
"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case          =						logging.get_logger(__name__)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														_lowerCAmelCase  	=			192
														_lowerCAmelCase  	=			768
														_lowerCAmelCase  	=			12
														_lowerCAmelCase  	=			3
														_lowerCAmelCase  	=			[800, 1333]
														_lowerCAmelCase  	=			False
							elif yolos_name == "yolos_s_dWr":
														_lowerCAmelCase  	=			330
														_lowerCAmelCase  	=			14
														_lowerCAmelCase  	=			6
														_lowerCAmelCase  	=			1320
							elif "yolos_s" in yolos_name:
														_lowerCAmelCase  	=			384
														_lowerCAmelCase  	=			1536
														_lowerCAmelCase  	=			12
														_lowerCAmelCase  	=			6
							elif "yolos_b" in yolos_name:
														_lowerCAmelCase  	=			[800, 1344]

							_lowerCAmelCase  	=			91
							_lowerCAmelCase  	=			'huggingface/label-files'
							_lowerCAmelCase  	=			'coco-detection-id2label.json'
							_lowerCAmelCase  	=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  repo_type='dataset'						)     ,  'r'						)						)
							_lowerCAmelCase  	=			{int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
							_lowerCAmelCase  	=			idalabel
							_lowerCAmelCase  	=			{v: k for k, v in idalabel.items()}

							return config






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					dict     ,  SCREAMING_SNAKE_CASE:					YolosConfig     ,  SCREAMING_SNAKE_CASE:					bool = False						):






							"""simple docstring"""



							for i in range(config.num_hidden_layers						):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														_lowerCAmelCase  	=			state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""						)
														_lowerCAmelCase  	=			state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""						)
														# next, add query, keys and values (in that order) to the state dict
														_lowerCAmelCase  	=			in_proj_weight[: config.hidden_size, :]
														_lowerCAmelCase  	=			in_proj_bias[: config.hidden_size]
														_lowerCAmelCase  	=			in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														_lowerCAmelCase  	=			in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														_lowerCAmelCase  	=			in_proj_weight[-config.hidden_size :, :]
														_lowerCAmelCase  	=			in_proj_bias[-config.hidden_size :]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							if "backbone" in name:
														_lowerCAmelCase  	=			name.replace('backbone'     ,  'vit'						)
							if "cls_token" in name:
														_lowerCAmelCase  	=			name.replace('cls_token'     ,  'embeddings.cls_token'						)
							if "det_token" in name:
														_lowerCAmelCase  	=			name.replace('det_token'     ,  'embeddings.detection_tokens'						)
							if "mid_pos_embed" in name:
														_lowerCAmelCase  	=			name.replace('mid_pos_embed'     ,  'encoder.mid_position_embeddings'						)
							if "pos_embed" in name:
														_lowerCAmelCase  	=			name.replace('pos_embed'     ,  'embeddings.position_embeddings'						)
							if "patch_embed.proj" in name:
														_lowerCAmelCase  	=			name.replace('patch_embed.proj'     ,  'embeddings.patch_embeddings.projection'						)
							if "blocks" in name:
														_lowerCAmelCase  	=			name.replace('blocks'     ,  'encoder.layer'						)
							if "attn.proj" in name:
														_lowerCAmelCase  	=			name.replace('attn.proj'     ,  'attention.output.dense'						)
							if "attn" in name:
														_lowerCAmelCase  	=			name.replace('attn'     ,  'attention.self'						)
							if "norm1" in name:
														_lowerCAmelCase  	=			name.replace('norm1'     ,  'layernorm_before'						)
							if "norm2" in name:
														_lowerCAmelCase  	=			name.replace('norm2'     ,  'layernorm_after'						)
							if "mlp.fc1" in name:
														_lowerCAmelCase  	=			name.replace('mlp.fc1'     ,  'intermediate.dense'						)
							if "mlp.fc2" in name:
														_lowerCAmelCase  	=			name.replace('mlp.fc2'     ,  'output.dense'						)
							if "class_embed" in name:
														_lowerCAmelCase  	=			name.replace('class_embed'     ,  'class_labels_classifier'						)
							if "bbox_embed" in name:
														_lowerCAmelCase  	=			name.replace('bbox_embed'     ,  'bbox_predictor'						)
							if "vit.norm" in name:
														_lowerCAmelCase  	=			name.replace('vit.norm'     ,  'vit.layernorm'						)

							return name






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					dict     ,  SCREAMING_SNAKE_CASE:					YolosForObjectDetection						):






