

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_        =      {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE_        =      {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}

class 				a  (	__a				):
						_lowercase    						=			VOCAB_FILES_NAMES
						_lowercase    						=			PRETRAINED_VOCAB_FILES_MAP
						_lowercase    						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase    						=			["input_ids", "attention_mask"]
						_lowercase    						=			RobertaTokenizer


						def __init__(       self						,					A_=None						,					A_=None						,					A_=None						,					A_="replace"						,					A_="<s>"						,					A_="</s>"						,					A_="</s>"						,					A_="<s>"						,					A_="<unk>"						,					A_="<pad>"						,					A_="<mask>"						,					A_=False						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(
										    a_						,					a_						,					tokenizer_file=a_						,					errors=a_						,					bos_token=a_						,					eos_token=a_						,					sep_token=a_						,					cls_token=a_						,					unk_token=a_						,					pad_token=a_						,					mask_token=a_						,					add_prefix_space=a_						,					trim_offsets=a_						,					**a_						,					)

										_UpperCAmelCase       :		Tuple 		=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get("add_prefix_space"						,					a_	) != add_prefix_space:
														_UpperCAmelCase       :		List[str] 		=    getattr(a_						,					pre_tok_state.pop("type"	)	)
														_UpperCAmelCase       :		int 		=    add_prefix_space
														_UpperCAmelCase       :		Optional[int] 		=    pre_tok_class(**a_	)

										_UpperCAmelCase       :		Dict 		=    add_prefix_space

										_UpperCAmelCase       :		Optional[Any] 		=    """post_processor"""
										_UpperCAmelCase       :		Any 		=    getattr(self.backend_tokenizer						,					a_						,					a_	)
										if tokenizer_component_instance:
														_UpperCAmelCase       :		List[str] 		=    json.loads(tokenizer_component_instance.__getstate__()	)

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																		_UpperCAmelCase       :		Any 		=    tuple(state["sep"]	)
														if "cls" in state:
																		_UpperCAmelCase       :		List[str] 		=    tuple(state["cls"]	)

														_UpperCAmelCase       :		List[Any] 		=    False

														if state.get("add_prefix_space"						,					a_	) != add_prefix_space:
																		_UpperCAmelCase       :		Dict 		=    add_prefix_space
																		_UpperCAmelCase       :		Any 		=    True

														if state.get("trim_offsets"						,					a_	) != trim_offsets:
																		_UpperCAmelCase       :		int 		=    trim_offsets
																		_UpperCAmelCase       :		Tuple 		=    True

														if changes_to_apply:
																		_UpperCAmelCase       :		Optional[Any] 		=    getattr(a_						,					state.pop("type"	)	)
																		_UpperCAmelCase       :		Tuple 		=    component_class(**a_	)
																		setattr(self.backend_tokenizer						,					a_						,					a_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if self._mask_token is None:
														if self.verbose:
																		logger.error("Using mask_token, but it is not set yet."	)
														return None
										return str(self._mask_token	)


						@mask_token.setter
						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    AddedToken(a_						,					lstrip=a_						,					rstrip=a_	) if isinstance(a_						,					a_	) else value
										_UpperCAmelCase       :		Tuple 		=    value


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    kwargs.get("is_split_into_words"						,					a_	)
										assert self.add_prefix_space or not is_split_into_words, (
										    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*a_						,					**a_	)


						def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    kwargs.get("is_split_into_words"						,					a_	)

										assert self.add_prefix_space or not is_split_into_words, (
										    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*a_						,					**a_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self._tokenizer.model.save(a_						,					name=a_	)
										return tuple(a_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_=None	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    [self.sep_token_id]
										_UpperCAmelCase       :		Optional[Any] 		=    [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]





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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Union[str, Any] )   ->       Union[str, Any]:
				_UpperCAmelCase       :		Dict 		=    b.T
				_UpperCAmelCase       :		Dict 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=1 )
				_UpperCAmelCase       :		Optional[Any] 		=    np.sum(np.square(lowerCAmelCase )  ,      axis=0 )
				_UpperCAmelCase       :		str 		=    np.matmul(lowerCAmelCase  ,      lowerCAmelCase )
				_UpperCAmelCase       :		Any 		=    aa[:, None] - 2 * ab + ba[None, :]
				return d

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Dict )   ->       int:
				_UpperCAmelCase       :		Any 		=    x.reshape(-1  ,      3 )
				_UpperCAmelCase       :		List[str] 		=    squared_euclidean_distance(lowerCAmelCase  ,      lowerCAmelCase )
				return np.argmin(lowerCAmelCase  ,      axis=1 )





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["pixel_values"]


						def __init__(       self						,					A_ = None						,					A_ = True						,					A_ = None						,					A_ = PILImageResampling.BILINEAR						,					A_ = True						,					A_ = True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    size if size is not None else {"height": 256, "width": 256}
										_UpperCAmelCase       :		Optional[int] 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    np.array(A_	) if clusters is not None else None
										_UpperCAmelCase       :		int 		=    do_resize
										_UpperCAmelCase       :		Union[str, Any] 		=    size
										_UpperCAmelCase       :		Optional[Any] 		=    resample
										_UpperCAmelCase       :		str 		=    do_normalize
										_UpperCAmelCase       :		List[str] 		=    do_color_quantize


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BILINEAR						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    get_size_dict(A_	)
										if "height" not in size or "width" not in size:
														raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'	)
										return resize(
										    A_						,					size=(size["height"], size["width"])						,					resample=A_						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    rescale(image=A_						,					scale=1 / 1_27.5						,					data_format=A_	)
										_UpperCAmelCase       :		List[Any] 		=    image - 1
										return image





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = ChannelDimension.FIRST						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase       :		Any 		=    size if size is not None else self.size
										_UpperCAmelCase       :		Dict 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		List[Any] 		=    resample if resample is not None else self.resample
										_UpperCAmelCase       :		Any 		=    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase       :		Optional[Any] 		=    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										_UpperCAmelCase       :		Any 		=    clusters if clusters is not None else self.clusters
										_UpperCAmelCase       :		Optional[int] 		=    np.array(A_	)

										_UpperCAmelCase       :		List[str] 		=    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_color_quantize and clusters is None:
														raise ValueError("Clusters must be specified if do_color_quantize is True."	)

										# All transformations expect numpy arrays.
										_UpperCAmelCase       :		List[str] 		=    [to_numpy_array(A_	) for image in images]

										if do_resize:
														_UpperCAmelCase       :		int 		=    [self.resize(image=A_						,					size=A_						,					resample=A_	) for image in images]

										if do_normalize:
														_UpperCAmelCase       :		List[str] 		=    [self.normalize(image=A_	) for image in images]

										if do_color_quantize:
														_UpperCAmelCase       :		Tuple 		=    [to_channel_dimension_format(A_						,					ChannelDimension.LAST	) for image in images]
														# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
														_UpperCAmelCase       :		List[str] 		=    np.array(A_	)
														_UpperCAmelCase       :		List[Any] 		=    color_quantize(A_						,					A_	).reshape(images.shape[:-1]	)

														# flatten to (batch_size, height*width)
														_UpperCAmelCase       :		Any 		=    images.shape[0]
														_UpperCAmelCase       :		List[Any] 		=    images.reshape(A_						,					-1	)

														# We need to convert back to a list of images to keep consistent behaviour across processors.
														_UpperCAmelCase       :		Union[str, Any] 		=    list(A_	)
										else:
														_UpperCAmelCase       :		Optional[Any] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

										_UpperCAmelCase       :		List[Any] 		=    {"input_ids": images}
										return BatchFeature(data=A_						,					tensor_type=A_	)


