



"""simple docstring"""




import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 _lowerCAmelCase    : List[Any]							=							None


_lowerCAmelCase    : str							=							logging.get_logger(__name__)

_lowerCAmelCase    : Optional[Any]							=							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase    : str							=							{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCAmelCase    : Optional[int]							=							{
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       Tuple								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       Dict								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			TaTokenizer

      __SCREAMING_SNAKE_CASE						:       List[int]								=			[]



      def __init__(					self					:     List[str]					,     A					:     Optional[int]=None					,     A					:     int=None					,     A					:     Any="</s>"					,     A					:     Optional[Any]="<unk>"					,     A					:     int="<pad>"					,     A					:     List[Any]=1_0_0					,     A					:     str=None					,     **A					:     List[Any]					,     ):
           # Add extra_ids to the special token list
           if extra_ids > 0 and additional_special_tokens is None:
                _UpperCAmelCase      :     Any        =   [f'<extra_id_{i}>' for i in range(A						)]
           elif extra_ids > 0 and additional_special_tokens is not None:
                # Check that we have the right number of extra special tokens
                _UpperCAmelCase      :     str        =   len(set(filter(lambda A						: bool("extra_id_" in str(A						)						)					,     A						)						)						)
                if extra_tokens != extra_ids:
                     raise ValueError(
                         f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                         " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                         " tokens"						)

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

           _UpperCAmelCase      :     str        =   vocab_file
           _UpperCAmelCase      :     List[Any]        =   False if not self.vocab_file else True
           _UpperCAmelCase      :     List[Any]        =   extra_ids



      @staticmethod
      def 						snake_case_ (					A					:     List[str]					,     A					:     Union[str, Any]					,     A					:     List[Any]						):
           if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                _UpperCAmelCase      :     Tuple        =   TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                if init_max_model_length is not None and init_max_model_length != max_model_length:
                     return init_max_model_length
                elif init_max_model_length is None:
                     warnings.warn(
                         "This tokenizer was incorrectly instantiated with a model max length of"
                         f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                         " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                         " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                         f' {pretrained_model_name_or_path} automatically truncating your input to'
                         f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                         f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                         " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                         " instantiate this tokenizer with `model_max_length` set to your preferred value."					,     A					,     )

           return max_model_length



      def 						snake_case_ (					self					:     List[Any]					,     A					:     str					,     A					:     Optional[str] = None						):
           if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."						)

           if not os.path.isdir(A						):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
                return
           _UpperCAmelCase      :     Optional[int]        =   os.path.join(
               A					,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

           if os.path.abspath(self.vocab_file						) != os.path.abspath(A						):
                copyfile(self.vocab_file					,     A						)
                logger.info(f'Copy vocab file to {out_vocab_file}'						)

           return (out_vocab_file,)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     str        =   token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return self.prefix_tokens + token_ids_a
           else:
                _UpperCAmelCase      :     int        =   token_ids_a + [self.eos_token_id]
                return self.prefix_tokens + token_ids_a + token_ids_a



      def 						snake_case_ (					self					:     Dict					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     List[Any]        =   [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 						snake_case_ (					self					:     int						):
           return list(
               set(filter(lambda A						: bool(re.search(R"<extra_id_\d+>"					,     A						)						) is not None					,     self.additional_special_tokens						)						)						)



      def 						snake_case_ (					self					:     int						):
           return [self.convert_tokens_to_ids(A						) for token in self.get_sentinel_tokens()]





"""simple docstring"""




from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase    : Tuple							=							logging.get_logger(__name__)






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       str								=			['pixel_values']



      def __init__(					self					:     List[Any]					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     bool = True					,     A					:     Union[int, float] = 1 / 2_5_5					,     A					:     bool = True					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					,     **A					:     List[Any]					,     ):
           super().__init__(**A						)
           _UpperCAmelCase      :     int        =   size if size is not None else {"shortest_edge": 2_2_4}
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     int        =   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase      :     Union[str, Any]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     str        =   do_resize
           _UpperCAmelCase      :     Union[str, Any]        =   size
           _UpperCAmelCase      :     Tuple        =   resample
           _UpperCAmelCase      :     List[Any]        =   do_center_crop
           _UpperCAmelCase      :     List[Any]        =   crop_size
           _UpperCAmelCase      :     Tuple        =   do_rescale
           _UpperCAmelCase      :     List[Any]        =   rescale_factor
           _UpperCAmelCase      :     Dict        =   do_normalize
           _UpperCAmelCase      :     Optional[Any]        =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           _UpperCAmelCase      :     Any        =   image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Union[str, Any]					,     ):
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                _UpperCAmelCase      :     Any        =   int((2_5_6 / 2_2_4) * size["shortest_edge"]						)
                _UpperCAmelCase      :     Union[str, Any]        =   get_resize_output_image_size(A					,     size=A					,     default_to_square=A						)
                _UpperCAmelCase      :     str        =   {"height": output_size[0], "width": output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                raise ValueError(
                    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'						)
           return resize(
               A					,     size=(size_dict["height"], size_dict["width"])					,     resample=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     int					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Optional[Any]					,     ):
           _UpperCAmelCase      :     Dict        =   get_size_dict(A						)
           if "height" not in size or "width" not in size:
                raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'						)
           return center_crop(A					,     size=(size["height"], size["width"])					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[int, float]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Dict					,     ):
           return rescale(A					,     scale=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[float, List[float]]					,     A					:     Union[float, List[float]]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     str					,     ):
           return normalize(A					,     mean=A					,     std=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     ImageInput					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     PILImageResampling = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[float] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[TensorType] = None					,     A					:     ChannelDimension = ChannelDimension.FIRST					,     **A					:     Dict					,     ):
           _UpperCAmelCase      :     Dict        =   do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase      :     List[str]        =   resample if resample is not None else self.resample
           _UpperCAmelCase      :     int        =   do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase      :     str        =   do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase      :     Tuple        =   rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase      :     Optional[int]        =   do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase      :     List[str]        =   image_mean if image_mean is not None else self.image_mean
           _UpperCAmelCase      :     Tuple        =   image_std if image_std is not None else self.image_std

           _UpperCAmelCase      :     Any        =   size if size is not None else self.size
           _UpperCAmelCase      :     Optional[int]        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     List[str]        =   crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase      :     List[str]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     List[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.
           _UpperCAmelCase      :     Any        =   [to_numpy_array(A						) for image in images]

           if do_resize:
                _UpperCAmelCase      :     str        =   [self.resize(A					,     A					,     A						) for image in images]

           if do_center_crop:
                _UpperCAmelCase      :     Dict        =   [self.center_crop(A					,     A						) for image in images]

           if do_rescale:
                _UpperCAmelCase      :     Dict        =   [self.rescale(A					,     A						) for image in images]

           if do_normalize:
                _UpperCAmelCase      :     Optional[int]        =   [self.normalize(A					,     A					,     A						) for image in images]

           _UpperCAmelCase      :     Union[str, Any]        =   [to_channel_dimension_format(A					,     A						) for image in images]

           _UpperCAmelCase      :     List[Any]        =   {"pixel_values": images}
           return BatchFeature(data=A					,     tensor_type=A						)
