



'''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


__lowerCAmelCase									=   logging.getLogger(__name__)

@dataclass
class 	__magic_name__						:
							lowerCAmelCase       :    str
							lowerCAmelCase       :    List[str]
							lowerCAmelCase       :    Optional[List[str]]

@dataclass
class 	__magic_name__						:
							lowerCAmelCase       :    List[int]
							lowerCAmelCase       :    List[int]
							lowerCAmelCase       :    Optional[List[int]]   				=					None
							lowerCAmelCase       :    Optional[List[int]]   				=					None

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    List[Any]   				=					'train'
							lowerCAmelCase       :    List[str]   				=					'dev'
							lowerCAmelCase       :    Optional[int]   				=					'test'

class 	__magic_name__						:







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Union[Split, str]		):
									raise NotImplementedError







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       str		):
									raise NotImplementedError







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       List[InputExample]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       PreTrainedTokenizer	,_UpperCAmelCase					:       Dict=False	,_UpperCAmelCase					:       int="[CLS]"	,_UpperCAmelCase					:       List[Any]=1	,_UpperCAmelCase					:       Optional[int]="[SEP]"	,_UpperCAmelCase					:       Any=False	,_UpperCAmelCase					:       int=False	,_UpperCAmelCase					:       Tuple=0	,_UpperCAmelCase					:       Any=0	,_UpperCAmelCase					:       int=-100	,_UpperCAmelCase					:       List[Any]=0	,_UpperCAmelCase					:       str=True	,):
									_a     :      str        =							{label: i for i, label in enumerate(_UpperCAmelCase		)}

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

											_a     :      Optional[int]        =							[]
											_a     :      Union[str, Any]        =							[]
											for word, label in zip(example.words	,example.labels		):
													_a     :      str        =							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.
											_a     :      List[Any]        =							tokenizer.num_special_tokens_to_add()
											if len(_UpperCAmelCase		) > max_seq_length - special_tokens_count:
													_a     :      Optional[Any]        =							tokens[: (max_seq_length - special_tokens_count)]
													_a     :      Optional[int]        =							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]
											_a     :      str        =							[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:
													_a     :      List[Any]        =							[cls_token] + tokens
													_a     :      Any        =							[pad_token_label_id] + label_ids
													_a     :      List[str]        =							[cls_token_segment_id] + segment_ids

											_a     :      Union[str, Any]        =							tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)

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

											# Zero-pad up to the sequence length.
											_a     :      int        =							max_seq_length - len(_UpperCAmelCase		)
											if pad_on_left:
													_a     :      Optional[Any]        =							([pad_token] * padding_length) + input_ids
													_a     :      str        =							([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
													_a     :      str        =							([pad_token_segment_id] * padding_length) + segment_ids
													_a     :      Optional[int]        =							([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:
													_a     :      Union[str, Any]        =							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 	__magic_name__						(    _UpperCamelCase   ):
														lowerCAmelCase       :    List[InputFeatures]
														lowerCAmelCase       :    int   				=					nn.CrossEntropyLoss().ignore_index







														def __init__(							self					:       int	,_UpperCAmelCase					:       TokenClassificationTask	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       PreTrainedTokenizer	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       List[Any]=False	,_UpperCAmelCase					:       Split = Split.train	,):
																# Load data features from cache or dataset file
																_a     :      str        =							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.
																_a     :      Tuple        =							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}"""		)
																				_a     :      int        =							torch.load(_UpperCAmelCase		)
																		else:
																				logger.info(F"""Creating features from dataset file at {data_dir}"""		)
																				_a     :      List[str]        =							token_classification_task.read_examples_from_file(_UpperCAmelCase	,_UpperCAmelCase		)
																				# TODO clean up all this to leverage built-in features of tokenizers
																				_a     :      List[str]        =							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					:       Any		):
																return len(self.features		)







														def __getitem__(							self					:       Tuple	,_UpperCAmelCase					:       Dict		):
																return self.features[i]


if is_tf_available():
							import tensorflow as tf

							class 	__magic_name__						:
														lowerCAmelCase       :    List[InputFeatures]
														lowerCAmelCase       :    int   				=					-1_0_0







														def __init__(							self					:       str	,_UpperCAmelCase					:       TokenClassificationTask	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       PreTrainedTokenizer	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       int=False	,_UpperCAmelCase					:       Split = Split.train	,):
																_a     :      List[Any]        =							token_classification_task.read_examples_from_file(_UpperCAmelCase	,_UpperCAmelCase		)
																# TODO clean up all this to leverage built-in features of tokenizers
																_a     :      Dict        =							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:
																		_a     :      Optional[Any]        =							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:
																		_a     :      str        =							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 							__lowercase			(							self					:       str		):
																_a     :      Optional[int]        =							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

																return self.dataset







														def __len__(							self					:       Optional[Any]		):
																return len(self.features		)







														def __getitem__(							self					:       str	,_UpperCAmelCase					:       int		):
																return self.features[i]









