




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


UpperCamelCase__ :   Any						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Union[str, Any]						=     "detr"
				__a     :       Dict						=     ["past_key_values"]
				__a     :       Optional[int]						=     {
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}






				def __init__(			self      ,snake_case__=True      ,snake_case__=None      ,snake_case__=3      ,snake_case__=100      ,snake_case__=6      ,snake_case__=2048      ,snake_case__=8      ,snake_case__=6      ,snake_case__=2048      ,snake_case__=8      ,snake_case__=0.0      ,snake_case__=0.0      ,snake_case__=True      ,snake_case__="relu"      ,snake_case__=256      ,snake_case__=0.1      ,snake_case__=0.0      ,snake_case__=0.0      ,snake_case__=0.02      ,snake_case__=1.0      ,snake_case__=False      ,snake_case__="sine"      ,snake_case__="resnet50"      ,snake_case__=True      ,snake_case__=False      ,snake_case__=1      ,snake_case__=5      ,snake_case__=2      ,snake_case__=1      ,snake_case__=1      ,snake_case__=5      ,snake_case__=2      ,snake_case__=0.1      ,**snake_case__      ,):
							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.' )
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      CONFIG_MAPPING['resnet'](out_features=['stage4'] )
										elif isinstance(snake_case__      ,snake_case__ ):
													SCREAMING_SNAKE_CASE_   : List[str]					  =      backbone_config.get('model_type' )
													SCREAMING_SNAKE_CASE_   : Any					  =      CONFIG_MAPPING[backbone_model_type]
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      config_class.from_dict(snake_case__ )
										# set timm attributes to None
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : int					  =      None, None, None

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






				@property
				def 		snake_case	(			self ):
							return self.encoder_attention_heads






				@property
				def 		snake_case	(			self ):
							return self.d_model






				@classmethod
				def 		snake_case	(			cls      ,snake_case__      ,**snake_case__ ):
							return cls(backbone_config=snake_case__      ,**snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      copy.deepcopy(self.__dict__ )
							if output["backbone_config"] is not None:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.backbone_config.to_dict()
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.__class__.model_type
							return output







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       int						=     version.parse("1.11"							)






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






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






				@property
				def 		snake_case	(			self ):
							return 12








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    None:
					__lowerCAmelCase ,						__lowerCAmelCase						 =analyze_text(__lowerCamelCase						)
					__lowerCAmelCase						 =list(""" """ + ascii_lowercase						)
					# what is our total sum of probabilities.
					__lowerCAmelCase						 =sum(single_char_strings.values()						)

					# one length string
					__lowerCAmelCase						 =0
					# for each alpha we go in our dict and if it is in it we calculate entropy
					for ch in my_alphas:
										if ch in single_char_strings:
															__lowerCAmelCase						 =single_char_strings[ch]
															__lowerCAmelCase						 =my_str / all_sum
															my_fir_sum += prob * math.loga(__lowerCamelCase						)  # entropy formula.

    # print entropy
					print(f"""{round(-1 * my_fir_sum						):.1f}"""						)

					# two len string
					__lowerCAmelCase						 =sum(two_char_strings.values()						)
					__lowerCAmelCase						 =0
					# for each alpha (two in size) calculate entropy.
					for cha in my_alphas:
										for cha in my_alphas:
															__lowerCAmelCase						 =cha + cha
															if sequence in two_char_strings:
																				__lowerCAmelCase						 =two_char_strings[sequence]
																				__lowerCAmelCase						 =int(__lowerCamelCase						) / all_sum
																				my_sec_sum += prob * math.loga(__lowerCamelCase						)

    # print second entropy
					print(f"""{round(-1 * my_sec_sum						):.1f}"""						)

					# print the difference between them
					print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)						):.1f}"""						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    tuple[dict, dict]:
					__lowerCAmelCase						 =Counter()  # type: ignore
					__lowerCAmelCase						 =Counter()  # type: ignore
					single_char_strings[text[-1]] += 1

					# first case when we have space at start.
					two_char_strings[" " + text[0]] += 1
					for i in range(0					, len(__lowerCamelCase						) - 1						):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings






def        __lowerCAmelCase		(	)	->    str:
					import doctest

					doctest.testmod()
					# text = (
					#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
					#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
					#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
					#     "beloved evident through no service elderly is. Blind there if every no so "
					#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
					#     "of message cottage windows do besides against uncivil.  Delightful "
					#     "unreserved impossible few estimating men favourable see entreaties. She "
					#     "propriety immediate was improving. He or entrance humoured likewise "
					#     "moderate. Much nor game son say feel. Fat make met can must form into "
					#     "gate. Me we offending prevailed discovery. "
					# )

					# calculate_prob(text)


if __name__ == "__main__":
				main()




