"""simple docstring"""



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[Any]								=			k_size // 2
							A__				,							A__       :     Dict								=			mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							A__       :     Tuple								=			1 / (2 * pi * sigma) * exp(-(square(lowerCAmelCase							) + square(lowerCAmelCase							)) / (2 * square(lowerCAmelCase							))							)
							return g

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__				,							A__       :     Dict								=			image.shape[0], image.shape[1]
							# dst image height and width
							A__       :     Dict								=			height - k_size + 1
							A__       :     Dict								=			width - k_size + 1

							# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
							A__       :     int								=			zeros((dst_height * dst_width, k_size * k_size)							)
							A__       :     Any								=			0
							for i, j in product(range(lowerCAmelCase							)			,  range(lowerCAmelCase							)							):
														A__       :     Any								=			ravel(image[i : i + k_size, j : j + k_size]							)
														A__       :     Tuple								=			window
														row += 1

							#  turn the kernel into shape(k*k, 1)
							A__       :     Dict								=			gen_gaussian_kernel(lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     Union[str, Any]								=			ravel(lowerCAmelCase							)

							# reshape and get the dst image
							A__       :     str								=			dot(lowerCAmelCase			,  lowerCAmelCase							).reshape(lowerCAmelCase			,  lowerCAmelCase							).astype(lowerCAmelCase							)

							return dst


if __name__ == "__main__":
				# read original image
				_UpperCamelCase								=		imread(R"../image_data/lena.jpg")
				# turn image in gray scale value
				_UpperCamelCase								=		cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				_UpperCamelCase								=		gaussian_filter(gray, 3, sigma=1)
				_UpperCamelCase								=		gaussian_filter(gray, 5, sigma=0.8)

				# show result images
				imshow("gaussian filter with 3x3 mask", gaussianaxa)
				imshow("gaussian filter with 5x5 mask", gaussianaxa)
				waitKey()






"""simple docstring"""



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

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				'gpt_neo'
			_UpperCamelCase  :						Optional[Any]					     =				['past_key_values']
			_UpperCamelCase  :						int					     =				{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





			def __init__(			self   ,      snake_case_=50_257   ,      snake_case_=2_048   ,      snake_case_=2_048   ,      snake_case_=24   ,      snake_case_=[[["global", "local"], 12]]   ,      snake_case_=16   ,      snake_case_=None   ,      snake_case_=256   ,      snake_case_="gelu_new"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=1E-5   ,      snake_case_=0.02   ,      snake_case_=True   ,      snake_case_=50_256   ,      snake_case_=50_256   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     int								=			vocab_size
										A__       :     Optional[Any]								=			max_position_embeddings
										A__       :     int								=			hidden_size
										A__       :     List[Any]								=			num_layers
										A__       :     Any								=			num_heads
										A__       :     List[str]								=			intermediate_size
										A__       :     Dict								=			window_size
										A__       :     Optional[int]								=			activation_function
										A__       :     Optional[int]								=			resid_dropout
										A__       :     List[str]								=			embed_dropout
										A__       :     str								=			attention_dropout
										A__       :     List[str]								=			classifier_dropout
										A__       :     str								=			layer_norm_epsilon
										A__       :     str								=			initializer_range
										A__       :     Any								=			use_cache

										A__       :     List[str]								=			bos_token_id
										A__       :     Any								=			eos_token_id

										A__       :     Optional[Any]								=			attention_types
										A__       :     List[Any]								=			self.expand_attention_types_params(snake_case_							)

										if len(self.attention_layers							) != self.num_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.attention_layers)` == `config.num_layers` """
																	    F'''but is `len(config.attention_layers) = {len(self.attention_layers							)}`, '''
																	    F'''`config.num_layers = {self.num_layers}`. '''
																	    """`config.attention_layers` is prepared using `config.attention_types`. """
																	    """Please verify the value of `config.attention_types` argument."""							)

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





			@staticmethod
			def 		lowerCamelCase					(			snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			[]
										for item in attention_types:
																	for _ in range(item[1]							):
																								attentions.extend(item[0]							)
										return attentions





def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							import torch

							A__       :     Union[str, Any]								=			input.size()
							A__       :     str								=			len(lowerCAmelCase							)
							A__       :     Union[str, Any]								=			shape[dimension]

							A__       :     Dict								=			torch.arange(0			,  lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     Dict								=			torch.div(sizedim - size			,  lowerCAmelCase			,  rounding_mode="""floor"""							) + 1
							A__       :     str								=			torch.arange(lowerCAmelCase							) + low_indices[:min_length][:, None]

							A__       :     str								=			[slice(lowerCAmelCase							)] * rank
							A__       :     Optional[Any]								=			indices
							A__       :     Tuple								=			input[s]

							A__       :     List[str]								=			list(range(0			,  rank + 1							)							)
							perm.append(perm.pop(dimension + 1							)							)

							return sliced.permute(lowerCAmelCase							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							import torch

							A__       :     Optional[Any]								=			torch.arange(1			,  lowerCAmelCase							)
							A__       :     int								=			torch.remainder(lowerCAmelCase			,  lowerCAmelCase							)
							A__       :     str								=			remainders == 0
							A__       :     Tuple								=			candidates[divisor_indices]
							A__       :     Any								=			torch.max(lowerCAmelCase							)
							return largest_divisor, torch.div(lowerCAmelCase			,  lowerCAmelCase			,  rounding_mode="""floor"""							)



class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Any								=			OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
										if self.use_past:
																	self.fill_with_past_key_values_(snake_case_   ,      direction="""inputs"""							)
																	A__       :     str								=			{0: """batch""", 1: """past_sequence + sequence"""}
										else:
																	A__       :     Optional[Any]								=			{0: """batch""", 1: """sequence"""}

										return common_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self._config.num_heads





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = -1   ,      snake_case_ = -1   ,      snake_case_ = False   ,      snake_case_ = None   ,      ):







										'''simple docstring'''


										A__       :     Optional[int]								=			super(snake_case_   ,      self							).generate_dummy_inputs(
										    snake_case_   ,      batch_size=snake_case_   ,      seq_length=snake_case_   ,      is_pair=snake_case_   ,      framework=snake_case_							)

										# We need to order the input in the way they appears in the forward()
										A__       :     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

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

										A__       :     Tuple								=			common_inputs["""attention_mask"""]
										if self.use_past:
																	A__       :     List[Any]								=			ordered_inputs["""attention_mask"""].dtype
																	A__       :     Optional[Any]								=			torch.cat(
																	    [ordered_inputs["""attention_mask"""], torch.ones(snake_case_   ,      snake_case_   ,      dtype=snake_case_							)]   ,      dim=1							)

										return ordered_inputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return 13





