






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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_						     =  logging.get_logger(__name__)

lowerCamelCase_						     =  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowerCamelCase_						     =  {
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

lowerCamelCase_						     =  {
    """microsoft/biogpt""": 1_0_2_4,
}
def      lowerCamelCase					(						a_	)	->			int:
							lowerCAmelCase_										=     set()
							lowerCAmelCase_										=     word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)	)
														lowerCAmelCase_										=     char
							return pairs




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Union[str, Any]     =    VOCAB_FILES_NAMES
							__a:	int     =    PRETRAINED_VOCAB_FILES_MAP
							__a:	Union[str, Any]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a:	int     =    ['''input_ids''', '''attention_mask''']

							def __init__(		self			,			lowercase_			,			lowercase_			,			lowercase_="<unk>"			,			lowercase_="<s>"			,			lowercase_="</s>"			,			lowercase_="</s>"			,			lowercase_="<pad>"			,			**lowercase_			,			)					->					Optional[int]:
														'''simple docstring'''

														super().__init__(
														    bos_token=lowercase_			,			eos_token=lowercase_			,			sep_token=lowercase_			,			unk_token=lowercase_			,			pad_token=lowercase_			,			**lowercase_			,			)

														try:
																					import sacremoses
														except ImportError:
																					raise ImportError(
																					    'You need to install sacremoses to use BioGptTokenizer. '
																					    'See https://pypi.org/project/sacremoses/ for installation.'   )

														lowerCAmelCase_										=     'en'
														lowerCAmelCase_										=     sacremoses
														# cache of sm.MosesTokenizer instance
														lowerCAmelCase_										=     {}
														lowerCAmelCase_										=     {}
														with open(lowercase_			,			encoding='utf-8'   ) as vocab_handle:
																					lowerCAmelCase_										=     json.load(lowercase_   )
														lowerCAmelCase_										=     {v: k for k, v in self.encoder.items()}
														with open(lowercase_			,			encoding='utf-8'   ) as merges_handle:
																					lowerCAmelCase_										=     merges_handle.read().split('\n'   )[:-1]
														lowerCAmelCase_										=     [tuple(merge.split()[:2]   ) for merge in merges]
														lowerCAmelCase_										=     dict(zip(lowercase_			,			range(len(lowercase_   )   )   )   )
														lowerCAmelCase_										=     {}

							@property
							def      _lowercase       (		self   )					->					Optional[Any]:
														'''simple docstring'''

														return len(self.encoder   )

							def      _lowercase       (		self   )					->					int:
														'''simple docstring'''

														return dict(self.encoder			,			**self.added_tokens_encoder   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					Optional[Any]:
														'''simple docstring'''

														if lang not in self.cache_moses_tokenizer:
																					lowerCAmelCase_										=     self.sm.MosesTokenizer(lang=lowercase_   )
																					lowerCAmelCase_										=     moses_tokenizer
														return self.cache_moses_tokenizer[lang].tokenize(
														    lowercase_			,			aggressive_dash_splits=lowercase_			,			return_str=lowercase_			,			escape=lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_   )					->					Optional[Any]:
														'''simple docstring'''

														if lang not in self.cache_moses_detokenizer:
																					lowerCAmelCase_										=     self.sm.MosesDetokenizer(lang=lowercase_   )
																					lowerCAmelCase_										=     moses_detokenizer
														return self.cache_moses_detokenizer[lang].detokenize(lowercase_   )

							def      _lowercase       (		self			,			lowercase_   )					->					Tuple:
														'''simple docstring'''

														lowerCAmelCase_										=     tuple(token[:-1]   ) + (token[-1] + '</w>',)
														if token in self.cache:
																					return self.cache[token]
														lowerCAmelCase_										=     get_pairs(lowercase_   )

														if not pairs:
																					return token + "</w>"

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

																												if word[i] == first and i < len(lowercase_   ) - 1 and word[i + 1] == second:
																																			new_word.append(first + second   )
																																			i += 2
																												else:
																																			new_word.append(word[i]   )
																																			i += 1
																					lowerCAmelCase_										=     tuple(lowercase_   )
																					lowerCAmelCase_										=     new_word
																					if len(lowercase_   ) == 1:
																												break
																					else:
																												lowerCAmelCase_										=     get_pairs(lowercase_   )
														lowerCAmelCase_										=     ' '.join(lowercase_   )
														if word == "\n  </w>":
																					lowerCAmelCase_										=     '\n</w>'
														lowerCAmelCase_										=     word
														return word

							def      _lowercase       (		self			,			lowercase_			,			lowercase_=False   )					->					str:
														'''simple docstring'''

														if bypass_tokenizer:
																					lowerCAmelCase_										=     text.split()
														else:
																					lowerCAmelCase_										=     self.moses_tokenize(lowercase_			,			self.lang   )

														lowerCAmelCase_										=     []
														for token in text:
																					if token:
																												split_tokens.extend(list(self.bpe(lowercase_   ).split(' '   )   )   )

														return split_tokens

							def      _lowercase       (		self			,			lowercase_   )					->					List[str]:
														'''simple docstring'''

														return self.encoder.get(lowercase_			,			self.encoder.get(self.unk_token   )   )

							def      _lowercase       (		self			,			lowercase_   )					->					Any:
														'''simple docstring'''

														return self.decoder.get(lowercase_			,			self.unk_token   )

							def      _lowercase       (		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     [t.replace(' '			,			''   ).replace('</w>'			,			' '   ) for t in tokens]
														lowerCAmelCase_										=     ''.join(lowercase_   ).split()
														# detokenize
														lowerCAmelCase_										=     self.moses_detokenize(lowercase_			,			self.lang   )
														return text

