

'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_a			:	str								=  logging.get_logger(__name__)


if is_vision_available():
  import PIL



class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Dict      					=				["pixel_values"]
  def __init__(       self				,	a_ = True				,	a_ = None				,	a_ = PILImageResampling.BILINEAR				,	a_ = True				,	a_ = False				,	a_ = True				,	a_ = True				,	a_ = 1 / 2_5_5				,	a_ = True				,	a_ = None				,	a_ = None				,	**a_				,	)							->			None:


    """simple docstring"""
    super().__init__(**a_       )

    UpperCAmelCase  							=		size if size is not None else {'height': 2_5_6_0, 'width': 1_9_2_0}
    if isinstance(a_				,	(tuple, list)       ):
      # The previous feature extractor size parameter was in (width, height) format
      UpperCAmelCase  							=		size[::-1]
    UpperCAmelCase  							=		get_size_dict(a_       )

    UpperCAmelCase  							=		do_resize
    UpperCAmelCase  							=		size
    UpperCAmelCase  							=		resample
    UpperCAmelCase  							=		do_thumbnail
    UpperCAmelCase  							=		do_align_long_axis
    UpperCAmelCase  							=		do_pad
    UpperCAmelCase  							=		do_rescale
    UpperCAmelCase  							=		rescale_factor
    UpperCAmelCase  							=		do_normalize
    UpperCAmelCase  							=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    UpperCAmelCase  							=		image_std if image_std is not None else IMAGENET_STANDARD_STD
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = None       )							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		get_image_size(a_       )
    UpperCAmelCase							, UpperCAmelCase  							=		size['height'], size['width']

    if (output_width < output_height and input_width > input_height) or (
        output_width > output_height and input_width < input_height
    ):
      UpperCAmelCase  							=		np.rotaa(a_				,	3       )

    if data_format is not None:
      UpperCAmelCase  							=		to_channel_dimension_format(a_				,	a_       )

    return image
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = False				,	a_ = None				,	)							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		size['height'], size['width']
    UpperCAmelCase							, UpperCAmelCase  							=		get_image_size(a_       )

    UpperCAmelCase  							=		output_width - input_width
    UpperCAmelCase  							=		output_height - input_height

    if random_padding:
      UpperCAmelCase  							=		np.random.randint(low=0				,	high=delta_height + 1       )
      UpperCAmelCase  							=		np.random.randint(low=0				,	high=delta_width + 1       )
    else:
      UpperCAmelCase  							=		delta_height // 2
      UpperCAmelCase  							=		delta_width // 2

    UpperCAmelCase  							=		delta_height - pad_top
    UpperCAmelCase  							=		delta_width - pad_left

    UpperCAmelCase  							=		((pad_top, pad_bottom), (pad_left, pad_right))
    return pad(a_				,	a_				,	data_format=a_       )
  def   snake_case_     (       self				,	*a_				,	**a_       )							->			Union[str, Any]:


    """simple docstring"""
    logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'       )
    return self.pad_image(*a_				,	**a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = PILImageResampling.BICUBIC				,	a_ = None				,	**a_				,	)							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		get_image_size(a_       )
    UpperCAmelCase							, UpperCAmelCase  							=		size['height'], size['width']

    # We always resize to the smallest of either the input or output size.
    UpperCAmelCase  							=		min(a_				,	a_       )
    UpperCAmelCase  							=		min(a_				,	a_       )

    if height == input_height and width == input_width:
      return image

    if input_height > input_width:
      UpperCAmelCase  							=		int(input_width * height / input_height       )
    elif input_width > input_height:
      UpperCAmelCase  							=		int(input_height * width / input_width       )

    return resize(
        a_				,	size=(height, width)				,	resample=a_				,	reducing_gap=2.0				,	data_format=a_				,	**a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = PILImageResampling.BICUBIC				,	a_ = None				,	**a_				,	)							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase  							=		get_size_dict(a_       )
    UpperCAmelCase  							=		min(size['height']				,	size['width']       )
    UpperCAmelCase  							=		get_resize_output_image_size(a_				,	size=a_				,	default_to_square=a_       )
    UpperCAmelCase  							=		resize(a_				,	size=a_				,	resample=a_				,	data_format=a_				,	**a_       )
    return resized_image
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = None				,	**a_				,	)							->			Optional[Any]:


    """simple docstring"""
    return rescale(a_				,	scale=a_				,	data_format=a_				,	**a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_ = None				,	**a_				,	)							->			np.ndarray:


