




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






def  snake_case (							A						,       A=3      )							->							Optional[int]:




	'''simple docstring'''


	lowerCamelCase_							:		str							       =							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_							:		List[Any]							       =							ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
			# calculate mask median
			lowerCamelCase_							:		Optional[Any]							       =							sort(A      )[inta(divide((multiply(A						,       A      ))						,       2      ) + 1      )]
			lowerCamelCase_							:		Union[str, Any]							       =							median
	return median_img


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

							# get values with two different mask size
							UpperCamelCase__		=						median_filter(gray, 3)
							UpperCamelCase__		=						median_filter(gray, 5)

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






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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			"""m2m_100"""
					_lowerCamelCase		  =			["""past_key_values"""]
					_lowerCamelCase		  =			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(    self			,	__lowercase=128_112			,	__lowercase=1_024			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=0.05			,	__lowercase=0.05			,	__lowercase=True			,	__lowercase=True			,	__lowercase="relu"			,	__lowercase=1_024			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=0.0			,	__lowercase=0.02			,	__lowercase=2			,	__lowercase=True			,	__lowercase=1			,	__lowercase=0			,	__lowercase=2			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							vocab_size
						lowerCamelCase_							:		List[str]							       =							max_position_embeddings
						lowerCamelCase_							:		Tuple							       =							d_model
						lowerCamelCase_							:		Optional[int]							       =							encoder_ffn_dim
						lowerCamelCase_							:		Union[str, Any]							       =							encoder_layers
						lowerCamelCase_							:		Dict							       =							encoder_attention_heads
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_ffn_dim
						lowerCamelCase_							:		List[str]							       =							decoder_layers
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_attention_heads
						lowerCamelCase_							:		Optional[int]							       =							dropout
						lowerCamelCase_							:		str							       =							attention_dropout
						lowerCamelCase_							:		Optional[Any]							       =							activation_dropout
						lowerCamelCase_							:		str							       =							activation_function
						lowerCamelCase_							:		int							       =							init_std
						lowerCamelCase_							:		Optional[int]							       =							encoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							decoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							use_cache
						lowerCamelCase_							:		Any							       =							encoder_layers
						lowerCamelCase_							:		Any							       =							scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=__lowercase			,	bos_token_id=__lowercase			,	eos_token_id=__lowercase			,	is_encoder_decoder=__lowercase			,	decoder_start_token_id=__lowercase			,	**__lowercase			,	)


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ] )

						if self.use_past:
							lowerCamelCase_							:		Union[str, Any]							       =							{0: 'batch'}
							lowerCamelCase_							:		Optional[Any]							       =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
							lowerCamelCase_							:		Any							       =							{0: 'batch', 1: 'decoder_sequence'}
							lowerCamelCase_							:		Union[str, Any]							       =							{0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
							self.fill_with_past_key_values_(__lowercase			,	direction='inputs' )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							compute_effective_axis_dimension(
						    __lowercase			,	fixed_dimension=OnnxConfig.default_fixed_batch			,	num_token_to_add=0 )

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						lowerCamelCase_							:		str							       =							tokenizer.num_special_tokens_to_add(__lowercase )
						lowerCamelCase_							:		Dict							       =							compute_effective_axis_dimension(
						    __lowercase			,	fixed_dimension=OnnxConfig.default_fixed_sequence			,	num_token_to_add=__lowercase )

						# Generate dummy inputs according to compute batch and sequence
						lowerCamelCase_							:		Optional[int]							       =							[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
						lowerCamelCase_							:		Optional[Any]							       =							dict(tokenizer(__lowercase			,	return_tensors=__lowercase ) )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )

						# Generate decoder inputs
						lowerCamelCase_							:		Dict							       =							seq_length if not self.use_past else 1
						lowerCamelCase_							:		List[str]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )
						lowerCamelCase_							:		Any							       =							{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
						lowerCamelCase_							:		str							       =							dict(**__lowercase			,	**__lowercase )

						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_							:		int							       =							common_inputs['input_ids'].shape
							lowerCamelCase_							:		List[Any]							       =							common_inputs['decoder_input_ids'].shape[1]
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_attention_heads
							lowerCamelCase_							:		Any							       =							(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							lowerCamelCase_							:		Tuple							       =							decoder_seq_length + 3
							lowerCamelCase_							:		Any							       =							(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							lowerCamelCase_							:		List[Any]							       =							torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(__lowercase			,	__lowercase )]			,	dim=1 )

							lowerCamelCase_							:		Tuple							       =							[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_layers
							lowerCamelCase_							:		Optional[int]							       =							min(__lowercase			,	__lowercase )
							lowerCamelCase_							:		int							       =							max(__lowercase			,	__lowercase ) - min_num_layers
							lowerCamelCase_							:		Union[str, Any]							       =							'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

							for _ in range(__lowercase ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								    ) )
							# TODO: test this.
							lowerCamelCase_							:		Optional[int]							       =							encoder_shape if remaining_side_name == 'encoder' else decoder_shape
							for _ in range(__lowercase			,	__lowercase ):
								common_inputs["past_key_values"].append((torch.zeros(__lowercase ), torch.zeros(__lowercase )) )
						return common_inputs





					_lowerCamelCase		  =			_generate_dummy_inputs_for_default_and_seqaseq_lm
