


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

import regex as re

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


_UpperCAmelCase			:  int     =logging.get_logger(__name__)


_UpperCAmelCase			:  List[Any]     ={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_UpperCAmelCase			:  Optional[int]     ={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

_UpperCAmelCase			:  Union[str, Any]     ={"""facebook/blenderbot_small-90M""": 512}



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Any:
				lowerCAmelCase_ :      Optional[Any]    				=				set()
				lowerCAmelCase_ :      List[Any]    				=				word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								lowerCAmelCase_ :      int    				=				char

				lowerCAmelCase_ :      str    				=				set(lowerCAmelCase_ )
				return pairs







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  int					=							VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE__      :  List[str]					=							PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE__      :  List[Any]					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							["""input_ids""", """attention_mask"""]




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase="__start__"   ,						__lowercase="__end__"   ,						__lowercase="__unk__"   ,						__lowercase="__null__"   ,						**__lowercase   ,						)					->    Optional[Any]:
										super().__init__(unk_token=__lowercase   ,						bos_token=__lowercase   ,						eos_token=__lowercase   ,						pad_token=__lowercase   ,						**__lowercase							)

										with open(__lowercase   ,						encoding='''utf-8'''							) as vocab_handle:
														lowerCAmelCase_ :      str    				=				json.load(__lowercase							)
										lowerCAmelCase_ :      Any    				=				{v: k for k, v in self.encoder.items()}
										with open(__lowercase   ,						encoding='''utf-8'''							) as merges_handle:
														lowerCAmelCase_ :      Dict    				=				merges_handle.read().split('''\n'''							)[1:-1]
										lowerCAmelCase_ :      Dict    				=				[tuple(merge.split()							) for merge in merges]
										lowerCAmelCase_ :      int    				=				dict(zip(__lowercase   ,						range(len(__lowercase							)							)							)							)
										lowerCAmelCase_ :      Optional[Any]    				=				{}




						@property
						def 						lowercase_ (				self							)					->    int:
										return len(self.encoder							)




						def 						lowercase_ (				self							)					->    Dict:
										return dict(self.encoder   ,						**self.added_tokens_encoder							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    str:
										if token in self.cache:
														return self.cache[token]
										lowerCAmelCase_ :      int    				=				re.sub('''([.,!?()])'''   ,						R''' \1'''   ,						__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				re.sub('''(\')'''   ,						R''' \1 '''   ,						__lowercase							)
										lowerCAmelCase_ :      Optional[int]    				=				re.sub(R'''\s{2,}'''   ,						''' '''   ,						__lowercase							)
										if "\n" in token:
														lowerCAmelCase_ :      Any    				=				token.replace('''\n'''   ,						''' __newln__'''							)

										lowerCAmelCase_ :      List[str]    				=				token.split(''' '''							)
										lowerCAmelCase_ :      List[Any]    				=				[]
										for token in tokens:
														if not len(__lowercase							):
																		continue

														lowerCAmelCase_ :      Union[str, Any]    				=				token.lower()
														lowerCAmelCase_ :      Optional[int]    				=				tuple(__lowercase							)
														lowerCAmelCase_ :      str    				=				tuple(list(word[:-1]							) + [word[-1] + '''</w>''']							)
														lowerCAmelCase_ :      Optional[int]    				=				get_pairs(__lowercase							)

														if not pairs:
																		words.append(__lowercase							)
																		continue

														while True:
																		lowerCAmelCase_ :      int    				=				min(__lowercase   ,						key=lambda __lowercase							: self.bpe_ranks.get(__lowercase   ,						float('''inf'''							)							)							)
																		if bigram not in self.bpe_ranks:
																						break
																		lowerCAmelCase_ :      Dict    				=				bigram
																		lowerCAmelCase_ :      Union[str, Any]    				=				[]
																		lowerCAmelCase_ :      str    				=				0

																		while i < len(__lowercase							):
																						try:
																										lowerCAmelCase_ :      Optional[Any]    				=				word.index(__lowercase   ,						__lowercase							)
																										new_word.extend(word[i:j]							)
																										lowerCAmelCase_ :      str    				=				j
																						except ValueError:
																										new_word.extend(word[i:]							)
																										break

																						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_ :      Union[str, Any]    				=				tuple(__lowercase							)
																		lowerCAmelCase_ :      Union[str, Any]    				=				new_word
																		if len(__lowercase							) == 1:
																						break
																		else:
																						lowerCAmelCase_ :      Dict    				=				get_pairs(__lowercase							)
														lowerCAmelCase_ :      Dict    				=				'''@@ '''.join(__lowercase							)
														lowerCAmelCase_ :      Optional[Any]    				=				word[:-4]

														lowerCAmelCase_ :      Optional[int]    				=				word
														words.append(__lowercase							)
										return " ".join(__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    List[str]:
										lowerCAmelCase_ :      Optional[int]    				=				[]

										lowerCAmelCase_ :      List[Any]    				=				re.findall(R'''\S+\n?'''   ,						__lowercase							)

										for token in words:
														split_tokens.extend(list(self.bpe(__lowercase							).split(''' '''							)							)							)
										return split_tokens




