






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


snake_case__   :						Tuple													=   logging.get_logger(__name__)

snake_case__   :						List[str]													=   {
    """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     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """yolos"""




  def __init__(  self		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=[512, 864]		,		_UpperCAmelCase=16		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=100		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=1		,		_UpperCAmelCase=5		,		_UpperCAmelCase=2		,		_UpperCAmelCase=5		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.1		,		**_UpperCAmelCase		,		)      ->			List[str]:
     super().__init__(**_UpperCAmelCase			)

     UpperCamelCase_						=  hidden_size
     UpperCamelCase_						=  num_hidden_layers
     UpperCamelCase_						=  num_attention_heads
     UpperCamelCase_						=  intermediate_size
     UpperCamelCase_						=  hidden_act
     UpperCamelCase_						=  hidden_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  initializer_range
     UpperCamelCase_						=  layer_norm_eps
     UpperCamelCase_						=  image_size
     UpperCamelCase_						=  patch_size
     UpperCamelCase_						=  num_channels
     UpperCamelCase_						=  qkv_bias
     UpperCamelCase_						=  num_detection_tokens
     UpperCamelCase_						=  use_mid_position_embeddings
     UpperCamelCase_						=  auxiliary_loss
     # Hungarian matcher
     UpperCamelCase_						=  class_cost
     UpperCamelCase_						=  bbox_cost
     UpperCamelCase_						=  giou_cost
     # Loss coefficients
     UpperCamelCase_						=  bbox_loss_coefficient
     UpperCamelCase_						=  giou_loss_coefficient
     UpperCamelCase_						=  eos_coefficient




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = version.parse("""1.11"""     )




  @property
  def  _UpperCAmelCase  (  self			)      ->			Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]			)




  @property
  def  _UpperCAmelCase  (  self			)      ->			float:
     return 1e-4




  @property
  def  _UpperCAmelCase  (  self			)      ->			int:
     return 12











import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class 		__A   (      tf.keras.layers.Layer      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict[str, int]     ,			__snake_case :List[str]     ,			__snake_case :int = None     ,			__snake_case :int = None			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Optional[int]										=pad_token_id
										__magic_name__						:						List[Any]										=max_length
										__magic_name__						:						Dict										=vocab
										__magic_name__						:						int										=merges
										__magic_name__						:						Optional[int]										=BytePairTokenizer(__snake_case     ,			__snake_case     ,			sequence_length=__snake_case			)

						@classmethod
						def      A__    (		cls :List[Any]     ,			__snake_case :GPTaTokenizer     ,			*__snake_case :int     ,			**__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=[""" """.join(__snake_case			) for m in tokenizer.bpe_ranks.keys()]
										__magic_name__						:						str										=tokenizer.get_vocab()
										return cls(__snake_case     ,			__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Dict     ,			__snake_case :Union[str, os.PathLike]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=GPTaTokenizer.from_pretrained(__snake_case     ,			*__snake_case     ,			**__snake_case			)
										return cls.from_tokenizer(__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Optional[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										return cls(**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return {
										    "vocab": self.vocab,
										    "merges": self.merges,
										    "max_length": self.max_length,
										    "pad_token_id": self.pad_token_id,
										}


						def      A__    (		self :List[Any]     ,			__snake_case :Dict     ,			__snake_case :int = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.tf_tokenizer(__snake_case			)
										__magic_name__						:						Tuple										=tf.ones_like(__snake_case			)

										if self.pad_token_id is not None:
														# pad the tokens up to max length
														__magic_name__						:						Tuple										=max_length if max_length is not None else self.max_length

														if max_length is not None:
																		__magic_name__					,			__magic_name__						:						Tuple										=pad_model_inputs(
																		    __snake_case     ,			max_seq_length=__snake_case     ,			pad_value=self.pad_token_id			)

										return {"attention_mask": attention_mask, "input_ids": input_ids}




