





'''simple docstring'''



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE   =      logging.getLogger(__name__)






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			str
	a      :			List[str]
	a      :			Optional[List[str]]






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			List[int]
	a      :			List[int]
	a      :			Optional[List[int]]     =    None
	a      :			Optional[List[int]]     =    None






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			List[str]     =    "train"
	a      :			Optional[int]     =    "dev"
	a      :			Optional[Any]     =    "test"






class 							__lowercase    :







	'''simple docstring'''

	@staticmethod
	def   _UpperCAmelCase       (_lowerCamelCase       ,_lowerCamelCase	)							->							List[InputExample]:







				'''simple docstring'''





				raise NotImplementedError


	@staticmethod
	def   _UpperCAmelCase       (_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





				raise NotImplementedError


	@staticmethod
	def   _UpperCAmelCase       (_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=False       ,_lowerCamelCase="[CLS]"       ,_lowerCamelCase=1       ,_lowerCamelCase="[SEP]"       ,_lowerCamelCase=False       ,_lowerCamelCase=False       ,_lowerCamelCase=0       ,_lowerCamelCase=0       ,_lowerCamelCase=-100       ,_lowerCamelCase=0       ,_lowerCamelCase=True       ,)							->							List[InputFeatures]:







				'''simple docstring'''





				__lowercase			  =							{label: i for i, label in enumerate(_lowerCamelCase	)}

				__lowercase			  =							[]
				for ex_index, example in enumerate(_lowerCamelCase	):
							if ex_index % 10000 == 0:
										logger.info('''Writing example %d of %d'''       ,_lowerCamelCase       ,len(_lowerCamelCase	)	)

							__lowercase			  =							[]
							__lowercase			  =							[]
							for word, label in zip(example.words       ,example.labels	):
										__lowercase			  =							tokenizer.tokenize(_lowerCamelCase	)

										# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
										if len(_lowerCamelCase	) > 0:
													tokens.extend(_lowerCamelCase	)
													# Use the real label id for the first token of the word, and padding ids for the remaining tokens
													label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_lowerCamelCase	) - 1)	)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
							__lowercase			  =							tokenizer.num_special_tokens_to_add()
							if len(_lowerCamelCase	) > max_seq_length - special_tokens_count:
										__lowercase			  =							tokens[: (max_seq_length - special_tokens_count)]
										__lowercase			  =							label_ids[: (max_seq_length - special_tokens_count)]

							# The convention in BERT is:
							# (a) For sequence pairs:
							#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
							#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
							# (b) For single sequences:
							#  tokens:   [CLS] the dog is hairy . [SEP]
							#  type_ids:   0   0   0   0  0     0   0
							#
							# Where "type_ids" are used to indicate whether this is the first
							# sequence or the second sequence. The embedding vectors for `type=0` and
							# `type=1` were learned during pre-training and are added to the wordpiece
							# embedding vector (and position vector). This is not *strictly* necessary
							# since the [SEP] token unambiguously separates the sequences, but it makes
							# it easier for the model to learn the concept of sequences.
							#
							# For classification tasks, the first vector (corresponding to [CLS]) is
							# used as the "sentence vector". Note that this only makes sense because
							# the entire model is fine-tuned.
							tokens += [sep_token]
							label_ids += [pad_token_label_id]
							if sep_token_extra:
										# roberta uses an extra separator b/w pairs of sentences
										tokens += [sep_token]
										label_ids += [pad_token_label_id]
							__lowercase			  =							[sequence_a_segment_id] * len(_lowerCamelCase	)

							if cls_token_at_end:
										tokens += [cls_token]
										label_ids += [pad_token_label_id]
										segment_ids += [cls_token_segment_id]
							else:
										__lowercase			  =							[cls_token] + tokens
										__lowercase			  =							[pad_token_label_id] + label_ids
										__lowercase			  =							[cls_token_segment_id] + segment_ids

							__lowercase			  =							tokenizer.convert_tokens_to_ids(_lowerCamelCase	)

							# The mask has 1 for real tokens and 0 for padding tokens. Only real
							# tokens are attended to.
							__lowercase			  =							[1 if mask_padding_with_zero else 0] * len(_lowerCamelCase	)

