
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


lowercase__          =logging.get_logger(__name__)



class 		UpperCamelCase__  (	lowerCamelCase__   ):
			_SCREAMING_SNAKE_CASE				:							List[str]     	=				["""pixel_values"""]
			def __init__(self			: Optional[Any] ,    snake_case_			: Union[str, Any] = True ,    snake_case_			: Any = None ,    snake_case_			: Optional[int] = PILImageResampling.BICUBIC ,    snake_case_			: Tuple = True ,    snake_case_			: Union[str, Any] = 1 / 2_5_5 ,    snake_case_			: str = True ,    snake_case_			: Optional[Any] = None ,    snake_case_			: List[str] = None ,    snake_case_			: Optional[Any] = True ,    **snake_case_			: Optional[Any] ,    ):
				super().__init__(**snake_case__       )
				__a			:  List[str]		    =  size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
				__a			:  List[str]		    =  get_size_dict(snake_case__ ,    default_to_square=snake_case__       )

				__a			:  List[str]		    =  do_resize
				__a			:  Tuple		    =  size
				__a			:  Optional[Any]		    =  resample
				__a			:  Optional[Any]		    =  do_rescale
				__a			:  Dict		    =  rescale_factor
				__a			:  Union[str, Any]		    =  do_normalize
				__a			:  Dict		    =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				__a			:  Any		    =  image_std if image_std is not None else OPENAI_CLIP_STD
				__a			:  List[Any]		    =  do_convert_rgb
			def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: List[Any] ,    snake_case_			: int ,    snake_case_			: int = PILImageResampling.BICUBIC ,    snake_case_			: int = None ,    **snake_case_			: Any ,    ):
				__a			:  int		    =  get_size_dict(snake_case__ ,    default_to_square=snake_case__       )
				if "height" not in size or "width" not in size:
					raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"       )
				__a			:  Union[str, Any]		    =  (size['''height'''], size['''width'''])
				return resize(snake_case__ ,    size=snake_case__ ,    resample=snake_case__ ,    data_format=snake_case__ ,    **snake_case__       )
			def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: Dict ,    snake_case_			: Any ,    snake_case_			: List[Any] = None ,    **snake_case_			: Any ,    ):
				return rescale(snake_case__ ,    scale=snake_case__ ,    data_format=snake_case__ ,    **snake_case__       )
			def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: int ,    snake_case_			: str ,    snake_case_			: Optional[int] ,    snake_case_			: Optional[int] = None ,    **snake_case_			: Tuple ,    ):
				return normalize(snake_case__ ,    mean=snake_case__ ,    std=snake_case__ ,    data_format=snake_case__ ,    **snake_case__       )







			def 				lowerCAmelCase      (self			: str ,    snake_case_			: List[str] ,    snake_case_			: int = None ,    snake_case_			: int = None ,    snake_case_			: List[Any] = None ,    snake_case_			: Tuple = None ,    snake_case_			: int = None ,    snake_case_			: str = None ,    snake_case_			: str = None ,    snake_case_			: Union[str, Any] = None ,    snake_case_			: Dict = None ,    snake_case_			: Any = None ,    snake_case_			: Optional[Any] = ChannelDimension.FIRST ,    **snake_case_			: Tuple ,    ):
				__a			:  List[str]		    =  do_resize if do_resize is not None else self.do_resize
				__a			:  Union[str, Any]		    =  resample if resample is not None else self.resample
				__a			:  Any		    =  do_rescale if do_rescale is not None else self.do_rescale
				__a			:  int		    =  rescale_factor if rescale_factor is not None else self.rescale_factor
				__a			:  Union[str, Any]		    =  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			:  List[Any]		    =  image_std if image_std is not None else self.image_std
				__a			:  Optional[Any]		    =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				__a			:  List[Any]		    =  size if size is not None else self.size
				__a			:  Tuple		    =  get_size_dict(snake_case__ ,    default_to_square=snake_case__       )

				__a			:  int		    =  make_list_of_images(snake_case__       )

				if not valid_images(snake_case__       ):
					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_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.'''       )

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
					__a			:  Optional[int]		    =  [convert_to_rgb(snake_case__       ) for image in images]

				# All transformations expect numpy arrays.
				__a			:  str		    =  [to_numpy_array(snake_case__       ) for image in images]

				if do_resize:
					__a			:  List[Any]		    =  [self.resize(image=snake_case__ ,    size=snake_case__ ,    resample=snake_case__       ) for image in images]

				if do_rescale:
					__a			:  int		    =  [self.rescale(image=snake_case__ ,    scale=snake_case__       ) for image in images]

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

				__a			:  Optional[int]		    =  [to_channel_dimension_format(snake_case__ ,    snake_case__       ) for image in images]

				__a			:  str		    =  BatchFeature(data={'''pixel_values''': images} ,    tensor_type=snake_case__       )

				return encoded_outputs


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [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       ) * [0] + len(token_ids_a + sep       ) * [1]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