							"""simple docstring"""



							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase  	=			orig_state_dict.pop(SCREAMING_SNAKE_CASE						)

														if "qkv" in key:
																					_lowerCAmelCase  	=			key.split('.'						)
																					_lowerCAmelCase  	=			int(key_split[2]						)
																					_lowerCAmelCase  	=			model.vit.encoder.layer[layer_num].attention.attention.all_head_size
																					if "weight" in key:
																												_lowerCAmelCase  	=			val[:dim, :]
																												_lowerCAmelCase  	=			val[
																												    dim : dim * 2, :
																												]
																												_lowerCAmelCase  	=			val[-dim:, :]
																					else:
																												_lowerCAmelCase  	=			val[:dim]
																												_lowerCAmelCase  	=			val[dim : dim * 2]
																												_lowerCAmelCase  	=			val[-dim:]
														else:
																					_lowerCAmelCase  	=			val

							return orig_state_dict






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowerCAmelCase  	=			Image.open(requests.get(SCREAMING_SNAKE_CASE     ,  stream=SCREAMING_SNAKE_CASE						).raw						)
							return im






@torch.no_grad()
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					bool = False						):






							"""simple docstring"""



							_lowerCAmelCase  	=			get_yolos_config(SCREAMING_SNAKE_CASE						)

							# load original state_dict
							_lowerCAmelCase  	=			torch.load(SCREAMING_SNAKE_CASE     ,  map_location='cpu'						)['model']

							# load 🤗 model
							_lowerCAmelCase  	=			YolosForObjectDetection(SCREAMING_SNAKE_CASE						)
							model.eval()
							_lowerCAmelCase  	=			convert_state_dict(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							model.load_state_dict(SCREAMING_SNAKE_CASE						)

							# Check outputs on an image, prepared by YolosImageProcessor
							_lowerCAmelCase  	=			800 if yolos_name != 'yolos_ti' else 512
							_lowerCAmelCase  	=			YolosImageProcessor(format='coco_detection'     ,  size=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			image_processor(images=prepare_img()     ,  return_tensors='pt'						)
							_lowerCAmelCase  	=			model(**SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase	,		_lowerCAmelCase  	=			outputs.logits, outputs.pred_boxes

							_lowerCAmelCase	,		_lowerCAmelCase  	=			None, None
							if yolos_name == "yolos_ti":
														_lowerCAmelCase  	=			torch.tensor(
														    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]						)
														_lowerCAmelCase  	=			torch.tensor(
														    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]						)
							elif yolos_name == "yolos_s_200_pre":
														_lowerCAmelCase  	=			torch.tensor(
														    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]						)
														_lowerCAmelCase  	=			torch.tensor(
														    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]						)
							elif yolos_name == "yolos_s_300_pre":
														_lowerCAmelCase  	=			torch.tensor(
														    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]						)
														_lowerCAmelCase  	=			torch.tensor(
														    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]						)
							elif yolos_name == "yolos_s_dWr":
														_lowerCAmelCase  	=			torch.tensor(
														    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]						)
														_lowerCAmelCase  	=			torch.tensor(
														    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]						)
							elif yolos_name == "yolos_base":
														_lowerCAmelCase  	=			torch.tensor(
														    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]						)
														_lowerCAmelCase  	=			torch.tensor(
														    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]						)
							else:
														raise ValueError(f"""Unknown yolos_name: {yolos_name}"""						)

							assert torch.allclose(logits[0, :3, :3]     ,  SCREAMING_SNAKE_CASE     ,  atol=1e-4						)
							assert torch.allclose(pred_boxes[0, :3, :3]     ,  SCREAMING_SNAKE_CASE     ,  atol=1e-4						)