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										lowerCAmelCase_ :      int    				=				token.lower()
										return self.encoder.get(__lowercase   ,						self.encoder.get(self.unk_token							)							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    str:
										return self.decoder.get(__lowercase   ,						self.unk_token							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      int    				=				''' '''.join(__lowercase							).replace('''@@ '''   ,						''''''							).strip()
										return out_string




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None							)					->    Tuple[str]:
										if not os.path.isdir(__lowercase							):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
														return
										lowerCAmelCase_ :      Dict    				=				os.path.join(
										    __lowercase   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
										lowerCAmelCase_ :      Tuple    				=				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_ :      Optional[int]    				=				0
										with open(__lowercase   ,						'''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 __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_ :      Tuple    				=				token_index
																		writer.write(''' '''.join(__lowercase							) + '''\n'''							)
																		index += 1

										return vocab_file, merge_file



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_STANDARD_MEAN,
    IMAGENET_STANDARD_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			:  Any     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Dict					=							["""pixel_values"""]




						def __init__(				self   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = PILImageResampling.BILINEAR   ,						__lowercase = True   ,						__lowercase = 1 / 2_5_5   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						**__lowercase   ,						)					->    None:
										super().__init__(**__lowercase							)
										lowerCAmelCase_ :      Dict    				=				size if size is not None else {'''shortest_edge''': 3_8_4}
										lowerCAmelCase_ :      Optional[Any]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)

										lowerCAmelCase_ :      List[Any]    				=				do_resize
										lowerCAmelCase_ :      Optional[int]    				=				size
										# Default value set here for backwards compatibility where the value in config is None
										lowerCAmelCase_ :      str    				=				crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
										lowerCAmelCase_ :      Tuple    				=				resample
										lowerCAmelCase_ :      Optional[int]    				=				do_rescale
										lowerCAmelCase_ :      Any    				=				rescale_factor
										lowerCAmelCase_ :      List[str]    				=				do_normalize
										lowerCAmelCase_ :      str    				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowerCAmelCase_ :      Dict    				=				image_std if image_std is not None else IMAGENET_STANDARD_STD




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = PILImageResampling.BICUBIC   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										lowerCAmelCase_ :      Optional[Any]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)
										if "shortest_edge" not in size:
														raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""							)
										lowerCAmelCase_ :      Optional[int]    				=				size['''shortest_edge''']

										if shortest_edge < 3_8_4:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														lowerCAmelCase_ :      Optional[Any]    				=				int(shortest_edge / crop_pct							)
														lowerCAmelCase_ :      List[str]    				=				get_resize_output_image_size(__lowercase   ,						size=__lowercase   ,						default_to_square=__lowercase							)
														lowerCAmelCase_ :      List[Any]    				=				resize(image=__lowercase   ,						size=__lowercase   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=__lowercase   ,						size=(shortest_edge, shortest_edge)   ,						data_format=__lowercase   ,						**__lowercase							)
										else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    __lowercase   ,						size=(shortest_edge, shortest_edge)   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    Any:
										return rescale(__lowercase   ,						scale=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										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 = ChannelDimension.FIRST   ,						**__lowercase   ,						)					->    PIL.Image.Image:
										lowerCAmelCase_ :      Optional[int]    				=				do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_ :      Any    				=				crop_pct if crop_pct is not None else self.crop_pct
										lowerCAmelCase_ :      str    				=				resample if resample is not None else self.resample
										lowerCAmelCase_ :      Union[str, Any]    				=				do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_ :      str    				=				rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCAmelCase_ :      Any    				=				do_normalize if do_normalize is not None else self.do_normalize
										lowerCAmelCase_ :      str    				=				image_mean if image_mean is not None else self.image_mean
										lowerCAmelCase_ :      int    				=				image_std if image_std is not None else self.image_std

										lowerCAmelCase_ :      int    				=				size if size is not None else self.size
										lowerCAmelCase_ :      List[str]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)

										lowerCAmelCase_ :      Tuple    				=				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_resize and size is None or resample is None:
														raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

										if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
														raise ValueError('''crop_pct must be specified if size < 384.'''							)

										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_ :      Optional[Any]    				=				[to_numpy_array(__lowercase							) for image in images]

										if do_resize:
														lowerCAmelCase_ :      Union[str, Any]    				=				[self.resize(image=__lowercase   ,						size=__lowercase   ,						crop_pct=__lowercase   ,						resample=__lowercase							) for image in images]

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

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

										lowerCAmelCase_ :      Optional[Any]    				=				[to_channel_dimension_format(__lowercase   ,						__lowercase							) for image in images]

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