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					List[int]:
														'''simple docstring'''

														if token_ids_a is None:
																					return [self.sep_token_id] + token_ids_a
														lowerCAmelCase_										=     [self.sep_token_id]
														return sep + token_ids_a + sep + token_ids_a

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = False   )					->					List[int]:
														'''simple docstring'''

														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowercase_			,			token_ids_a=lowercase_			,			already_has_special_tokens=lowercase_   )
														# no bos used in fairseq
														if token_ids_a is not None:
																					return [1] + ([0] * len(lowercase_   )) + [1] + ([0] * len(lowercase_   ))
														return [1] + ([0] * len(lowercase_   ))

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					List[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     [self.sep_token_id]

														# no bos used in fairseq
														if token_ids_a is None:
																					return len(token_ids_a + sep   ) * [0]
														return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]

							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None   )					->					Tuple[str]:
														'''simple docstring'''

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

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

														lowerCAmelCase_										=     0
														with open(lowercase_			,			'w'			,			encoding='utf-8'   ) as writer:
																					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,			key=lambda lowercase_   : 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!'   )
																																			lowerCAmelCase_										=     token_index
																												writer.write(' '.join(lowercase_   ) + '\n'   )
																												index += 1

														return vocab_file, merge_file

							def __getstate__(		self   )					->					Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_										=     self.__dict__.copy()
														lowerCAmelCase_										=     None
														return state






							def __setstate__(		self			,			lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     d

														try:
																					import sacremoses
														except ImportError:
																					raise ImportError(
																					    'You need to install sacremoses to use XLMTokenizer. '
																					    'See https://pypi.org/project/sacremoses/ for installation.'   )

														lowerCAmelCase_										=     sacremoses











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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase_						     =  logging.get_logger(__name__)




class 							a_ (						a_		):


							'''simple docstring'''






							__a:	Optional[Any]     =    ['''pixel_values''']

							def __init__(		self			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = PILImageResampling.BICUBIC			,			lowercase_ = True			,			lowercase_ = 1 / 2_5_5			,			lowercase_ = True			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					None:
														'''simple docstring'''

														super().__init__(**lowercase_   )
														lowerCAmelCase_										=     crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
														lowerCAmelCase_										=     get_size_dict(lowercase_   )

														lowerCAmelCase_										=     do_center_crop
														lowerCAmelCase_										=     crop_size
														lowerCAmelCase_										=     do_resize
														lowerCAmelCase_										=     size
														lowerCAmelCase_										=     resample
														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      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     self.size if size is None else size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )

														lowerCAmelCase_						,   lowerCAmelCase_										=     get_image_size(lowercase_   )
														lowerCAmelCase_										=     min(lowercase_			,			lowercase_   )
														lowerCAmelCase_										=     (size['height'] / crop_size['height']) * min_dim
														lowerCAmelCase_										=     (size['width'] / crop_size['width']) * min_dim
														return center_crop(lowercase_			,			size=(cropped_height, cropped_width)			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = PIL.Image.BICUBIC			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''   )
														return resize(
														    lowercase_			,			size=(size['height'], size['width'])			,			resample=lowercase_			,			data_format=lowercase_			,			**lowercase_   )

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					Any:
														'''simple docstring'''

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

							def      _lowercase       (		self			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ = None			,			**lowercase_			,			)					->					np.ndarray:
														'''simple docstring'''

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






							def      _lowercase       (		self			,			lowercase_			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = None			,			lowercase_ = ChannelDimension.FIRST			,			**lowercase_			,			)					->					PIL.Image.Image:
														'''simple docstring'''

														lowerCAmelCase_										=     do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase_										=     crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase_										=     get_size_dict(lowercase_			,			param_name='crop_size'   )
														lowerCAmelCase_										=     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase_										=     size if size is not None else self.size
														lowerCAmelCase_										=     get_size_dict(lowercase_   )
														lowerCAmelCase_										=     resample if resample is not None else self.resample
														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_										=     make_list_of_images(lowercase_   )

														if not valid_images(lowercase_   ):
																					raise ValueError(
																					    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																					    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

														if do_center_crop and crop_size is None:
																					raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'   )

														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_normalize and (image_mean is None or image_std is None):
																					raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'   )

														# All transformations expect numpy arrays.
														lowerCAmelCase_										=     [to_numpy_array(lowercase_   ) for image in images]

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

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

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

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

														lowerCAmelCase_										=     [to_channel_dimension_format(lowercase_			,			lowercase_   ) for image in images]

														lowerCAmelCase_										=     {'pixel_values': images}
														return BatchFeature(data=lowercase_			,			tensor_type=lowercase_   )



