
'''simple docstring'''



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__					:						Tuple											=     logging.get_logger(__name__)


UpperCamelCase__					:						Dict											=     {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__					:						List[str]											=     {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

UpperCamelCase__					:						int											=     {
    "RUCAIBox/mvp": 1_024,
}
@lru_cache()
def       lowerCAmelCase_		( )   ->   int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = (
       list(range(ord("""!"""  )			,					ord("""~"""  ) + 1  )  ) + list(range(ord("""¡"""  )			,					ord("""¬"""  ) + 1  )  ) + list(range(ord("""®"""  )			,					ord("""ÿ"""  ) + 1  )  )
   )
   _SCREAMING_SNAKE_CASE          = bs[:]
   _SCREAMING_SNAKE_CASE          = 0
   for b in range(2**8  ):
      if b not in bs:
         bs.append(__lowerCamelCase  )
         cs.append(2**8 + n  )
         n += 1
   _SCREAMING_SNAKE_CASE          = [chr(__lowerCamelCase  ) for n in cs]
   return dict(zip(__lowerCamelCase			,					__lowerCamelCase  )  )
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = set()
   _SCREAMING_SNAKE_CASE          = word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)  )
      _SCREAMING_SNAKE_CASE          = char
   return pairs







class       _a (__lowercase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE					=     PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE					=     ['input_ids', 'attention_mask']






       def __init__(							self				,							A__				,							A__				,							A__="replace"				,							A__="<s>"				,							A__="</s>"				,							A__="</s>"				,							A__="<s>"				,							A__="<unk>"				,							A__="<pad>"				,							A__="<mask>"				,							A__=False				,							**A__				,							)			->	List[Any]:
          _SCREAMING_SNAKE_CASE          = AddedToken(__lowerCamelCase				,							lstrip=__lowerCamelCase				,							rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase				,							__lowerCamelCase					) else bos_token
          _SCREAMING_SNAKE_CASE          = AddedToken(__lowerCamelCase				,							lstrip=__lowerCamelCase				,							rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase				,							__lowerCamelCase					) else eos_token
          _SCREAMING_SNAKE_CASE          = AddedToken(__lowerCamelCase				,							lstrip=__lowerCamelCase				,							rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase				,							__lowerCamelCase					) else sep_token
          _SCREAMING_SNAKE_CASE          = AddedToken(__lowerCamelCase				,							lstrip=__lowerCamelCase				,							rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase				,							__lowerCamelCase					) else cls_token
          _SCREAMING_SNAKE_CASE          = AddedToken(__lowerCamelCase				,							lstrip=__lowerCamelCase				,							rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase				,							__lowerCamelCase					) else unk_token
          _SCREAMING_SNAKE_CASE          = AddedToken(__lowerCamelCase				,							lstrip=__lowerCamelCase				,							rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase				,							__lowerCamelCase					) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          _SCREAMING_SNAKE_CASE          = AddedToken(__lowerCamelCase				,							lstrip=__lowerCamelCase				,							rstrip=__lowerCamelCase					) if isinstance(__lowerCamelCase				,							__lowerCamelCase					) else mask_token

          super().__init__(
              errors=__lowerCamelCase				,							bos_token=__lowerCamelCase				,							eos_token=__lowerCamelCase				,							unk_token=__lowerCamelCase				,							sep_token=__lowerCamelCase				,							cls_token=__lowerCamelCase				,							pad_token=__lowerCamelCase				,							mask_token=__lowerCamelCase				,							add_prefix_space=__lowerCamelCase				,							**__lowerCamelCase				,							)
          with open(__lowerCamelCase				,							encoding="""utf-8"""					) as vocab_handle:
             _SCREAMING_SNAKE_CASE          = json.load(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE          = {v: k for k, v in self.encoder.items()}
          _SCREAMING_SNAKE_CASE          = errors  # how to handle errors in decoding
          _SCREAMING_SNAKE_CASE          = bytes_to_unicode()
          _SCREAMING_SNAKE_CASE          = {v: k for k, v in self.byte_encoder.items()}
          with open(__lowerCamelCase				,							encoding="""utf-8"""					) as merges_handle:
             _SCREAMING_SNAKE_CASE          = merges_handle.read().split("""\n"""					)[1:-1]
          _SCREAMING_SNAKE_CASE          = [tuple(merge.split()					) for merge in bpe_merges]
          _SCREAMING_SNAKE_CASE          = dict(zip(__lowerCamelCase				,							range(len(__lowerCamelCase					)					)					)					)
          _SCREAMING_SNAKE_CASE          = {}
          _SCREAMING_SNAKE_CASE          = add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          _SCREAMING_SNAKE_CASE          = re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""					)






