

"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			zeros_like(A_				)
	for i in range(A_		,  gray_img.shape[0] - bd				):
		for j in range(A_		,  gray_img.shape[1] - bd				):
			# get mask according with mask
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


if __name__ == "__main__":
							# read original image
							__UpperCamelCase						:   Dict							   =	imread('''../image_data/lena.jpg''')
							# turn image in gray scale value
							__UpperCamelCase						:   Optional[Any]							   =	cvtColor(img, COLOR_BGR2GRAY)

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

							# show result images
							imshow('''median filter with 3x3 mask''', medianaxa)
							imshow('''median filter with 5x5 mask''', medianaxa)
							waitKey(0)







"""simple docstring"""





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCamelCase						:   str							   =	logging.get_logger(__name__)

__UpperCamelCase						:   Union[str, Any]							   =	{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= "gpt2"
					lowercase__       				= ["past_key_values"]
					lowercase__       				= {
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__( self    :       List[str]     ,lowercase_    :       Dict=5_0_2_5_7     ,lowercase_    :       Dict=1_0_2_4     ,lowercase_    :       Any=7_6_8     ,lowercase_    :       str=1_2     ,lowercase_    :       int=1_2     ,lowercase_    :       List[Any]=None     ,lowercase_    :       Dict="gelu_new"     ,lowercase_    :       Any=0.1     ,lowercase_    :       Optional[Any]=0.1     ,lowercase_    :       Tuple=0.1     ,lowercase_    :       Dict=1E-5     ,lowercase_    :       List[Any]=0.02     ,lowercase_    :       Tuple="cls_index"     ,lowercase_    :       Optional[int]=True     ,lowercase_    :       Union[str, Any]=None     ,lowercase_    :       Optional[Any]=True     ,lowercase_    :       List[Any]=0.1     ,lowercase_    :       List[str]=True     ,lowercase_    :       int=True     ,lowercase_    :       Optional[Any]=5_0_2_5_6     ,lowercase_    :       Dict=5_0_2_5_6     ,lowercase_    :       Union[str, Any]=False     ,lowercase_    :       Optional[Any]=False     ,**lowercase_    :       Dict     ,):
						lowerCAmelCase__			:		Tuple						       =			vocab_size
						lowerCAmelCase__			:		List[Any]						       =			n_positions
						lowerCAmelCase__			:		Optional[int]						       =			n_embd
						lowerCAmelCase__			:		Tuple						       =			n_layer
						lowerCAmelCase__			:		Optional[Any]						       =			n_head
						lowerCAmelCase__			:		List[Any]						       =			n_inner
						lowerCAmelCase__			:		Any						       =			activation_function
						lowerCAmelCase__			:		Optional[int]						       =			resid_pdrop
						lowerCAmelCase__			:		str						       =			embd_pdrop
						lowerCAmelCase__			:		Tuple						       =			attn_pdrop
						lowerCAmelCase__			:		List[str]						       =			layer_norm_epsilon
						lowerCAmelCase__			:		Union[str, Any]						       =			initializer_range
						lowerCAmelCase__			:		Optional[int]						       =			summary_type
						lowerCAmelCase__			:		Optional[Any]						       =			summary_use_proj
						lowerCAmelCase__			:		List[str]						       =			summary_activation
						lowerCAmelCase__			:		Optional[int]						       =			summary_first_dropout
						lowerCAmelCase__			:		Dict						       =			summary_proj_to_labels
						lowerCAmelCase__			:		Dict						       =			scale_attn_weights
						lowerCAmelCase__			:		Dict						       =			use_cache
						lowerCAmelCase__			:		List[Any]						       =			scale_attn_by_inverse_layer_idx
						lowerCAmelCase__			:		int						       =			reorder_and_upcast_attn

						lowerCAmelCase__			:		List[str]						       =			bos_token_id
						lowerCAmelCase__			:		List[str]						       =			eos_token_id

						super().__init__(bos_token_id=lowercase_     ,eos_token_id=lowercase_     ,**lowercase_	)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       Optional[int]     ,lowercase_    :       PretrainedConfig     ,lowercase_    :       str = "default"     ,lowercase_    :       List[PatchingSpec] = None     ,lowercase_    :       bool = False     ,):
						super().__init__(lowercase_     ,task=lowercase_     ,patching_specs=lowercase_     ,use_past=lowercase_	)
						if not getattr(self._config     ,'''pad_token_id'''     ,lowercase_	):
							# TODO: how to do that better?
							lowerCAmelCase__			:		int						       =			0







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
						if self.use_past:
							self.fill_with_past_key_values_(lowercase_     ,direction='''inputs'''	)
							lowerCAmelCase__			:		str						       =			{0: '''batch''', 1: '''past_sequence + sequence'''}
						else:
							lowerCAmelCase__			:		List[str]						       =			{0: '''batch''', 1: '''sequence'''}

						return common_inputs







					@property
					def  __lowerCAmelCase				( self    :       Any	):
						return self._config.n_layer







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						return self._config.n_head







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       PreTrainedTokenizer     ,lowercase_    :       int = -1     ,lowercase_    :       int = -1     ,lowercase_    :       bool = False     ,lowercase_    :       Optional[TensorType] = None     ,):
						lowerCAmelCase__			:		Any						       =			super(lowercase_     ,self	).generate_dummy_inputs(
						    lowercase_     ,batch_size=lowercase_     ,seq_length=lowercase_     ,is_pair=lowercase_     ,framework=lowercase_	)

						# We need to order the input in the way they appears in the forward()
						lowerCAmelCase__			:		List[Any]						       =			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
							else:
								import torch

								lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								lowerCAmelCase__			:		Dict						       =			seqlen + 2
								lowerCAmelCase__			:		Optional[Any]						       =			(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								lowerCAmelCase__			:		Tuple						       =			[
								    (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(self.num_layers	)
								]

						lowerCAmelCase__			:		Any						       =			common_inputs['''attention_mask''']
						if self.use_past:
							lowerCAmelCase__			:		Optional[Any]						       =			ordered_inputs['''attention_mask'''].dtype
							lowerCAmelCase__			:		Union[str, Any]						       =			torch.cat(
							    [ordered_inputs['''attention_mask'''], torch.ones(lowercase_     ,lowercase_     ,dtype=lowercase_	)]     ,dim=1	)

						return ordered_inputs







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return 1_3




