

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


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		List[Any]										=    ["pixel_values"]



      def __init__(      self				:    Union[str, Any]      , A__				:    bool = True      , A__				:    Dict[str, int] = None      , A__				:    PILImageResampling = PIL.Image.BICUBIC      , A__				:    bool = True      , A__				:    Dict[str, int] = None      , A__				:    Union[int, float] = 1 / 255      , A__				:    bool = True      , A__				:    bool = True      , A__				:    Optional[Union[float, List[float]]] = None      , A__				:    Optional[Union[float, List[float]]] = None      , **A__				:    Optional[int]      , )   ->					None:
        super().__init__(**A__		)
        _snake_case						=							size if size is not None else {'''height''': 256, '''width''': 256}
        _snake_case						=							get_size_dict(A__		)
        _snake_case						=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _snake_case						=							get_size_dict(A__      , param_name='''crop_size'''		)

        _snake_case						=							do_resize
        _snake_case						=							size
        _snake_case						=							resample
        _snake_case						=							do_center_crop
        _snake_case						=							crop_size
        _snake_case						=							do_rescale
        _snake_case						=							rescale_factor
        _snake_case						=							do_normalize
        _snake_case						=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _snake_case						=							image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 						UpperCamelCase_		(      self				:    str      , A__				:    np.ndarray      , A__				:    Dict[str, int]      , A__				:    PILImageResampling = PIL.Image.BICUBIC      , A__				:    Optional[Union[str, ChannelDimension]] = None      , **A__				:    List[str]      , )   ->					np.ndarray:
        _snake_case						=							get_size_dict(A__		)
        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(
            A__      , size=(size['''height'''], size['''width'''])      , resample=A__      , data_format=A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    int      , A__				:    np.ndarray      , A__				:    Dict[str, int]      , A__				:    Optional[Union[str, ChannelDimension]] = None      , **A__				:    str      , )   ->					np.ndarray:
        _snake_case						=							get_size_dict(A__		)
        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(A__      , size=(size['''height'''], size['''width'''])      , data_format=A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    np.ndarray      , A__				:    Union[int, float]      , A__				:    Optional[Union[str, ChannelDimension]] = None      , **A__				:    str      , )   ->					List[Any]:
        return rescale(A__      , scale=A__      , data_format=A__      , **A__		)



      def 						UpperCamelCase_		(      self				:    str      , A__				:    np.ndarray      , A__				:    Union[float, List[float]]      , A__				:    Union[float, List[float]]      , A__				:    Optional[Union[str, ChannelDimension]] = None      , **A__				:    Optional[Any]      , )   ->					np.ndarray:
        return normalize(A__      , mean=A__      , std=A__      , data_format=A__      , **A__		)







      def 						UpperCamelCase_		(      self				:    str      , A__				:    ImageInput      , A__				:    bool = None      , A__				:    Dict[str, int] = None      , A__				:    int=None      , A__				:    bool = None      , A__				:    Dict[str, int] = None      , A__				:    bool = None      , A__				:    float = None      , A__				:    bool = None      , A__				:    Optional[Union[float, List[float]]] = None      , A__				:    Optional[Union[float, List[float]]] = None      , A__				:    Optional[Union[str, TensorType]] = None      , A__				:    ChannelDimension = ChannelDimension.FIRST      , **A__				:    Union[str, Any]      , )   ->					PIL.Image.Image:
        _snake_case						=							do_resize if do_resize is not None else self.do_resize
        _snake_case						=							resample if resample is not None else self.resample
        _snake_case						=							do_center_crop if do_center_crop is not None else self.do_center_crop
        _snake_case						=							do_rescale if do_rescale is not None else self.do_rescale
        _snake_case						=							rescale_factor if rescale_factor is not None else self.rescale_factor
        _snake_case						=							do_normalize if do_normalize is not None else self.do_normalize
        _snake_case						=							image_mean if image_mean is not None else self.image_mean
        _snake_case						=							image_std if image_std is not None else self.image_std

        _snake_case						=							size if size is not None else self.size
        _snake_case						=							get_size_dict(A__		)
        _snake_case						=							crop_size if crop_size is not None else self.crop_size
        _snake_case						=							get_size_dict(A__      , param_name='''crop_size'''		)

        _snake_case						=							make_list_of_images(A__		)

        if not valid_images(A__		):
          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.
        _snake_case						=							[to_numpy_array(A__		) for image in images]

        if do_resize:
          _snake_case						=							[self.resize(image=A__      , size=A__      , resample=A__		) for image in images]

        if do_center_crop:
          _snake_case						=							[self.center_crop(image=A__      , size=A__		) for image in images]

        if do_rescale:
          _snake_case						=							[self.rescale(image=A__      , scale=A__		) for image in images]

        if do_normalize:
          _snake_case						=							[self.normalize(image=A__      , mean=A__      , std=A__		) for image in images]

        _snake_case						=							[to_channel_dimension_format(A__      , A__		) for image in images]

        _snake_case						=							{'''pixel_values''': images}
        return BatchFeature(data=A__      , tensor_type=A__		)




from typing import List, Optional, Tuple

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


__A      		=     logging.get_logger(__name__)

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

__A      		=     {
    '''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'''
    },
}

__A      		=     {'''allegro/herbert-base-cased''': 5_14}
__A      		=     {}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_      :		Dict										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    HerbertTokenizer



      def __init__(      self				:    Tuple      , A__				:    str=None      , A__				:    Optional[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]="<s>"      , A__				:    Optional[int]="<unk>"      , A__				:    str="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Dict="</s>"      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            A__      , A__      , tokenizer_file=A__      , cls_token=A__      , unk_token=A__      , pad_token=A__      , mask_token=A__      , sep_token=A__      , **A__      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[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 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

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



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _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		) * [0] + len(token_ids_a + sep		) * [1]







      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							self._tokenizer.model.save(A__      , name=A__		)
        return tuple(A__		)