'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Any   				=					'conditional_detr'
							lowerCAmelCase       :    List[str]   				=					['past_key_values']
							lowerCAmelCase       :    Optional[int]   				=					{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]=None	,_UpperCAmelCase					:       List[Any]=3	,_UpperCAmelCase					:       List[Any]=300	,_UpperCAmelCase					:       Dict=6	,_UpperCAmelCase					:       List[str]=2048	,_UpperCAmelCase					:       Optional[int]=8	,_UpperCAmelCase					:       List[Any]=6	,_UpperCAmelCase					:       Optional[int]=2048	,_UpperCAmelCase					:       Dict=8	,_UpperCAmelCase					:       int=0.0	,_UpperCAmelCase					:       Optional[Any]=0.0	,_UpperCAmelCase					:       Optional[Any]=True	,_UpperCAmelCase					:       str="relu"	,_UpperCAmelCase					:       Tuple=256	,_UpperCAmelCase					:       Optional[int]=0.1	,_UpperCAmelCase					:       str=0.0	,_UpperCAmelCase					:       Optional[int]=0.0	,_UpperCAmelCase					:       Union[str, Any]=0.02	,_UpperCAmelCase					:       List[str]=1.0	,_UpperCAmelCase					:       Any=False	,_UpperCAmelCase					:       int="sine"	,_UpperCAmelCase					:       List[str]="resnet50"	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       str=False	,_UpperCAmelCase					:       str=2	,_UpperCAmelCase					:       int=5	,_UpperCAmelCase					:       Optional[int]=2	,_UpperCAmelCase					:       str=1	,_UpperCAmelCase					:       Union[str, Any]=1	,_UpperCAmelCase					:       List[str]=2	,_UpperCAmelCase					:       Union[str, Any]=5	,_UpperCAmelCase					:       List[Any]=2	,_UpperCAmelCase					:       Optional[int]=0.25	,**_UpperCAmelCase					:       Tuple	,):
									if backbone_config is not None and use_timm_backbone:
											raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'		)

									if not use_timm_backbone:
											if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'		)
													_a     :      Optional[Any]        =							CONFIG_MAPPING['resnet'](out_features=['stage4']		)
											elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      str        =							backbone_config.get('model_type'		)
													_a     :      Union[str, Any]        =							CONFIG_MAPPING[backbone_model_type]
													_a     :      List[Any]        =							config_class.from_dict(_UpperCAmelCase		)

									_a     :      Tuple        =							use_timm_backbone
									_a     :      Union[str, Any]        =							backbone_config
									_a     :      List[Any]        =							num_channels
									_a     :      Union[str, Any]        =							num_queries
									_a     :      Optional[Any]        =							d_model
									_a     :      Tuple        =							encoder_ffn_dim
									_a     :      Dict        =							encoder_layers
									_a     :      List[str]        =							encoder_attention_heads
									_a     :      Union[str, Any]        =							decoder_ffn_dim
									_a     :      Optional[int]        =							decoder_layers
									_a     :      int        =							decoder_attention_heads
									_a     :      Optional[int]        =							dropout
									_a     :      Tuple        =							attention_dropout
									_a     :      List[Any]        =							activation_dropout
									_a     :      str        =							activation_function
									_a     :      Optional[Any]        =							init_std
									_a     :      Union[str, Any]        =							init_xavier_std
									_a     :      List[Any]        =							encoder_layerdrop
									_a     :      List[Any]        =							decoder_layerdrop
									_a     :      Dict        =							encoder_layers
									_a     :      List[Any]        =							auxiliary_loss
									_a     :      Optional[int]        =							position_embedding_type
									_a     :      List[Any]        =							backbone
									_a     :      Optional[int]        =							use_pretrained_backbone
									_a     :      Optional[int]        =							dilation
									# Hungarian matcher
									_a     :      Tuple        =							class_cost
									_a     :      str        =							bbox_cost
									_a     :      Any        =							giou_cost
									# Loss coefficients
									_a     :      Tuple        =							mask_loss_coefficient
									_a     :      Dict        =							dice_loss_coefficient
									_a     :      Tuple        =							cls_loss_coefficient
									_a     :      Any        =							bbox_loss_coefficient
									_a     :      Dict        =							giou_loss_coefficient
									_a     :      Union[str, Any]        =							focal_alpha
									super().__init__(is_encoder_decoder=_UpperCAmelCase	,**_UpperCAmelCase		)







							@property
							def 							__lowercase			(							self					:       Dict		):
									return self.encoder_attention_heads







							@property
							def 							__lowercase			(							self					:       str		):
									return self.d_model







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							copy.deepcopy(self.__dict__		)
									if self.backbone_config is not None:
											_a     :      Dict        =							self.backbone_config.to_dict()
									_a     :      Union[str, Any]        =							self.__class__.model_type
									return output

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					version.parse('1.11'   )







							@property
							def 							__lowercase			(							self					:       Dict		):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									        ('pixel_mask', {0: 'batch'}),
									    ]		)







							@property
							def 							__lowercase			(							self					:       Any		):
									return 1E-5







							@property
							def 							__lowercase			(							self					:       List[Any]		):
									return 12