							# Zero-pad up to the sequence length.
							__lowercase			  =							max_seq_length - len(_lowerCamelCase	)
							if pad_on_left:
										__lowercase			  =							([pad_token] * padding_length) + input_ids
										__lowercase			  =							([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
										__lowercase			  =							([pad_token_segment_id] * padding_length) + segment_ids
										__lowercase			  =							([pad_token_label_id] * padding_length) + label_ids
							else:
										input_ids += [pad_token] * padding_length
										input_mask += [0 if mask_padding_with_zero else 1] * padding_length
										segment_ids += [pad_token_segment_id] * padding_length
										label_ids += [pad_token_label_id] * padding_length

							assert len(_lowerCamelCase	) == max_seq_length
							assert len(_lowerCamelCase	) == max_seq_length
							assert len(_lowerCamelCase	) == max_seq_length
							assert len(_lowerCamelCase	) == max_seq_length

							if ex_index < 5:
										logger.info('''*** Example ***'''	)
										logger.info('''guid: %s'''       ,example.guid	)
										logger.info('''tokens: %s'''       ,''' '''.join([str(_lowerCamelCase	) for x in tokens]	)	)
										logger.info('''input_ids: %s'''       ,''' '''.join([str(_lowerCamelCase	) for x in input_ids]	)	)
										logger.info('''input_mask: %s'''       ,''' '''.join([str(_lowerCamelCase	) for x in input_mask]	)	)
										logger.info('''segment_ids: %s'''       ,''' '''.join([str(_lowerCamelCase	) for x in segment_ids]	)	)
										logger.info('''label_ids: %s'''       ,''' '''.join([str(_lowerCamelCase	) for x in label_ids]	)	)

							if "token_type_ids" not in tokenizer.model_input_names:
										__lowercase			  =							None

							features.append(
							    InputFeatures(
							        input_ids=_lowerCamelCase       ,attention_mask=_lowerCamelCase       ,token_type_ids=_lowerCamelCase       ,label_ids=_lowerCamelCase	)	)
				return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset






	class 							__lowercase    ( lowerCAmelCase__  ):







		'''simple docstring'''

		a      :			List[InputFeatures]
		a      :			int     =    nn.CrossEntropyLoss().ignore_index


		def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase=False       ,_lowerCamelCase = Split.train       ,)							->							List[Any]:







					'''simple docstring'''





					__lowercase			  =							os.path.join(
					    _lowerCamelCase       ,'''cached_{}_{}_{}'''.format(mode.value       ,tokenizer.__class__.__name__       ,str(_lowerCamelCase	)	)       ,)

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					__lowercase			  =							cached_features_file + '''.lock'''
					with FileLock(_lowerCamelCase	):
								if os.path.exists(_lowerCamelCase	) and not overwrite_cache:
											logger.info(f"Loading features from cached file {cached_features_file}"	)
											__lowercase			  =							torch.load(_lowerCamelCase	)
								else:
											logger.info(f"Creating features from dataset file at {data_dir}"	)
											__lowercase			  =							token_classification_task.read_examples_from_file(_lowerCamelCase       ,_lowerCamelCase	)
											# TODO clean up all this to leverage built-in features of tokenizers
											__lowercase			  =							token_classification_task.convert_examples_to_features(
											    _lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,cls_token_at_end=bool(model_type in ['''xlnet''']	)       ,cls_token=tokenizer.cls_token       ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0       ,sep_token=tokenizer.sep_token       ,sep_token_extra=_lowerCamelCase       ,pad_on_left=bool(tokenizer.padding_side == '''left'''	)       ,pad_token=tokenizer.pad_token_id       ,pad_token_segment_id=tokenizer.pad_token_type_id       ,pad_token_label_id=self.pad_token_label_id       ,)
											logger.info(f"Saving features into cached file {cached_features_file}"	)
											torch.save(self.features       ,_lowerCamelCase	)


		def __len__(self	)							->							Dict:







					'''simple docstring'''





					return len(self.features	)


		def __getitem__(self       ,_lowerCamelCase	)							->							InputFeatures:







					'''simple docstring'''





					return self.features[i]


if is_tf_available():
	import tensorflow as tf






	class 							__lowercase    :







		'''simple docstring'''

		a      :			List[InputFeatures]
		a      :			int     =    -100


		def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase=False       ,_lowerCamelCase = Split.train       ,)							->							Dict:







					'''simple docstring'''





