






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_	:    str            =      None


lowerCAmelCase_	:    str            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Union[str, Any]            =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_	:    Dict            =      {
    '''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_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Any				=	VOCAB_FILES_NAMES
  _A       :					Tuple				=	PRETRAINED_VOCAB_FILES_MAP
  _A       :					List[Any]				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A       :					str				=	['input_ids', 'attention_mask']
  _A       :					Optional[int]				=	TaTokenizer

  _A       :					List[int]				=	[]



  def __init__(      self ,		snake_case__=None ,		snake_case__=None ,		snake_case__="</s>" ,		snake_case__="<unk>" ,		snake_case__="<pad>" ,		snake_case__=1_00 ,		snake_case__=None ,		**snake_case__ ,		)			->			Tuple:



         """simple docstring"""





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

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



  @staticmethod
  def 		UpperCamelCase_				(      snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                UpperCAmelCase	    =			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.""" ,		snake_case__ ,		)

         return max_model_length



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			Tuple[str]:



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

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

         return (out_vocab_file,)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			List[int]:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			List[int]:



         """simple docstring"""





         UpperCAmelCase	    =			[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 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         return [self.convert_tokens_to_ids(snake_case__				) 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


lowerCAmelCase_	:    Union[str, Any]            =      logging.get_logger(__name__)





class   UpperCamelCase_ (       a_  ):
  _A       :					List[str]				=	['pixel_values']



  def __init__(      self ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 0.9 ,		snake_case__ = PILImageResampling.BICUBIC ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = None ,		**snake_case__ ,		)			->			None:



         """simple docstring"""





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

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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = PILImageResampling.BICUBIC ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         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:
                       UpperCAmelCase	    =			int(size["""shortest_edge"""] / crop_pct				)
                elif "height" in size and "width" in size:
                       if size["height"] == size["width"]:
                              UpperCAmelCase	    =			int(size["""height"""] / crop_pct				)
                       else:
                              UpperCAmelCase	    =			(int(size["""height"""] / crop_pct				), int(size["""width"""] / crop_pct				))
                else:
                       raise ValueError("""Invalid size for resize: {}""".format(snake_case__				)				)

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

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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__				)
         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(snake_case__ ,		size=(size["""height"""], size["""width"""]) ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = ChannelDimension.FIRST ,		**snake_case__ ,		)			->			PIL.Image.Image:



         """simple docstring"""





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

         UpperCAmelCase	    =			size if size is not None else self.size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         UpperCAmelCase	    =			crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		param_name="""crop_size"""				)

         UpperCAmelCase	    =			make_list_of_images(snake_case__				)

         if not valid_images(snake_case__				):
                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.
         UpperCAmelCase	    =			[to_numpy_array(snake_case__				) for image in images]

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

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

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

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

         UpperCAmelCase	    =			[to_channel_dimension_format(snake_case__ ,		snake_case__				) for image in images]

         UpperCAmelCase	    =			{"""pixel_values""": images}
         return BatchFeature(data=snake_case__ ,		tensor_type=snake_case__				)
