

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,
    get_resize_output_image_size,
    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, logging


a_      =     logging.get_logger(__name__)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =['pixel_values']
			def __init__(      self				:			Tuple   ,   a				:			bool = True   ,   a				:			Optional[Dict[str, int]] = None   ,   a				:			PILImageResampling = PILImageResampling.BILINEAR   ,   a				:			bool = True   ,   a				:			Dict[str, int] = None   ,   a				:			bool = True   ,   a				:			Union[int, float] = 1 / 255   ,   a				:			bool = True   ,   a				:			Optional[Union[float, List[float]]] = None   ,   a				:			Optional[Union[float, List[float]]] = None   ,   **a				:			List[str]   ,   )				->							None:

				"""simple docstring"""

				super().__init__(**a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							size if size is not None else {"shortest_edge": 256}
				SCREAMING_SNAKE_CASE :   Tuple					  =							get_size_dict(a   ,   default_to_square=a						)
				SCREAMING_SNAKE_CASE :   Any					  =							crop_size if crop_size is not None else {"height": 224, "width": 224}
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							get_size_dict(a						)
				SCREAMING_SNAKE_CASE :   str					  =							do_resize
				SCREAMING_SNAKE_CASE :   List[str]					  =							size
				SCREAMING_SNAKE_CASE :   List[str]					  =							resample
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							do_center_crop
				SCREAMING_SNAKE_CASE :   int					  =							crop_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							do_rescale
				SCREAMING_SNAKE_CASE :   str					  =							rescale_factor
				SCREAMING_SNAKE_CASE :   List[str]					  =							do_normalize
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				SCREAMING_SNAKE_CASE :   int					  =							image_std if image_std is not None else IMAGENET_STANDARD_STD
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			np.ndarray   ,   a				:			Dict[str, int]   ,   a				:			PILImageResampling = PILImageResampling.BICUBIC   ,   a				:			Optional[Union[str, ChannelDimension]] = None   ,   **a				:			str   ,   )				->							np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							get_size_dict(a   ,   default_to_square=a						)
				if "shortest_edge" not in size:
					raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"						)
				SCREAMING_SNAKE_CASE :   str					  =							get_resize_output_image_size(a   ,   size=size["shortest_edge"]   ,   default_to_square=a						)
				return resize(a   ,   size=a   ,   resample=a   ,   data_format=a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			np.ndarray   ,   a				:			Dict[str, int]   ,   a				:			Optional[Union[str, ChannelDimension]] = None   ,   **a				:			str   ,   )				->							np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							get_size_dict(a						)
				return center_crop(a   ,   size=(size["height"], size["width"])   ,   data_format=a   ,   **a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			np.ndarray   ,   a				:			float   ,   a				:			Optional[Union[str, ChannelDimension]] = None   ,   **a				:			List[str]						)				->							np.ndarray:

				"""simple docstring"""

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

				"""simple docstring"""

				return normalize(a   ,   mean=a   ,   std=a   ,   data_format=a   ,   **a						)






			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			ImageInput   ,   a				:			Optional[bool] = None   ,   a				:			Dict[str, int] = None   ,   a				:			PILImageResampling = None   ,   a				:			bool = None   ,   a				:			Dict[str, int] = None   ,   a				:			Optional[bool] = None   ,   a				:			Optional[float] = None   ,   a				:			Optional[bool] = None   ,   a				:			Optional[Union[float, List[float]]] = None   ,   a				:			Optional[Union[float, List[float]]] = None   ,   a				:			Optional[Union[str, TensorType]] = None   ,   a				:			Union[str, ChannelDimension] = ChannelDimension.FIRST   ,   **a				:			Any   ,   )				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE :   Any					  =							size if size is not None else self.size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							get_size_dict(a   ,   default_to_square=a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							do_center_crop if do_center_crop is not None else self.do_center_crop
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							crop_size if crop_size is not None else self.crop_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							get_size_dict(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							do_rescale if do_rescale is not None else self.do_rescale
				SCREAMING_SNAKE_CASE :   Dict					  =							rescale_factor if rescale_factor is not None else self.rescale_factor
				SCREAMING_SNAKE_CASE :   List[Any]					  =							do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_mean if image_mean is not None else self.image_mean
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_std if image_std is not None else self.image_std

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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:
					raise ValueError("Size 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.
				SCREAMING_SNAKE_CASE :   Dict					  =							[to_numpy_array(a						) for image in images]

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

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

				if do_rescale:
					SCREAMING_SNAKE_CASE :   List[str]					  =							[self.rescale(image=a   ,   scale=a						) for image in images]

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

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[to_channel_dimension_format(a   ,   a						) for image in images]

				SCREAMING_SNAKE_CASE :   Tuple					  =							{"pixel_values": images}
				return BatchFeature(data=a   ,   tensor_type=a						)