					__lowercase			  =							token_classification_task.read_examples_from_file(_lowerCamelCase       ,_lowerCamelCase	)
					# TODO clean up all this to leverage built-in features of tokenizers
					__lowercase			  =							token_classification_task.convert_examples_to_features(
					    _lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,cls_token_at_end=bool(model_type in ['''xlnet''']	)       ,cls_token=tokenizer.cls_token       ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0       ,sep_token=tokenizer.sep_token       ,sep_token_extra=_lowerCamelCase       ,pad_on_left=bool(tokenizer.padding_side == '''left'''	)       ,pad_token=tokenizer.pad_token_id       ,pad_token_segment_id=tokenizer.pad_token_type_id       ,pad_token_label_id=self.pad_token_label_id       ,)

					def gen():
								for ex in self.features:
											if ex.token_type_ids is None:
														yield (
														    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
														    ex.label_ids,
														)
											else:
														yield (
														    {
														        "input_ids": ex.input_ids,
														        "attention_mask": ex.attention_mask,
														        "token_type_ids": ex.token_type_ids,
														    },
														    ex.label_ids,
														)

					if "token_type_ids" not in tokenizer.model_input_names:
								__lowercase			  =							tf.data.Dataset.from_generator(
								    _lowerCamelCase       ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)       ,(
								        {'''input_ids''': tf.TensorShape([None]	), '''attention_mask''': tf.TensorShape([None]	)},
								        tf.TensorShape([None]	),
								    )       ,)
					else:
								__lowercase			  =							tf.data.Dataset.from_generator(
								    _lowerCamelCase       ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)       ,(
								        {
								            '''input_ids''': tf.TensorShape([None]	),
								            '''attention_mask''': tf.TensorShape([None]	),
								            '''token_type_ids''': tf.TensorShape([None]	),
								        },
								        tf.TensorShape([None]	),
								    )       ,)


		def   _UpperCAmelCase       (self	)							->							List[str]:







					'''simple docstring'''





					__lowercase			  =							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features	)	)	)

					return self.dataset


		def __len__(self	)							->							str:







					'''simple docstring'''





					return len(self.features	)


		def __getitem__(self       ,_lowerCamelCase	)							->							InputFeatures:







					'''simple docstring'''





					return self.features[i]












'''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 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase=sys.maxsize	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							'''bilinear'''
				__lowercase			  =							max_size
				__lowercase			  =							short_edge_length


	def __call__(self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





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

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

				return img_augs






class 							__lowercase    :







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,cfg.INPUT.MAX_SIZE_TEST	)
				__lowercase			  =							cfg.INPUT.FORMAT
				__lowercase			  =							cfg.SIZE_DIVISIBILITY
				__lowercase			  =							cfg.PAD_VALUE
				__lowercase			  =							cfg.INPUT.MAX_SIZE_TEST
				__lowercase			  =							cfg.MODEL.DEVICE
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)       ,1       ,1	)
				__lowercase			  =							lambda _lowerCamelCase	: (x - self.pixel_mean) / self.pixel_std


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							tuple(max(_lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
				__lowercase			  =							[im.shape[-2:] for im in images]
				__lowercase			  =							[
				    nn.functional.pad(
				        _lowerCamelCase       ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,value=self.pad_value       ,)
				    for size, im in zip(_lowerCamelCase       ,_lowerCamelCase	)
				]

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


	def __call__(self       ,_lowerCamelCase       ,_lowerCamelCase=False	)							->							Tuple:







				'''simple docstring'''





				with torch.no_grad():
							if not isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										__lowercase			  =							[images]
							if single_image:
										assert len(_lowerCamelCase	) == 1
							for i in range(len(_lowerCamelCase	)	):
										if isinstance(images[i]       ,torch.Tensor	):
													images.insert(_lowerCamelCase       ,images.pop(_lowerCamelCase	).to(self.device	).float()	)
										elif not isinstance(images[i]       ,torch.Tensor	):
													images.insert(
													    _lowerCamelCase       ,torch.as_tensor(img_tensorize(images.pop(_lowerCamelCase	)       ,input_format=self.input_format	)	)
													    .to(self.device	)
													    .float()       ,)
            # resize smallest edge
							__lowercase			  =							torch.tensor([im.shape[:2] for im in images]	)
							__lowercase			  =							self.aug(_lowerCamelCase	)
							# 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
							__lowercase			  =							[self.normalizer(_lowerCamelCase	) for x in images]
							# now pad them to do the following operations
							__lowercase  ,						__lowercase			  =							self.pad(_lowerCamelCase	)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							__lowercase			  =							torch.true_divide(_lowerCamelCase       ,_lowerCamelCase	)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Tuple			):
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Tuple[int, int]			):
			assert torch.isfinite(lowerCamelCase_			).all(), "Box tensor contains infinite or NaN!"
			__lowercase  ,						__lowercase			  =							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_			)





