


'''simple docstring'''

import numpy as np

__lowercase:		Tuple							=     [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]







class  UpperCAmelCase :

 def __init__( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						np.array(a_					)

 def 		lowercase_						( self :	List[str],					a_ :	str					):





    """simple docstring"""



    UpperCamelCase__						,		UpperCamelCase__ 						=						np.where(letter == self.SQUARE					)
    UpperCamelCase__ 						=						np.concatenate([indexa + 1, indexa + 1]					)
    return indexes

 def 		lowercase_						( self :	List[Any],					a_ :	int,					a_ :	int					):





    """simple docstring"""



    return self.SQUARE[indexa - 1, indexa - 1]

 def 		lowercase_						( self :	Union[str, Any],					a_ :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						message.lower()
    UpperCamelCase__ 						=						message.replace("j",					"i"					)

    UpperCamelCase__ 						=						""
    for letter_index in range(len(a_					)					):
       if message[letter_index] != " ":
          UpperCamelCase__ 						=						self.letter_to_numbers(message[letter_index]					)
          UpperCamelCase__ 						=						encoded_message + str(numbers[0]					) + str(numbers[1]					)
       elif message[letter_index] == " ":
          UpperCamelCase__ 						=						encoded_message + " "

    return encoded_message

 def 		lowercase_						( self :	Union[str, Any],					a_ :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						message.replace(" ",					"  "					)
    UpperCamelCase__ 						=						""
    for numbers_index in range(int(len(a_					) / 2					)					):
       if message[numbers_index * 2] != " ":
          UpperCamelCase__ 						=						message[numbers_index * 2]
          UpperCamelCase__ 						=						message[numbers_index * 2 + 1]

          UpperCamelCase__ 						=						self.numbers_to_letter(int(a_					),					int(a_					)					)
          UpperCamelCase__ 						=						decoded_message + letter
       elif message[numbers_index * 2] == " ":
          UpperCamelCase__ 						=						decoded_message + " "

    return decoded_message



'''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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class  UpperCAmelCase (       unittest.TestCase):

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						tempfile.mkdtemp()

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

    UpperCamelCase__ 						=						GitProcessor(a_,					a_					)

    processor.save_pretrained(self.tmpdirname					)

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    shutil.rmtree(self.tmpdirname					)

 def 		lowercase_						( self :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]

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

    return image_inputs

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
    processor.save_pretrained(self.tmpdirname					)

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

    UpperCamelCase__ 						=						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 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
    UpperCamelCase__ 						=						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 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"

    UpperCamelCase__ 						=						processor(text=a_					)

    UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)

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

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

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

    UpperCamelCase__ 						=						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 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

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

    UpperCamelCase__ 						=						processor.batch_decode(a_					)
    UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)

    self.assertListEqual(a_,					a_					)

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





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

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

    UpperCamelCase__ 						=						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"]					)