       @property
       def 				UpperCamelCase (							self					)			->	List[Any]:
          return len(self.encoder					)






       def 				UpperCamelCase (							self					)			->	int:
          return dict(self.encoder				,							**self.added_tokens_encoder					)






       def 				UpperCamelCase (							self				,							A__					)			->	Optional[Any]:
          if token in self.cache:
             return self.cache[token]
          _SCREAMING_SNAKE_CASE          = tuple(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE          = get_pairs(__lowerCamelCase					)

          if not pairs:
             return token

          while True:
             _SCREAMING_SNAKE_CASE          = min(__lowerCamelCase				,							key=lambda A__					: self.bpe_ranks.get(__lowerCamelCase				,							float("""inf"""					)					)					)
             if bigram not in self.bpe_ranks:
                break
             _SCREAMING_SNAKE_CASE          = bigram
             _SCREAMING_SNAKE_CASE          = []
             _SCREAMING_SNAKE_CASE          = 0
             while i < len(__lowerCamelCase					):
                try:
                   _SCREAMING_SNAKE_CASE          = word.index(__lowerCamelCase				,							__lowerCamelCase					)
                except ValueError:
                   new_word.extend(word[i:]					)
                   break
                else:
                   new_word.extend(word[i:j]					)
                   _SCREAMING_SNAKE_CASE          = j

                if word[i] == first and i < len(__lowerCamelCase					) - 1 and word[i + 1] == second:
                   new_word.append(first + second					)
                   i += 2
                else:
                   new_word.append(word[i]					)
                   i += 1
             _SCREAMING_SNAKE_CASE          = tuple(__lowerCamelCase					)
             _SCREAMING_SNAKE_CASE          = new_word
             if len(__lowerCamelCase					) == 1:
                break
             else:
                _SCREAMING_SNAKE_CASE          = get_pairs(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE          = " ".join(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE          = word
          return word






       def 				UpperCamelCase (							self				,							A__					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE          = []
          for token in re.findall(self.pat				,							__lowerCamelCase					):
             _SCREAMING_SNAKE_CASE          = "".join(
                 self.byte_encoder[b] for b in token.encode("""utf-8"""					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase					).split(""" """					)					)
          return bpe_tokens






       def 				UpperCamelCase (							self				,							A__					)			->	List[Any]:
          return self.encoder.get(__lowerCamelCase				,							self.encoder.get(self.unk_token					)					)






       def 				UpperCamelCase (							self				,							A__					)			->	Tuple:
          return self.decoder.get(__lowerCamelCase					)






       def 				UpperCamelCase (							self				,							A__					)			->	Optional[int]:
          _SCREAMING_SNAKE_CASE          = "".join(__lowerCamelCase					)
          _SCREAMING_SNAKE_CASE          = bytearray([self.byte_decoder[c] for c in text]					).decode("""utf-8"""				,							errors=self.errors					)
          return text






       def 				UpperCamelCase (							self				,							A__				,							A__ = None					)			->	Tuple[str]:
          if not os.path.isdir(__lowerCamelCase					):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
             return
          _SCREAMING_SNAKE_CASE          = os.path.join(
              __lowerCamelCase				,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
          _SCREAMING_SNAKE_CASE          = os.path.join(
              __lowerCamelCase				,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]					)

          with open(__lowerCamelCase				,							"""w"""				,							encoding="""utf-8"""					) as f:
             f.write(json.dumps(self.encoder				,							indent=2				,							sort_keys=__lowerCamelCase				,							ensure_ascii=__lowerCamelCase					) + """\n"""					)

          _SCREAMING_SNAKE_CASE          = 0
          with open(__lowerCamelCase				,							"""w"""				,							encoding="""utf-8"""					) as writer:
             writer.write("""#version: 0.2\n"""					)
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,							key=lambda A__					: kv[1]					):
                if index != token_index:
                   logger.warning(
                       F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                       """ Please check that the tokenizer is not corrupted!"""					)
                   _SCREAMING_SNAKE_CASE          = token_index
                writer.write(""" """.join(__lowerCamelCase					) + """\n"""					)
                index += 1

          return vocab_file, merge_file






       def 				UpperCamelCase (							self				,							A__				,							A__ = None					)			->	List[int]:
          if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          _SCREAMING_SNAKE_CASE          = [self.cls_token_id]
          _SCREAMING_SNAKE_CASE          = [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 				UpperCamelCase (							self				,							A__				,							A__ = None				,							A__ = False					)			->	List[int]:
          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__lowerCamelCase				,							token_ids_a=__lowerCamelCase				,							already_has_special_tokens=__lowerCamelCase					)

          if token_ids_a is None:
             return [1] + ([0] * len(__lowerCamelCase					)) + [1]
          return [1] + ([0] * len(__lowerCamelCase					)) + [1, 1] + ([0] * len(__lowerCamelCase					)) + [1]






       def 				UpperCamelCase (							self				,							A__				,							A__ = None					)			->	List[int]:
          _SCREAMING_SNAKE_CASE          = [self.sep_token_id]
          _SCREAMING_SNAKE_CASE          = [self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep					) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