    """simple docstring"""
    return normalize(a_				,	mean=a_				,	std=a_				,	data_format=a_				,	**a_       )





  def   snake_case_     (       self				,	a_				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = False				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = ChannelDimension.FIRST				,	**a_				,	)							->			PIL.Image.Image:


    """simple docstring"""
    UpperCAmelCase  							=		do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase  							=		size if size is not None else self.size
    if isinstance(a_				,	(tuple, list)       ):
      # Previous feature extractor had size in (width, height) format
      UpperCAmelCase  							=		size[::-1]
    UpperCAmelCase  							=		get_size_dict(a_       )
    UpperCAmelCase  							=		resample if resample is not None else self.resample
    UpperCAmelCase  							=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
    UpperCAmelCase  							=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
    UpperCAmelCase  							=		do_pad if do_pad is not None else self.do_pad
    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  							=		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_rescale and rescale_factor is None:
      raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

    if do_pad and size is None:
      raise ValueError('Size must be specified if do_pad 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(a_       ) for image in images]

    if do_align_long_axis:
      UpperCAmelCase  							=		[self.align_long_axis(a_				,	size=a_       ) for image in images]

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

    if do_thumbnail:
      UpperCAmelCase  							=		[self.thumbnail(image=a_				,	size=a_       ) for image in images]

    if do_pad:
      UpperCAmelCase  							=		[self.pad_image(image=a_				,	size=a_				,	random_padding=a_       ) for image in images]

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

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

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

    UpperCAmelCase  							=		{'pixel_values': images}
    return BatchFeature(data=a_				,	tensor_type=a_       )





'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_a			:	Optional[int]								=  logging.get_logger(__name__)


_a			:	List[str]								=  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
_a			:	Tuple								=  {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

_a			:	str								=  {
    'RUCAIBox/mvp': 1_024,
}


@lru_cache()
def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		(
      list(range(ord('!'  )    ,				ord('~'  ) + 1  )  ) + list(range(ord('¡'  )    ,				ord('¬'  ) + 1  )  ) + list(range(ord('®'  )    ,				ord('ÿ'  ) + 1  )  )
  )
  UpperCAmelCase  							=		bs[:]
  UpperCAmelCase  							=		0
  for b in range(2**8  ):
    if b not in bs:
      bs.append(SCREAMING_SNAKE_CASE  )
      cs.append(2**8 + n  )
      n += 1
  UpperCAmelCase  							=		[chr(SCREAMING_SNAKE_CASE  ) for n in cs]
  return dict(zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[Any]  ):
  UpperCAmelCase  							=		set()
  UpperCAmelCase  							=		word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)  )
    UpperCAmelCase  							=		char
  return pairs



class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	str      					=				VOCAB_FILES_NAMES
  __lowerCAmelCase   :	List[Any]      					=				PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase   :	Dict      					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase   :	Optional[Any]      					=				["input_ids", "attention_mask"]
  def __init__(       self				,	a_				,	a_				,	a_="replace"				,	a_="<s>"				,	a_="</s>"				,	a_="</s>"				,	a_="<s>"				,	a_="<unk>"				,	a_="<pad>"				,	a_="<mask>"				,	a_=False				,	**a_				,	)							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else bos_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else eos_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else sep_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else cls_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else unk_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else mask_token

    super().__init__(
        errors=a_				,	bos_token=a_				,	eos_token=a_				,	unk_token=a_				,	sep_token=a_				,	cls_token=a_				,	pad_token=a_				,	mask_token=a_				,	add_prefix_space=a_				,	**a_				,	)
    with open(a_				,	encoding='utf-8'       ) as vocab_handle:
      UpperCAmelCase  							=		json.load(a_       )
    UpperCAmelCase  							=		{v: k for k, v in self.encoder.items()}
    UpperCAmelCase  							=		errors  # how to handle errors in decoding
    UpperCAmelCase  							=		bytes_to_unicode()
    UpperCAmelCase  							=		{v: k for k, v in self.byte_encoder.items()}
    with open(a_				,	encoding='utf-8'       ) as merges_handle:
      UpperCAmelCase  							=		merges_handle.read().split('\n'       )[1:-1]
    UpperCAmelCase  							=		[tuple(merge.split()       ) for merge in bpe_merges]
    UpperCAmelCase  							=		dict(zip(a_				,	range(len(a_       )       )       )       )
    UpperCAmelCase  							=		{}
    UpperCAmelCase  							=		add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    UpperCAmelCase  							=		re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'       )
  @property
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    return len(self.encoder       )
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    return dict(self.encoder				,	**self.added_tokens_encoder       )
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    if token in self.cache:
      return self.cache[token]
    UpperCAmelCase  							=		tuple(a_       )
    UpperCAmelCase  							=		get_pairs(a_       )

