


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 							a						(unittest.TestCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Any							)      ->       Union[str, Any]:
														__snake_case						:     Dict									=  tempfile.mkdtemp()

														__snake_case						:     Union[str, Any]									=  BlipImageProcessor()
														__snake_case						:     Union[str, Any]									=  GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"							)

														__snake_case						:     Optional[int]									=  BlipaProcessor(lowerCamelCase       ,      lowerCamelCase							)

														processor.save_pretrained(self.tmpdirname							)

							def        __snake_case   (				self			:    Optional[Any]       ,      **lowerCamelCase			:    Optional[Any]							)      ->       str:
														return AutoProcessor.from_pretrained(self.tmpdirname       ,      **lowerCamelCase							).tokenizer

							def        __snake_case   (				self			:    str       ,      **lowerCamelCase			:    List[str]							)      ->       str:
														return AutoProcessor.from_pretrained(self.tmpdirname       ,      **lowerCamelCase							).image_processor

							def        __snake_case   (				self			:    Dict							)      ->       Tuple:
														shutil.rmtree(self.tmpdirname							)

							def        __snake_case   (				self			:    Dict							)      ->       int:
														__snake_case						:     Union[str, Any]									=  [np.random.randint(255       ,      size=(3, 30, 400)       ,      dtype=np.uinta							)]

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

														return image_inputs

							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														__snake_case						:     Optional[int]									=  BlipaProcessor(tokenizer=self.get_tokenizer()       ,      image_processor=self.get_image_processor()							)
														processor.save_pretrained(self.tmpdirname							)

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

														__snake_case						:     List[Any]									=  BlipaProcessor.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        __snake_case   (				self			:    int							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.get_image_processor()
														__snake_case						:     Optional[int]									=  self.get_tokenizer()

														__snake_case						:     Dict									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

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

														__snake_case						:     Any									=  image_processor(lowerCamelCase       ,      return_tensors="np"							)
														__snake_case						:     List[str]									=  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        __snake_case   (				self			:    Union[str, Any]							)      ->       List[str]:
														__snake_case						:     Any									=  self.get_image_processor()
														__snake_case						:     int									=  self.get_tokenizer()

														__snake_case						:     List[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  "lower newer"

														__snake_case						:     Union[str, Any]									=  processor(text=lowerCamelCase							)

														__snake_case						:     Tuple									=  tokenizer(lowerCamelCase       ,      return_token_type_ids=lowerCamelCase							)

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

							def        __snake_case   (				self			:    Any							)      ->       Optional[Any]:
														__snake_case						:     Dict									=  self.get_image_processor()
														__snake_case						:     Union[str, Any]									=  self.get_tokenizer()

														__snake_case						:     Tuple									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

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

														__snake_case						:     Optional[int]									=  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        __snake_case   (				self			:    Dict							)      ->       Any:
														__snake_case						:     List[Any]									=  self.get_image_processor()
														__snake_case						:     str									=  self.get_tokenizer()

														__snake_case						:     Optional[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

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

														__snake_case						:     Union[str, Any]									=  processor.batch_decode(lowerCamelCase							)
														__snake_case						:     List[str]									=  tokenizer.batch_decode(lowerCamelCase							)

														self.assertListEqual(lowerCamelCase       ,      lowerCamelCase							)






							def        __snake_case   (				self			:    int							)      ->       Optional[Any]:
														__snake_case						:     Any									=  self.get_image_processor()
														__snake_case						:     Any									=  self.get_tokenizer()

														__snake_case						:     List[Any]									=  BlipaProcessor(tokenizer=lowerCamelCase       ,      image_processor=lowerCamelCase							)

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

														__snake_case						:     int									=  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"]							)








from __future__ import annotations




def  __UpperCAmelCase(   lowercase_			,     lowercase_ = None			,     lowercase_ = None			,     lowercase_ = False			,     ):
       _lowerCamelCase				:  Tuple						=		cipher_alphabet or [chr(lowercase_   ) for i in range(97			,     1_23   )]

