'''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:
  __UpperCAmelCase												=None


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase												={
    "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
__UpperCAmelCase												={
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}




class    a__				(       __SCREAMING_SNAKE_CASE			):
     lowerCamelCase  :					List[Any]							=VOCAB_FILES_NAMES
     lowerCamelCase  :					List[Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Optional[Any]							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					Dict							=["input_ids", "attention_mask"]
     lowerCamelCase  :					List[Any]							=TaTokenizer

     lowerCamelCase  :					List[int]							=[]






     def __init__(			self		:		int  ,    a		:		Union[str, Any]=None  ,    a		:		str=None  ,    a		:		List[Any]="</s>"  ,    a		:		Optional[Any]="<unk>"  ,    a		:		Optional[int]="<pad>"  ,    a		:		int=1_00  ,    a		:		str=None  ,    **a		:		Tuple  ,    ):





         """simple docstring"""



         if extra_ids > 0 and additional_special_tokens is None:
             __lowerCamelCase          =  [f"""<extra_id_{i}>""" for i in range(__UpperCAmelCase   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
             # Check that we have the right number of extra special tokens
             __lowerCamelCase          =  len(set(filter(lambda a   : bool('''extra_id_''' in str(__UpperCAmelCase   )   )  ,    __UpperCAmelCase   )   )   )
             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__(
             __UpperCAmelCase  ,    tokenizer_file=__UpperCAmelCase  ,    eos_token=__UpperCAmelCase  ,    unk_token=__UpperCAmelCase  ,    pad_token=__UpperCAmelCase  ,    extra_ids=__UpperCAmelCase  ,    additional_special_tokens=__UpperCAmelCase  ,    **__UpperCAmelCase  ,    )

         __lowerCamelCase          =  vocab_file
         __lowerCamelCase          =  False if not self.vocab_file else True
         __lowerCamelCase          =  extra_ids






     @staticmethod
     def        SCREAMING_SNAKE_CASE__       (			a		:		Optional[int]  ,    a		:		Optional[int]  ,    a		:		Optional[Any]   ):





         """simple docstring"""



         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
             __lowerCamelCase          =  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.'''  ,    __UpperCAmelCase  ,    )

         return max_model_length






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		Any  ,    a		:		Union[str, Any] = None   ):





         """simple docstring"""



         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(__UpperCAmelCase   ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
             return
         __lowerCamelCase          =  os.path.join(
             __UpperCAmelCase  ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

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

         return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		str  ,    a		:		List[str] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a
         else:
             __lowerCamelCase          =  token_ids_a + [self.eos_token_id]
             return self.prefix_tokens + token_ids_a + token_ids_a






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		Dict  ,    a		:		List[str] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [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        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         return list(
             set(filter(lambda a   : bool(re.search(R'''<extra_id_\d+>'''  ,    __UpperCAmelCase   )   ) is not None  ,    self.additional_special_tokens   )   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return [self.convert_tokens_to_ids(__UpperCAmelCase   ) for token in self.get_sentinel_tokens()]

'''simple docstring'''




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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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												=logging.get_logger(__name__)




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=["pixel_values"]






     def __init__(			self		:		List[str]  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		int = 0.9  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		Union[int, float] = 1 / 2_55  ,    a		:		bool = True  ,    a		:		bool = True  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         super().__init__(**a   )
         __lowerCamelCase          =  size if size is not None else {'''shortest_edge''': 2_24}
         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         __lowerCamelCase          =  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         __lowerCamelCase          =  get_size_dict(a  ,    param_name='''crop_size'''   )

         __lowerCamelCase          =  do_resize
         __lowerCamelCase          =  size
         __lowerCamelCase          =  crop_pct
         __lowerCamelCase          =  resample
         __lowerCamelCase          =  do_center_crop
         __lowerCamelCase          =  crop_size
         __lowerCamelCase          =  do_rescale
         __lowerCamelCase          =  rescale_factor
         __lowerCamelCase          =  do_normalize
         __lowerCamelCase          =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __lowerCamelCase          =  image_std if image_std is not None else IMAGENET_DEFAULT_STD






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		np.ndarray  ,    a		:		Dict[str, int]  ,    a		:		Optional[float] = None  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		List[str]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""   )
         if crop_pct is not None:
             if "shortest_edge" in size:
                 __lowerCamelCase          =  int(size['''shortest_edge'''] / crop_pct   )
             elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                     __lowerCamelCase          =  int(size['''height'''] / crop_pct   )
                 else:
                     __lowerCamelCase          =  (int(size['''height'''] / crop_pct   ), int(size['''width'''] / crop_pct   ))
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(a   )   )

             __lowerCamelCase          =  get_resize_output_image_size(a  ,    size=a  ,    default_to_square=a   )
         else:
             if "shortest_edge" in size:
                 __lowerCamelCase          =  get_resize_output_image_size(a  ,    size=size['''shortest_edge''']  ,    default_to_square=a   )
             elif "height" in size and "width" in size:
                 __lowerCamelCase          =  (size['''height'''], size['''width'''])
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(a   )   )

         return resize(a  ,    size=a  ,    resample=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		np.ndarray  ,    a		:		Dict[str, int]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  get_size_dict(a   )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""   )
         return center_crop(a  ,    size=(size['''height'''], size['''width'''])  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		np.ndarray  ,    a		:		Union[int, float]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Union[str, Any]  ,    ):





         """simple docstring"""



         return rescale(a  ,    scale=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_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		:		Any  ,    ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		ImageInput  ,    a		:		bool = None  ,    a		:		Dict[str, int] = None  ,    a		:		int = None  ,    a		:		PILImageResampling = 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		:		Tuple  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase          =  crop_pct if crop_pct is not None else self.crop_pct
         __lowerCamelCase          =  resample if resample is not None else self.resample
         __lowerCamelCase          =  do_center_crop if do_center_crop is not None else self.do_center_crop
         __lowerCamelCase          =  do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase          =  rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase          =  do_normalize if do_normalize is not None else self.do_normalize
         __lowerCamelCase          =  image_mean if image_mean is not None else self.image_mean
         __lowerCamelCase          =  image_std if image_std is not None else self.image_std

         __lowerCamelCase          =  size if size is not None else self.size
         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         __lowerCamelCase          =  crop_size if crop_size is not None else self.crop_size
         __lowerCamelCase          =  get_size_dict(a  ,    param_name='''crop_size'''   )

         __lowerCamelCase          =  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_pct is None:
             raise ValueError('''Crop_pct 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.
         __lowerCamelCase          =  [to_numpy_array(a   ) for image in images]

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

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

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

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

         __lowerCamelCase          =  [to_channel_dimension_format(a  ,    a   ) for image in images]

         __lowerCamelCase          =  {'''pixel_values''': images}
         return BatchFeature(data=a  ,    tensor_type=a   )