    if not pairs:
      return token

    while True:
      UpperCAmelCase  							=		min(a_				,	key=lambda a_       : self.bpe_ranks.get(a_				,	float('inf'       )       )       )
      if bigram not in self.bpe_ranks:
        break
      UpperCAmelCase							, UpperCAmelCase  							=		bigram
      UpperCAmelCase  							=		[]
      UpperCAmelCase  							=		0
      while i < len(a_       ):
        try:
          UpperCAmelCase  							=		word.index(a_				,	a_       )
        except ValueError:
          new_word.extend(word[i:]       )
          break
        else:
          new_word.extend(word[i:j]       )
          UpperCAmelCase  							=		j

        if word[i] == first and i < len(a_       ) - 1 and word[i + 1] == second:
          new_word.append(first + second       )
          i += 2
        else:
          new_word.append(word[i]       )
          i += 1
      UpperCAmelCase  							=		tuple(a_       )
      UpperCAmelCase  							=		new_word
      if len(a_       ) == 1:
        break
      else:
        UpperCAmelCase  							=		get_pairs(a_       )
    UpperCAmelCase  							=		' '.join(a_       )
    UpperCAmelCase  							=		word
    return word
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    for token in re.findall(self.pat				,	a_       ):
      UpperCAmelCase  							=		''.join(
          self.byte_encoder[b] for b in token.encode('utf-8'       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
      bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a_       ).split(' '       )       )
    return bpe_tokens
  def   snake_case_     (       self				,	a_       )							->			Optional[int]:


    """simple docstring"""
    return self.encoder.get(a_				,	self.encoder.get(self.unk_token       )       )
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    return self.decoder.get(a_       )
  def   snake_case_     (       self				,	a_       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		''.join(a_       )
    UpperCAmelCase  							=		bytearray([self.byte_decoder[c] for c in text]       ).decode('utf-8'				,	errors=self.errors       )
    return text
  def   snake_case_     (       self				,	a_				,	a_ = None       )							->			Tuple[str]:


    """simple docstring"""
    if not os.path.isdir(a_       ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
      return
    UpperCAmelCase  							=		os.path.join(
        a_				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
    UpperCAmelCase  							=		os.path.join(
        a_				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

    with open(a_				,	'w'				,	encoding='utf-8'       ) as f:
      f.write(json.dumps(self.encoder				,	indent=2				,	sort_keys=a_				,	ensure_ascii=a_       ) + '\n'       )

    UpperCAmelCase  							=		0
    with open(a_				,	'w'				,	encoding='utf-8'       ) as writer:
      writer.write('#version: 0.2\n'       )
      for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,	key=lambda a_       : kv[1]       ):
        if index != token_index:
          logger.warning(
              F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
              ' Please check that the tokenizer is not corrupted!'       )
          UpperCAmelCase  							=		token_index
        writer.write(' '.join(a_       ) + '\n'       )
        index += 1

    return vocab_file, merge_file
  def   snake_case_     (       self				,	a_				,	a_ = None       )							->			List[int]:


    """simple docstring"""
    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase  							=		[self.cls_token_id]
    UpperCAmelCase  							=		[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def   snake_case_     (       self				,	a_				,	a_ = None				,	a_ = False       )							->			List[int]:


    """simple docstring"""
    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=a_				,	token_ids_a=a_				,	already_has_special_tokens=a_       )

    if token_ids_a is None:
      return [1] + ([0] * len(a_       )) + [1]
    return [1] + ([0] * len(a_       )) + [1, 1] + ([0] * len(a_       )) + [1]
  def   snake_case_     (       self				,	a_				,	a_ = None       )							->			List[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[self.sep_token_id]
    UpperCAmelCase  							=		[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 + sep + token_ids_a + sep       ) * [0]





  def   snake_case_     (       self				,	a_				,	a_=False				,	**a_       )							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		kwargs.pop('add_prefix_space'				,	self.add_prefix_space       )
    if (is_split_into_words or add_prefix_space) and (len(a_       ) > 0 and not text[0].isspace()):
      UpperCAmelCase  							=		' ' + text
    return (text, kwargs)