       # If the argument is None or the user provided an empty dictionary
       if not frequencies_dict:
              # Frequencies of letters in the english language (how much they show up)
              _lowerCamelCase				:  Dict						=		{
                  '''a''': 0.0_8_4_9_7,
                  '''b''': 0.0_1_4_9_2,
                  '''c''': 0.0_2_2_0_2,
                  '''d''': 0.0_4_2_5_3,
                  '''e''': 0.1_1_1_6_2,
                  '''f''': 0.0_2_2_2_8,
                  '''g''': 0.0_2_0_1_5,
                  '''h''': 0.0_6_0_9_4,
                  '''i''': 0.0_7_5_4_6,
                  '''j''': 0.0_0_1_5_3,
                  '''k''': 0.0_1_2_9_2,
                  '''l''': 0.0_4_0_2_5,
                  '''m''': 0.0_2_4_0_6,
                  '''n''': 0.0_6_7_4_9,
                  '''o''': 0.0_7_5_0_7,
                  '''p''': 0.0_1_9_2_9,
                  '''q''': 0.0_0_0_9_5,
                  '''r''': 0.0_7_5_8_7,
                  '''s''': 0.0_6_3_2_7,
                  '''t''': 0.0_9_3_5_6,
                  '''u''': 0.0_2_7_5_8,
                  '''v''': 0.0_0_9_7_8,
                  '''w''': 0.0_2_5_6_0,
                  '''x''': 0.0_0_1_5_0,
                  '''y''': 0.0_1_9_9_4,
                  '''z''': 0.0_0_0_7_7,
              }
       else:
              # Custom frequencies dictionary
              _lowerCamelCase				:  Tuple						=		frequencies_dict

       if not case_sensitive:
              _lowerCamelCase				:  Optional[int]						=		ciphertext.lower()

       # Chi squared statistic values
       _lowerCamelCase				:  dict[int, tuple[float, str]]						=		{}

       # cycle through all of the shifts
       for shift in range(len(lowercase_   )   ):
              _lowerCamelCase				:  Any						=		''''''

              # decrypt the message with the shift
              for letter in ciphertext:
                     try:
                            # Try to index the letter in the alphabet
                            _lowerCamelCase				:  int						=		(alphabet_letters.index(letter.lower()   ) - shift) % len(
                                lowercase_   )
                            decrypted_with_shift += (
                                alphabet_letters[new_key].upper()
                                if case_sensitive and letter.isupper()
                                else alphabet_letters[new_key]
                            )
                     except ValueError:
                            # Append the character if it isn't in the alphabet
                            decrypted_with_shift += letter

              _lowerCamelCase				:  Optional[Any]						=		0.0

              # Loop through each letter in the decoded message with the shift
              for letter in decrypted_with_shift:
                     if case_sensitive:
                            _lowerCamelCase				:  Any						=		letter.lower()
                            if letter in frequencies:
                                   # Get the amount of times the letter occurs in the message
                                   _lowerCamelCase				:  List[str]						=		decrypted_with_shift.lower().count(lowercase_   )

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   _lowerCamelCase				:  Optional[Any]						=		frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   _lowerCamelCase				:  int						=		((occurrences - expected) ** 2) / expected

                                   # Add the margin of error to the total chi squared statistic
                                   chi_squared_statistic += chi_letter_value
                     else:
                            if letter.lower() in frequencies:
                                   # Get the amount of times the letter occurs in the message
                                   _lowerCamelCase				:  Union[str, Any]						=		decrypted_with_shift.count(lowercase_   )

                                   # Get the excepcted amount of times the letter should appear based
                                   # on letter frequencies
                                   _lowerCamelCase				:  List[str]						=		frequencies[letter] * occurrences

                                   # Complete the chi squared statistic formula
                                   _lowerCamelCase				:  str						=		((occurrences - expected) ** 2) / expected

                                   # Add the margin of error to the total chi squared statistic
                                   chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
              _lowerCamelCase				:  Tuple						=		(
                  chi_squared_statistic,
                  decrypted_with_shift,
              )

       # Get the most likely cipher by finding the cipher with the smallest chi squared
       # statistic
       def chi_squared_statistic_values_sorting_key(lowercase_   ) -> tuple[float, str]:
              return chi_squared_statistic_values[key]

       _lowerCamelCase				:  int						=		min(
           lowercase_			,     key=lowercase_			,     )

       # Get all the data from the most likely cipher (key, decoded message)
       (
           (
           _lowerCamelCase
       ),	(
           _lowerCamelCase
       ),	
       )				:  Optional[int]						=		chi_squared_statistic_values[most_likely_cipher]

       # Return the data on the most likely shift
       return (
           most_likely_cipher,
           most_likely_cipher_chi_squared_value,
           decoded_most_likely_cipher,
       )