       def 				UpperCamelCase (							self				,							A__				,							A__=False				,							**A__					)			->	str:
          _SCREAMING_SNAKE_CASE          = kwargs.pop("""add_prefix_space"""				,							self.add_prefix_space					)
          if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase					) > 0 and not text[0].isspace()):
             _SCREAMING_SNAKE_CASE          = " " + text
          return (text, kwargs)













import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import CLIPImageProcessor





class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase=7			,  __lowerCamelCase=3			,  __lowerCamelCase=1_8			,  __lowerCamelCase=3_0			,  __lowerCamelCase=4_0_0			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  __lowerCamelCase=True			,  __lowerCamelCase=[0.4814_5466, 0.457_8275, 0.4082_1073]			,  __lowerCamelCase=[0.2686_2954, 0.2613_0258, 0.2757_7711]			,  __lowerCamelCase=True			,  )	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							size if size is not None else {"shortest_edge": 2_0}
            _SCREAMING_SNAKE_CASE				:		int  			=							crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
            _SCREAMING_SNAKE_CASE				:		str  			=							parent
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							batch_size
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							num_channels
            _SCREAMING_SNAKE_CASE				:		Any  			=							image_size
            _SCREAMING_SNAKE_CASE				:		Any  			=							min_resolution
            _SCREAMING_SNAKE_CASE				:		int  			=							max_resolution
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							do_resize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_center_crop
            _SCREAMING_SNAKE_CASE				:		Dict  			=							crop_size
            _SCREAMING_SNAKE_CASE				:		int  			=							do_normalize
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_mean
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_std
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							do_convert_rgb




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_convert_rgb": self.do_convert_rgb,
            }







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=False			,  __lowerCamelCase=False			,  __lowerCamelCase=False							)	->   Tuple:
            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							[]
                 for i in range(self.batch_size							):
                      image_inputs.append(
                          np.random.randint(
                              2_5_5			,  size=(self.num_channels, self.max_resolution, self.max_resolution)			,  dtype=np.uinta							)							)
            else:
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							[]
                 for i in range(self.batch_size							):
                      _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Tuple  			=							np.random.choice(np.arange(self.min_resolution			,  self.max_resolution							)			,  2							)
                      image_inputs.append(np.random.randint(2_5_5			,  size=(self.num_channels, width, height)			,  dtype=np.uinta							)							)

            if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for x in image_inputs]

            if torchify:
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							[torch.from_numpy(__lowerCamelCase							) for x in image_inputs]

            return image_inputs





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		str  			=							CLIPImageProcessingTester(self							)




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 2_0}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 1_8, "width": 1_8}							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.image_processing_class.from_dict(self.image_processor_dict			,  size=4_2			,  crop_size=8_4							)
            self.assertEqual(image_processor.size			,  {"shortest_edge": 4_2}							)
            self.assertEqual(image_processor.crop_size			,  {"height": 8_4, "width": 8_4}							)




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            pass




       def 			UpperCamelCase_	(					self							)	->   str:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		int  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random numpy tensors
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  numpify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  np.ndarray							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		Dict  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )







       def 			UpperCamelCase_	(					self							)	->   Any:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PyTorch tensors
            _SCREAMING_SNAKE_CASE				:		int  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase			,  torchify=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  torch.Tensor							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )





@require_torch
@require_vision
class 	lowerCAmelCase__(       __lowercase     ,   unittest.TestCase			):






       '''simple docstring'''


       __snake_case       	=			CLIPImageProcessor if is_vision_available() else None




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							CLIPImageProcessingTester(self			,  num_channels=4							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							3




       @property
       def 			UpperCamelCase_	(					self							)	->   Dict:
            return self.image_processor_tester.prepare_image_processor_dict()




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.image_processing_class(**self.image_processor_dict							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_resize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "size"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "center_crop"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_normalize"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_mean"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "image_std"							)							)
            self.assertTrue(hasattr(__lowerCamelCase			,  "do_convert_rgb"							)							)




       def 			UpperCamelCase_	(					self							)	->   List[str]:
            pass







       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            # Initialize image_processing
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.image_processing_class(**self.image_processor_dict							)
            # create random PIL images
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase							)
            for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase			,  Image.Image							)

            # Test not batched input
            _SCREAMING_SNAKE_CASE				:		Dict  			=							image_processing(image_inputs[0]			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    1,
                    self.expected_encoded_image_num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )

            # Test batched
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							image_processing(__lowerCamelCase			,  return_tensors="pt"							).pixel_values
            self.assertEqual(
                encoded_images.shape			,  (
                    self.image_processor_tester.batch_size,
                    self.expected_encoded_image_num_channels,
                    self.image_processor_tester.crop_size["height"],
                    self.image_processor_tester.crop_size["width"],
                )			,  )