'''simple docstring'''

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_longformer import LongformerTokenizer


A_	   = logging.get_logger(__name__)

A_	   = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A_	   = {
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

A_	   = {
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Union[str, Any]   							=       VOCAB_FILES_NAMES
			__magic_name__ :				str   							=       PRETRAINED_VOCAB_FILES_MAP
			__magic_name__ :				Optional[Any]   							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__magic_name__ :				str   							=       ["""input_ids""", """attention_mask"""]
			__magic_name__ :				Optional[Any]   							=       LongformerTokenizer



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			snake_case_="replace"				,			snake_case_="<s>"				,			snake_case_="</s>"				,			snake_case_="</s>"				,			snake_case_="<s>"				,			snake_case_="<unk>"				,			snake_case_="<pad>"				,			snake_case_="<mask>"				,			snake_case_=False				,			snake_case_=True				,			**snake_case_				,			):
									super().__init__(
									    snake_case_				,			snake_case_				,			tokenizer_file=snake_case_				,			errors=snake_case_				,			bos_token=snake_case_				,			eos_token=snake_case_				,			sep_token=snake_case_				,			cls_token=snake_case_				,			unk_token=snake_case_				,			pad_token=snake_case_				,			mask_token=snake_case_				,			add_prefix_space=snake_case_				,			trim_offsets=snake_case_				,			**snake_case_				,			)

									snake_case :       Dict				=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
									if pre_tok_state.get("add_prefix_space"				,			snake_case_					) != add_prefix_space:
															snake_case :       str				=    getattr(snake_case_				,			pre_tok_state.pop("type"					)					)
															snake_case :       List[Any]				=    add_prefix_space
															snake_case :       Dict				=    pre_tok_class(**snake_case_					)

									snake_case :       Any				=    add_prefix_space

									snake_case :       int				=    "post_processor"
									snake_case :       str				=    getattr(self.backend_tokenizer				,			snake_case_				,			snake_case_					)
									if tokenizer_component_instance:
															snake_case :       Optional[Any]				=    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:
																					snake_case :       str				=    tuple(state["sep"]					)
															if "cls" in state:
																					snake_case :       List[str]				=    tuple(state["cls"]					)

															snake_case :       Tuple				=    False

															if state.get("add_prefix_space"				,			snake_case_					) != add_prefix_space:
																					snake_case :       Optional[int]				=    add_prefix_space
																					snake_case :       Optional[int]				=    True

															if state.get("trim_offsets"				,			snake_case_					) != trim_offsets:
																					snake_case :       Union[str, Any]				=    trim_offsets
																					snake_case :       int				=    True

															if changes_to_apply:
																					snake_case :       Dict				=    getattr(snake_case_				,			state.pop("type"					)					)
																					snake_case :       int				=    component_class(**snake_case_					)
																					setattr(self.backend_tokenizer				,			snake_case_				,			snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									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   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    AddedToken(snake_case_				,			lstrip=snake_case_				,			rstrip=snake_case_					) if isinstance(snake_case_				,			snake_case_					) else value
									snake_case :       Optional[int]				=    value



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									snake_case :       Optional[int]				=    kwargs.get("is_split_into_words"				,			snake_case_					)
									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(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									snake_case :       str				=    kwargs.get("is_split_into_words"				,			snake_case_					)

									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(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = None					):
									snake_case :       List[str]				=    self._tokenizer.model.save(snake_case_				,			name=snake_case_					)
									return tuple(snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_=None					):
									snake_case :       Union[str, Any]				=    [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   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = None					):
									snake_case :       Union[str, Any]				=    [self.sep_token_id]
									snake_case :       List[str]				=    [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]






'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






			def __call__(							self				,			snake_case_					):
									snake_case :       Optional[Any]				=    []
									for img in imgs:
															snake_case				,					snake_case :       Dict				=    img.shape[:2]
															# later: provide list and randomly choose index for resize
															snake_case :       Dict				=    np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1					)
															if size == 0:
																					return img
															snake_case :       Any				=    size * 1.0 / min(snake_case_				,			snake_case_					)
															if h < w:
																					snake_case				,					snake_case :       Optional[int]				=    size, scale * w
															else:
																					snake_case				,					snake_case :       Tuple				=    scale * h, size
															if max(snake_case_				,			snake_case_					) > self.max_size:
																					snake_case :       Optional[int]				=    self.max_size * 1.0 / max(snake_case_				,			snake_case_					)
																					snake_case :       Dict				=    newh * scale
																					snake_case :       List[Any]				=    neww * scale
															snake_case :       int				=    int(neww + 0.5					)
															snake_case :       List[Any]				=    int(newh + 0.5					)

															if img.dtype == np.uinta:
																					snake_case :       List[Any]				=    Image.fromarray(snake_case_					)
																					snake_case :       str				=    pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR					)
																					snake_case :       List[str]				=    np.asarray(snake_case_					)
															else:
																					snake_case :       List[Any]				=    img.permute(2				,			0				,			1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																					snake_case :       Tuple				=    nn.functional.interpolate(
																					    snake_case_				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=snake_case_					).squeeze(0					)
															img_augs.append(snake_case_					)

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





