


"""simple docstring"""
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


SCREAMING_SNAKE_CASE :    Dict									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Optional[int]									=				{
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='yolos'



		def __init__(self				,       a_=7_68				,       a_=12				,       a_=12				,       a_=30_72				,       a_="gelu"				,       a_=0.0				,       a_=0.0				,       a_=0.02				,       a_=1E-12				,       a_=[5_12, 8_64]				,       a_=16				,       a_=3				,       a_=True				,       a_=1_00				,       a_=True				,       a_=False				,       a_=1				,       a_=5				,       a_=2				,       a_=5				,       a_=2				,       a_=0.1				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)

			__snake_case	:						Union[str, Any]						=				hidden_size
			__snake_case	:						Union[str, Any]						=				num_hidden_layers
			__snake_case	:						Dict						=				num_attention_heads
			__snake_case	:						str						=				intermediate_size
			__snake_case	:						List[str]						=				hidden_act
			__snake_case	:						Tuple						=				hidden_dropout_prob
			__snake_case	:						Optional[int]						=				attention_probs_dropout_prob
			__snake_case	:						Union[str, Any]						=				initializer_range
			__snake_case	:						Tuple						=				layer_norm_eps
			__snake_case	:						List[Any]						=				image_size
			__snake_case	:						Tuple						=				patch_size
			__snake_case	:						str						=				num_channels
			__snake_case	:						Tuple						=				qkv_bias
			__snake_case	:						Union[str, Any]						=				num_detection_tokens
			__snake_case	:						List[str]						=				use_mid_position_embeddings
			__snake_case	:						Tuple						=				auxiliary_loss
			# Hungarian matcher
			__snake_case	:						List[str]						=				class_cost
			__snake_case	:						int						=				bbox_cost
			__snake_case	:						int						=				giou_cost
			# Loss coefficients
			__snake_case	:						Optional[int]						=				bbox_loss_coefficient
			__snake_case	:						List[str]						=				giou_loss_coefficient
			__snake_case	:						List[Any]						=				eos_coefficient



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=version.parse('1.11'				)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return OrderedDict(
			    [
			        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
			    ]					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1E-4



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 12









'''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


A__:    int					    =       logging.getLogger(__name__)







@dataclass
class A__       :
      __UpperCamelCase       :  str
      __UpperCamelCase       :  List[str]
      __UpperCamelCase       :  Optional[List[str]]







@dataclass
class A__       :
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  Optional[List[int]]	       =							None
      __UpperCamelCase       :  Optional[List[int]]	       =							None







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str	       =							"train"
      __UpperCamelCase       :  Tuple	       =							"dev"
      __UpperCamelCase       :  str	       =							"test"







class A__       :






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict     ,						SCREAMING_SNAKE_CASE			:Union[Split, str]	)       ->					List[InputExample]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:str	)       ->					List[str]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:List[InputExample]     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:int     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:Optional[Any]="[CLS]"     ,						SCREAMING_SNAKE_CASE			:Optional[int]=1     ,						SCREAMING_SNAKE_CASE			:Any="[SEP]"     ,						SCREAMING_SNAKE_CASE			:List[Any]=False     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=False     ,						SCREAMING_SNAKE_CASE			:List[str]=0     ,						SCREAMING_SNAKE_CASE			:str=0     ,						SCREAMING_SNAKE_CASE			:Dict=-1_0_0     ,						SCREAMING_SNAKE_CASE			:Optional[int]=0     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						)       ->					List[InputFeatures]:




             '''simple docstring'''
             _a    : str     					={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE	)}

             _a    : Tuple     					=[]
             for ex_index, example in enumerate(SCREAMING_SNAKE_CASE	):
                    if ex_index % 1_0_0_0_0 == 0:
                           logger.info("""Writing example %d of %d"""     ,						SCREAMING_SNAKE_CASE     ,						len(SCREAMING_SNAKE_CASE	)	)

                    _a    : Optional[Any]     					=[]
                    _a    : List[Any]     					=[]
                    for word, label in zip(example.words     ,						example.labels	):
                           _a    : Optional[int]     					=tokenizer.tokenize(SCREAMING_SNAKE_CASE	)

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                    _a    : Optional[int]     					=tokenizer.num_special_tokens_to_add()
                    if len(SCREAMING_SNAKE_CASE	) > max_seq_length - special_tokens_count:
                           _a    : List[Any]     					=tokens[: (max_seq_length - special_tokens_count)]
                           _a    : Tuple     					=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    : Dict     					=[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE	)

                    if cls_token_at_end:
                           tokens += [cls_token]
                           label_ids += [pad_token_label_id]
                           segment_ids += [cls_token_segment_id]
                    else:
                           _a    : Any     					=[cls_token] + tokens
                           _a    : Dict     					=[pad_token_label_id] + label_ids
                           _a    : Union[str, Any]     					=[cls_token_segment_id] + segment_ids

                    _a    : List[str]     					=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE	)

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

                    # Zero-pad up to the sequence length.
                    _a    : Union[str, Any]     					=max_seq_length - len(SCREAMING_SNAKE_CASE	)
                    if pad_on_left:
                           _a    : Optional[Any]     					=([pad_token] * padding_length) + input_ids
                           _a    : Optional[int]     					=([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                           _a    : Union[str, Any]     					=([pad_token_segment_id] * padding_length) + segment_ids
                           _a    : Dict     					=([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(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length

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

                    if "token_type_ids" not in tokenizer.model_input_names:
                           _a    : Tuple     					=None

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


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







   class A__       (						UpperCAmelCase__      ):
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							nn.CrossEntropyLoss().ignore_index






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




                '''simple docstring'''
                # Load data features from cache or dataset file
                _a    : Optional[Any]     					=os.path.join(
                    SCREAMING_SNAKE_CASE     ,						"""cached_{}_{}_{}""".format(mode.value     ,						tokenizer.__class__.__name__     ,						str(SCREAMING_SNAKE_CASE	)	)     ,						)

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






         def __len__( self			:Optional[int]	)       ->					Union[str, Any]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:Dict     ,						SCREAMING_SNAKE_CASE			:int	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


if is_tf_available():
   import tensorflow as tf







   class A__       :
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							-100






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




                '''simple docstring'''
                _a    : Tuple     					=token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                # TODO clean up all this to leverage built-in features of tokenizers
                _a    : List[Any]     					=token_classification_task.convert_examples_to_features(
                    SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						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=SCREAMING_SNAKE_CASE     ,						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    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""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    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""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			:Tuple	)       ->					Any:




                '''simple docstring'''
                _a    : List[Any]     					=self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features	)	)	)

                return self.dataset






         def __len__( self			:str	)       ->					Optional[int]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:int     ,						SCREAMING_SNAKE_CASE			:str	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