"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple

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


_A							=  logging.get_logger(__name__)

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['input_ids', 'attention_mask']



  def __init__(self							,       _lowerCamelCase="</s>"							,       _lowerCamelCase="<unk>"							,       _lowerCamelCase="<pad>"							,       _lowerCamelCase=125							,       _lowerCamelCase=None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        if extra_ids > 0 and additional_special_tokens is None:
              UpperCAmelCase__			:							List[str]							=    [F"""<extra_id_{i}>""" for i in range(_lowerCamelCase	)]
        elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              UpperCAmelCase__			:							int							=    len(set(filter(lambda _lowerCamelCase	: bool("""extra_id""" in str(_lowerCamelCase	)	)							,       _lowerCamelCase	)	)	)
              if extra_tokens != extra_ids:
                    raise ValueError(
                        F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                        """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
                        """ extra_ids tokens"""	)

        UpperCAmelCase__			:							List[str]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else pad_token
        UpperCAmelCase__			:							int							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else eos_token
        UpperCAmelCase__			:							Optional[int]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else unk_token

        super().__init__(
            eos_token=_lowerCamelCase							,       unk_token=_lowerCamelCase							,       pad_token=_lowerCamelCase							,       extra_ids=_lowerCamelCase							,       additional_special_tokens=_lowerCamelCase							,       **_lowerCamelCase							,       )

        UpperCAmelCase__			:							int							=    extra_ids

        UpperCAmelCase__			:							List[str]							=    2**8  # utf is 8 bits

        # define special tokens dict
        UpperCAmelCase__			:							Dict[int, str]							=    {
            self.pad_token: 0,
            self.eos_token: 1,
            self.unk_token: 2,
        }
        UpperCAmelCase__			:							str							=    len(self.special_tokens_encoder	)
        UpperCAmelCase__			:							Union[str, Any]							=    len(_lowerCamelCase	)
        for i, token in enumerate(_lowerCamelCase	):
              UpperCAmelCase__			:							Optional[Any]							=    self.vocab_size + i - n
        UpperCAmelCase__			:							Dict[str, int]							=    {v: k for k, v in self.special_tokens_encoder.items()}



  @property
  def  _a      (self	):


        """simple docstring"""

        return self._utf_vocab_size + self._num_special_tokens + self._extra_ids



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = False	):


        """simple docstring"""

        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_lowerCamelCase							,       token_ids_a=_lowerCamelCase							,       already_has_special_tokens=_lowerCamelCase	)

        # normal case: some special tokens
        if token_ids_a is None:
              return ([0] * len(_lowerCamelCase	)) + [1]
        return ([0] * len(_lowerCamelCase	)) + [1] + ([0] * len(_lowerCamelCase	)) + [1]



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if len(_lowerCamelCase	) > 0 and token_ids[-1] == self.eos_token_id:
              warnings.warn(
                  F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                  """ eos tokens being added."""	)
              return token_ids
        else:
              return token_ids + [self.eos_token_id]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    [self.eos_token_id]

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



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self._add_eos_if_not_present(_lowerCamelCase	)
        if token_ids_a is None:
              return token_ids_a
        else:
              UpperCAmelCase__			:							List[str]							=    self._add_eos_if_not_present(_lowerCamelCase	)
              return token_ids_a + token_ids_a



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    [chr(_lowerCamelCase	) for i in text.encode("""utf-8"""	)]
        return tokens



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if token in self.special_tokens_encoder:
              UpperCAmelCase__			:							List[str]							=    self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
              UpperCAmelCase__			:							Tuple							=    self.added_tokens_encoder[token]
        elif len(_lowerCamelCase	) != 1:
              UpperCAmelCase__			:							Dict							=    self.unk_token_id
        else:
              UpperCAmelCase__			:							Tuple							=    ord(_lowerCamelCase	) + self._num_special_tokens
        return token_id



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if index in self.special_tokens_decoder:
              UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[index]
        else:
              UpperCAmelCase__			:							Dict							=    chr(index - self._num_special_tokens	)
        return token



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    B""""""
        for token in tokens:
              if token in self.special_tokens_decoder:
                    UpperCAmelCase__			:							Tuple							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.added_tokens_decoder:
                    UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.special_tokens_encoder:
                    UpperCAmelCase__			:							str							=    token.encode("""utf-8"""	)
              elif token in self.added_tokens_encoder:
                    UpperCAmelCase__			:							List[Any]							=    token.encode("""utf-8"""	)
              else:
                    UpperCAmelCase__			:							Optional[int]							=    bytes([ord(_lowerCamelCase	)]	)
              bstring += tok_string
        UpperCAmelCase__			:							int							=    bstring.decode("""utf-8"""							,       errors="""ignore"""	)
        return string




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        return ()

