






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


__UpperCAmelCase							:   Tuple							=  logging.get_logger(__name__)


class 					_snake_case						(			_A ):
 _A          =   ['pixel_values']






 def __init__(		self					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BICUBIC					,UpperCamelCase = True					,UpperCamelCase = 1 / 255					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = True					,**UpperCamelCase					,)						->    None:
     super().__init__(**UpperCamelCase       )
     snake_case__      :Optional[Any]         =   size if size is not None else {"height": 384, "width": 384}
     snake_case__      :Tuple         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )

     snake_case__      :int         =   do_resize
     snake_case__      :Optional[int]         =   size
     snake_case__      :Optional[int]         =   resample
     snake_case__      :List[str]         =   do_rescale
     snake_case__      :Tuple         =   rescale_factor
     snake_case__      :Optional[Any]         =   do_normalize
     snake_case__      :Optional[int]         =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     snake_case__      :List[str]         =   image_std if image_std is not None else OPENAI_CLIP_STD
     snake_case__      :Tuple         =   do_convert_rgb






 def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BICUBIC					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
     snake_case__      :Optional[Any]         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )
     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()}'       )
     snake_case__      :str         =   (size["height"], size["width"])
     return resize(UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






 def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    str:
     return rescale(UpperCamelCase					,scale=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






 def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
     return normalize(UpperCamelCase					,mean=UpperCamelCase					,std=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






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

     snake_case__      :Optional[Any]         =   size if size is not None else self.size
     snake_case__      :Union[str, Any]         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )

     snake_case__      :Optional[Any]         =   make_list_of_images(UpperCamelCase       )

     if not valid_images(UpperCamelCase       ):
         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:
         snake_case__      :List[Any]         =   [convert_to_rgb(UpperCamelCase       ) for image in images]

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

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

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

     if do_normalize:
         snake_case__      :Optional[int]         =   [self.normalize(image=UpperCamelCase					,mean=UpperCamelCase					,std=UpperCamelCase       ) for image in images]

     snake_case__      :str         =   [to_channel_dimension_format(UpperCamelCase					,UpperCamelCase       ) for image in images]

     snake_case__      :List[Any]         =   BatchFeature(data={"pixel_values": images}					,tensor_type=UpperCamelCase       )

     return encoded_outputs







from typing import List, Optional, Tuple

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


__UpperCAmelCase							:   str							=  logging.get_logger(__name__)

__UpperCAmelCase							:   List[Any]							=  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase							:   List[Any]							=  {
    "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"
    },
}

__UpperCAmelCase							:   str							=  {"allegro/herbert-base-cased": 5_1_4}
__UpperCAmelCase							:   List[str]							=  {}


class 					_snake_case						(			_A ):
	_A          =   VOCAB_FILES_NAMES
	_A          =   PRETRAINED_VOCAB_FILES_MAP
	_A          =   PRETRAINED_INIT_CONFIGURATION
	_A          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A          =   HerbertTokenizer






	def __init__(		self					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase=None					,UpperCamelCase="<s>"					,UpperCamelCase="<unk>"					,UpperCamelCase="<pad>"					,UpperCamelCase="<mask>"					,UpperCamelCase="</s>"					,**UpperCamelCase					,)						->    Dict:
					super().__init__(
					    UpperCamelCase					,UpperCamelCase					,tokenizer_file=UpperCamelCase					,cls_token=UpperCamelCase					,unk_token=UpperCamelCase					,pad_token=UpperCamelCase					,mask_token=UpperCamelCase					,sep_token=UpperCamelCase					,**UpperCamelCase					,)






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :Optional[int]         =   [self.cls_token_id]
					snake_case__      :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					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = False       )						->    List[int]:
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=UpperCamelCase					,token_ids_a=UpperCamelCase					,already_has_special_tokens=UpperCamelCase       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None       )						->    List[int]:
					snake_case__      :Any         =   [self.sep_token_id]
					snake_case__      :Union[str, Any]         =   [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					,UpperCamelCase					,UpperCamelCase = None       )						->    Tuple[str]:
					snake_case__      :List[str]         =   self._tokenizer.model.save(UpperCamelCase					,name=UpperCamelCase       )
					return tuple(UpperCamelCase       )