							Path(SCREAMING_SNAKE_CASE						).mkdir(exist_ok=SCREAMING_SNAKE_CASE						)
							print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)

							if push_to_hub:
														_lowerCAmelCase  	=			{
														    'yolos_ti': 'yolos-tiny',
														    'yolos_s_200_pre': 'yolos-small',
														    'yolos_s_300_pre': 'yolos-small-300',
														    'yolos_s_dWr': 'yolos-small-dwr',
														    'yolos_base': 'yolos-base',
														}

														print('Pushing to the hub...'						)
														_lowerCAmelCase  	=			model_mapping[yolos_name]
														image_processor.push_to_hub(SCREAMING_SNAKE_CASE     ,  organization='hustvl'						)
														model.push_to_hub(SCREAMING_SNAKE_CASE     ,  organization='hustvl'						)


if __name__ == "__main__":
	_snake_case          =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--yolos_name''',
	    default='''yolos_s_200_pre''',
	    type=str,
	    help=(
	        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
	        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
	    ),
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	_snake_case          =						parser.parse_args()
	convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








"""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


_snake_case          =						logging.getLogger(__name__)
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str
				SCREAMING_SNAKE_CASE_:							List[str]
				SCREAMING_SNAKE_CASE_:							Optional[List[str]]






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



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






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"train"
				SCREAMING_SNAKE_CASE_:							Tuple							=					"dev"
				SCREAMING_SNAKE_CASE_:							List[str]							=					"test"






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Union[Split, str]  )		->  List[InputExample]:







											"""simple docstring"""




											raise NotImplementedError


				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str  )		->  List[str]:







											"""simple docstring"""




											raise NotImplementedError







				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			List[InputExample]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Optional[int]="[CLS]"				,     UpperCAmelCase_	:			Tuple=1				,     UpperCAmelCase_	:			str="[SEP]"				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=0				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=-100				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=True				,     )		->  List[InputFeatures]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{label: i for i, label in enumerate(UpperCAmelCase_  )}

											_lowerCAmelCase  	=			[]
											for ex_index, example in enumerate(UpperCAmelCase_  ):
																		if ex_index % 10_000 == 0:
																									logger.info('Writing example %d of %d'				,     UpperCAmelCase_				,     len(UpperCAmelCase_  )  )

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

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(UpperCAmelCase_  ) > 0:
																																tokens.extend(UpperCAmelCase_  )
																																# 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(UpperCAmelCase_  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		_lowerCAmelCase  	=			tokenizer.num_special_tokens_to_add()
																		if len(UpperCAmelCase_  ) > max_seq_length - special_tokens_count:
																									_lowerCAmelCase  	=			tokens[: (max_seq_length - special_tokens_count)]
																									_lowerCAmelCase  	=			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]
																		_lowerCAmelCase  	=			[sequence_a_segment_id] * len(UpperCAmelCase_  )

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

																		_lowerCAmelCase  	=			tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )

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

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

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

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

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


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






					'''simple docstring'''



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


					def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  List[str]:







												"""simple docstring"""




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

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_lowerCAmelCase  	=			cached_features_file + '.lock'
												with FileLock(UpperCAmelCase_  ):
																			if os.path.exists(UpperCAmelCase_  ) and not overwrite_cache:
																										logger.info(F"""Loading features from cached file {cached_features_file}"""  )
																										_lowerCAmelCase  	=			torch.load(UpperCAmelCase_  )
																			else:
																										logger.info(F"""Creating features from dataset file at {data_dir}"""  )
																										_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
																										# TODO clean up all this to leverage built-in features of tokenizers
																										_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
																										    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     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=UpperCAmelCase_				,     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				,     UpperCAmelCase_  )


					def __len__(					self	:			Dict  )		->  int:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			List[str]				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]


if is_tf_available():
	import tensorflow as tf
	class        _SCREAMING_SNAKE_CASE  :






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					-1_0_0


					def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  Dict:







												"""simple docstring"""




												_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
												# TODO clean up all this to leverage built-in features of tokenizers
												_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
												    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     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=UpperCAmelCase_				,     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:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)				,     (
																			        {'input_ids': tf.TensorShape([None]  ), 'attention_mask': tf.TensorShape([None]  )},
																			        tf.TensorShape([None]  ),
																			    )				,     )
												else:
																			_lowerCAmelCase  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'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 				__lowerCamelCase	(					self	:			str  )		->  Tuple:







												"""simple docstring"""




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

												return self.dataset


					def __len__(					self	:			List[Any]  )		->  Optional[Any]:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			int				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]






