




'''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   _snake_case( unittest.TestCase			):






 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      Union[str, Any]:






    """simple docstring"""


    A__         =    tempfile.mkdtemp()

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

    A__         =    GitProcessor(_lowerCamelCase						,		_lowerCamelCase						)

    processor.save_pretrained(self.tmpdirname						)






 def 	_UpperCamelCase   (self					:     List[str]						,		**a					:     Dict						)					->      Dict:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     str						,		**a					:     Optional[Any]						)					->      Tuple:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      List[Any]:






    """simple docstring"""


    shutil.rmtree(self.tmpdirname						)






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      List[Any]:






    """simple docstring"""


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

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

    return image_inputs






 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      Optional[int]:






    """simple docstring"""


    A__         =    GitProcessor(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=_lowerCamelCase						,		padding_value=1.0						)

    A__         =    GitProcessor.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					:     str						)					->      List[str]:






    """simple docstring"""


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

    A__         =    GitProcessor(tokenizer=_lowerCamelCase						,		image_processor=_lowerCamelCase						)

    A__         =    self.prepare_image_inputs()

    A__         =    image_processor(_lowerCamelCase						,		return_tensors='np'						)
    A__         =    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					:     Optional[Any]						)					->      Any:






    """simple docstring"""


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

    A__         =    GitProcessor(tokenizer=_lowerCamelCase						,		image_processor=_lowerCamelCase						)

    A__         =    """lower newer"""

    A__         =    processor(text=_lowerCamelCase						)

    A__         =    tokenizer(_lowerCamelCase						,		return_token_type_ids=_lowerCamelCase						)

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






 def 	_UpperCamelCase   (self					:     List[str]						)					->      Optional[Any]:






    """simple docstring"""


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

    A__         =    GitProcessor(tokenizer=_lowerCamelCase						,		image_processor=_lowerCamelCase						)

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

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

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

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






 def 	_UpperCamelCase   (self					:     int						)					->      Tuple:






    """simple docstring"""


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

    A__         =    GitProcessor(tokenizer=_lowerCamelCase						,		image_processor=_lowerCamelCase						)

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

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

    self.assertListEqual(_lowerCamelCase						,		_lowerCamelCase						)






 def 	_UpperCamelCase   (self					:     Tuple						)					->      Union[str, Any]:






    """simple docstring"""


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

    A__         =    GitProcessor(tokenizer=_lowerCamelCase						,		image_processor=_lowerCamelCase						)

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

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

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










'''simple docstring'''







from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowerCAmelCase_    	=							TypeVar('''T''', bound='''Enum''')

@unique
class   _snake_case( UpperCAmelCase			):
 __snake_case:       List[Any]									=				80
 __snake_case:       Optional[int]									=				70
 __snake_case:       Optional[int]									=				60
 __snake_case:       int									=				50
 __snake_case:       Dict									=				40
 __snake_case:       Dict									=				30
 __snake_case:       str									=				20
 __snake_case:       Optional[int]									=				10




@unique
class   _snake_case( UpperCAmelCase			):
 __snake_case:       List[Any]									=				24
 __snake_case:       Union[str, Any]									=				21
 __snake_case:       Dict									=				18
 __snake_case:       List[str]									=				15
 __snake_case:       Union[str, Any]									=				12
 __snake_case:       Tuple									=				9
 __snake_case:       int									=				6
 __snake_case:       Tuple									=				3
 __snake_case:       List[Any]									=				2
 __snake_case:       int									=				1
 __snake_case:       List[str]									=				-1
 __snake_case:       Union[str, Any]									=				-2
 __snake_case:       List[str]									=				-3
 __snake_case:       Any									=				-6
 __snake_case:       Tuple									=				-9
 __snake_case:       List[str]									=				-12
 __snake_case:       Dict									=				-15
 __snake_case:       Optional[int]									=				-18
 __snake_case:       str									=				-21
 __snake_case:       Any									=				-24






 @classmethod
 def 	_UpperCamelCase   (cls					:     type[T]						)					->      dict:






    """simple docstring"""


    return {unit.name: unit.value for unit in cls if unit.value > 0}






 @classmethod
 def 	_UpperCamelCase   (cls					:     type[T]						)					->      dict:






    """simple docstring"""


    return {unit.name: unit.value for unit in cls if unit.value < 0}







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
   for name_prefix, value_prefix in prefixes.items():
      A__         =    value / (10**value_prefix)
      if numerical_part > 1:
         return F"""{numerical_part!s} {name_prefix}"""
   return str(UpperCAmelCase	)







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   for prefix in BinaryUnit:
      A__         =    value / (2**prefix.value)
      if numerical_part > 1:
         return F"""{numerical_part!s} {prefix.name}"""
   return str(UpperCAmelCase	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


