ,     image_processor_add_kwargs.to_json_string()				)
							self.assertIsInstance(processor.image_processor    ,     a_				)
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     int     		=      self.get_image_processor()
							__magic_name__    :     Dict     		=      self.get_tokenizer()

							__magic_name__    :     List[Any]     		=      GitProcessor(tokenizer=a_    ,     image_processor=a_				)

							__magic_name__    :     Optional[int]     		=      self.prepare_image_inputs()

							__magic_name__    :     str     		=      image_processor(a_    ,     return_tensors="""np"""				)
							__magic_name__    :     Optional[int]     		=      processor(images=a_    ,     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     __magic_name__       (       self				)							->   Optional[Any]:
							__magic_name__    :     str     		=      self.get_image_processor()
							__magic_name__    :     Any     		=      self.get_tokenizer()

							__magic_name__    :     Any     		=      GitProcessor(tokenizer=a_    ,     image_processor=a_				)

							__magic_name__    :     Optional[int]     		=      """lower newer"""

							__magic_name__    :     Union[str, Any]     		=      processor(text=a_				)

							__magic_name__    :     Optional[int]     		=      tokenizer(a_    ,     return_token_type_ids=a_				)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]    ,     encoded_processor[key]				)
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__    :     Optional[int]     		=      self.get_image_processor()
							__magic_name__    :     Tuple     		=      self.get_tokenizer()

							__magic_name__    :     str     		=      GitProcessor(tokenizer=a_    ,     image_processor=a_				)

							__magic_name__    :     Optional[int]     		=      """lower newer"""
							__magic_name__    :     List[str]     		=      self.prepare_image_inputs()

							__magic_name__    :     int     		=      processor(text=a_    ,     images=a_				)

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

							# test if it raises when no input is passed
							with pytest.raises(a_				):
													processor()
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     int     		=      self.get_image_processor()
							__magic_name__    :     Any     		=      self.get_tokenizer()

							__magic_name__    :     str     		=      GitProcessor(tokenizer=a_    ,     image_processor=a_				)

							__magic_name__    :     Optional[Any]     		=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							__magic_name__    :     List[str]     		=      processor.batch_decode(a_				)
							__magic_name__    :     Tuple     		=      tokenizer.batch_decode(a_				)

							self.assertListEqual(a_    ,     a_				)







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

							__magic_name__    :     Any     		=      GitProcessor(tokenizer=a_    ,     image_processor=a_				)

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

							__magic_name__    :     List[str]     		=      processor(text=a_    ,     images=a_				)

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








from typing import Literal

__magic_name__:  List[Any]  	=			"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     int     		=      LETTERS if mode == """decrypt""" else key
						__magic_name__    :     Optional[Any]     		=      key if mode == """decrypt""" else LETTERS
						__magic_name__    :     Optional[Any]     		=      """"""
						# loop through each symbol in the message
						for symbol in message:
												if symbol.upper() in chars_a:
																		# encrypt/decrypt the symbol
																		__magic_name__    :     Optional[Any]     		=      chars_a.find(symbol.upper()				)
																		if symbol.isupper():
																								translated += chars_b[sym_index].upper()
																		else:
																								translated += chars_b[sym_index].lower()
												else:
																		# symbol is not in LETTERS, just add it
																		translated += symbol
						return translated

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""encrypt"""				)

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""decrypt"""				)

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     Optional[Any]     		=      """Hello World"""
						__magic_name__    :     str     		=      """QWERTYUIOPASDFGHJKLZXCVBNM"""
						__magic_name__    :     List[str]     		=      """decrypt"""  # set to 'encrypt' or 'decrypt'

						if mode == "encrypt":
												__magic_name__    :     Dict     		=      encrypt_message(_A,					_A				)
						elif mode == "decrypt":
												__magic_name__    :     Any     		=      decrypt_message(_A,					_A				)
						print(f'Using the key {key}, the {mode}ed message is: {translated}'				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()
