from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


UpperCAmelCase							   =      logging.get_logger(__name__)






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
			UpperCamelCase_				:						Optional[int] 			=   ['pixel_values']
			def __init__(    self							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = PIL.Image.NEAREST							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 1 / 2_55							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     **lowerCAmelCase_							,     )	->None:
								super().__init__(**lowerCAmelCase_    )
								a  :    Tuple				=						size if size is not None else {'''height''': 3_46, '''width''': 3_46}
								a  :    str				=						get_size_dict(lowerCAmelCase_    )
								a  :    Any				=						crop_size if crop_size is not None else {'''height''': 2_89, '''width''': 2_89}
								a  :    Tuple				=						get_size_dict(lowerCAmelCase_							,     param_name='''crop_size'''    )

								a  :    int				=						do_resize
								a  :    Tuple				=						size
								a  :    Union[str, Any]				=						resample
								a  :    Optional[Any]				=						do_center_crop
								a  :    Optional[Any]				=						crop_size
								a  :    Dict				=						do_rescale
								a  :    Tuple				=						rescale_factor
								a  :    Union[str, Any]				=						rescale_offset
								a  :    List[Any]				=						do_normalize
								a  :    Tuple				=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a  :    List[Any]				=						image_std if image_std is not None else IMAGENET_STANDARD_STD
								a  :    Union[str, Any]				=						include_top
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = PIL.Image.NEAREST							,     lowerCAmelCase_ = None							,     **lowerCAmelCase_							,     )	->np.ndarray:
								a  :    Any				=						get_size_dict(lowerCAmelCase_    )
								if "height" not in size or "width" not in size:
													raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
								return resize(
								    lowerCAmelCase_							,     size=(size['''height'''], size['''width'''])							,     resample=lowerCAmelCase_							,     data_format=lowerCAmelCase_							,     **lowerCAmelCase_    )
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     **lowerCAmelCase_							,     )	->np.ndarray:
								a  :    Tuple				=						get_size_dict(lowerCAmelCase_    )
								if "height" not in size or "width" not in size:
													raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
								return center_crop(lowerCAmelCase_							,     size=(size['''height'''], size['''width'''])							,     data_format=lowerCAmelCase_							,     **lowerCAmelCase_    )
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = None							,     **lowerCAmelCase_							,     )	->List[Any]:
								if offset:
													a  :    int				=						(image - 1_27.5) * scale
													if data_format is not None:
																		a  :    Dict				=						to_channel_dimension_format(lowerCAmelCase_							,     lowerCAmelCase_    )
													a  :    Dict				=						rescaled_image.astype(np.floataa    )
								else:
													a  :    Tuple				=						rescale(lowerCAmelCase_							,     scale=lowerCAmelCase_							,     data_format=lowerCAmelCase_							,     **lowerCAmelCase_    )
								return rescaled_image
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     **lowerCAmelCase_							,     )	->np.ndarray:
								return normalize(lowerCAmelCase_							,     mean=lowerCAmelCase_							,     std=lowerCAmelCase_							,     data_format=lowerCAmelCase_							,     **lowerCAmelCase_    )


			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_=None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = ChannelDimension.FIRST							,     **lowerCAmelCase_							,     )	->PIL.Image.Image:
								a  :    int				=						do_resize if do_resize is not None else self.do_resize
								a  :    Any				=						resample if resample is not None else self.resample
								a  :    List[str]				=						do_center_crop if do_center_crop is not None else self.do_center_crop
								a  :    Any				=						do_rescale if do_rescale is not None else self.do_rescale
								a  :    Any				=						rescale_factor if rescale_factor is not None else self.rescale_factor
								a  :    Tuple				=						rescale_offset if rescale_offset is not None else self.rescale_offset
								a  :    Dict				=						do_normalize if do_normalize is not None else self.do_normalize
								a  :    List[str]				=						image_mean if image_mean is not None else self.image_mean
								a  :    Union[str, Any]				=						image_std if image_std is not None else self.image_std
								a  :    int				=						include_top if include_top is not None else self.include_top

								a  :    Dict				=						size if size is not None else self.size
								a  :    int				=						get_size_dict(lowerCAmelCase_    )
								a  :    Any				=						crop_size if crop_size is not None else self.crop_size
								a  :    Optional[Any]				=						get_size_dict(lowerCAmelCase_							,     param_name='''crop_size'''    )

								a  :    int				=						make_list_of_images(lowerCAmelCase_    )

								if not valid_images(lowerCAmelCase_    ):
													raise ValueError(
													    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
													    '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

								if do_resize and size is None or resample is None:
													raise ValueError('''Size and resample must be specified if do_resize is True.'''    )

								if do_center_crop and crop_size is None:
													raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

								if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

								if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

								# All transformations expect numpy arrays.
								a  :    Optional[int]				=						[to_numpy_array(lowerCAmelCase_    ) for image in images]

								if do_resize:
													a  :    str				=						[self.resize(image=lowerCAmelCase_							,     size=lowerCAmelCase_							,     resample=lowerCAmelCase_    ) for image in images]

								if do_center_crop:
													a  :    List[Any]				=						[self.center_crop(image=lowerCAmelCase_							,     size=lowerCAmelCase_    ) for image in images]

								if do_rescale:
													a  :    Dict				=						[self.rescale(image=lowerCAmelCase_							,     scale=lowerCAmelCase_							,     offset=lowerCAmelCase_    ) for image in images]

								if do_normalize:
													a  :    Dict				=						[self.normalize(image=lowerCAmelCase_							,     mean=lowerCAmelCase_							,     std=lowerCAmelCase_    ) for image in images]

								if include_top:
													a  :    Optional[Any]				=						[self.normalize(image=lowerCAmelCase_							,     mean=[0, 0, 0]							,     std=lowerCAmelCase_    ) for image in images]

								a  :    List[str]				=						[to_channel_dimension_format(lowerCAmelCase_							,     lowerCAmelCase_    ) for image in images]

								a  :    int				=						{'''pixel_values''': images}
								return BatchFeature(data=lowerCAmelCase_							,     tensor_type=lowerCAmelCase_    )
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @cached_property
   def _lowercase(    self    )	->List[str]:
        return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''    )
   @cached_property
   def _lowercase(    self    )	->str:
        return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''    )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.tokenizer_ab
        a  :    List[Any]				=						''' I am a small frog.'''
        a  :    Optional[Any]				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    int				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						self.rust_tokenizer_ab
        a  :    str				=						''' I am a small frog.'''
        a  :    Tuple				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    Optional[Any]				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Tuple:
        assert self.tokenizer_ab.add_prefix_space
        assert self.tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]


   def _lowercase(    self    )	->Optional[Any]:
        assert self.rust_tokenizer_ab.add_prefix_space
        assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]