


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


_lowerCAmelCase:	Tuple      							=					logging.get_logger(__name__)

_lowerCAmelCase:	Optional[Any]      							=					{
    '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 			lowercase_				(lowercase__      ):
				snake_case  						='m2m_100'
				snake_case  						=['past_key_values']
				snake_case  						={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





				def __init__(							self					,     lowercase_=128112					,     lowercase_=1024					,     lowercase_=12					,     lowercase_=4096					,     lowercase_=16					,     lowercase_=12					,     lowercase_=4096					,     lowercase_=16					,     lowercase_=0.05					,     lowercase_=0.05					,     lowercase_=True					,     lowercase_=True					,     lowercase_="relu"					,     lowercase_=1024					,     lowercase_=0.1					,     lowercase_=0.1					,     lowercase_=0.0					,     lowercase_=0.02					,     lowercase_=2					,     lowercase_=True					,     lowercase_=1					,     lowercase_=0					,     lowercase_=2					,     **lowercase_					,     )     ->   Optional[Any]:
											a__     						=vocab_size
											a__     						=max_position_embeddings
											a__     						=d_model
											a__     						=encoder_ffn_dim
											a__     						=encoder_layers
											a__     						=encoder_attention_heads
											a__     						=decoder_ffn_dim
											a__     						=decoder_layers
											a__     						=decoder_attention_heads
											a__     						=dropout
											a__     						=attention_dropout
											a__     						=activation_dropout
											a__     						=activation_function
											a__     						=init_std
											a__     						=encoder_layerdrop
											a__     						=decoder_layerdrop
											a__     						=use_cache
											a__     						=encoder_layers
											a__     						=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 			lowercase_				(lowercase__      ):





				@property
				def 	__UpperCamelCase						(							self)     ->   Mapping[str, Mapping[int, str]]:
											a__     						=OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ])

											if self.use_past:
																		a__     						={0: 'batch'}
																		a__     						={0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																		a__     						={0: 'batch', 1: 'decoder_sequence'}
																		a__     						={0: 'batch', 1: 'decoder_sequence'}

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





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = -1					,     lowercase_ = -1					,     lowercase_ = False					,     lowercase_ = None					,     )     ->   Mapping[str, Any]:
											# Copied from OnnxConfig.generate_dummy_inputs
											# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
											# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
											a__     						=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
											a__     						=tokenizer.num_special_tokens_to_add(lowercase_)
											a__     						=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
											a__     						=[' '.join([tokenizer.unk_token]) * seq_length] * batch_size
											a__     						=dict(tokenizer(lowercase_					,     return_tensors=lowercase_))
											return common_inputs





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = -1					,     lowercase_ = -1					,     lowercase_ = False					,     lowercase_ = None					,     )     ->   Mapping[str, Any]:
											a__     						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)

											# Generate decoder inputs
											a__     						=seq_length if not self.use_past else 1
											a__     						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_)
											a__     						={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
											a__     						=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
																		a__						,    a__     						=common_inputs['input_ids'].shape
																		a__     						=common_inputs['decoder_input_ids'].shape[1]
																		a__						,    a__     						=self.num_attention_heads
																		a__     						=(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		a__     						=decoder_seq_length + 3
																		a__     						=(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		a__     						=torch.cat(
																		    [common_inputs['decoder_attention_mask'], torch.ones(lowercase_					,     lowercase_)]					,     dim=1)

																		a__     						=[]
																		# If the number of encoder and decoder layers are present in the model configuration, both are considered
																		a__						,    a__     						=self.num_layers
																		a__     						=min(lowercase_					,     lowercase_)
																		a__     						=max(lowercase_					,     lowercase_) - min_num_layers
																		a__     						='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.
																		a__     						=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

				snake_case  						=_generate_dummy_inputs_for_default_and_seqaseq_lm





import numpy as np
from PIL import Image






def      _lowercase(				__a							:	np.array    ):
							a__						,    a__						,    a__     						=rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def      _lowercase(				__a							:	np.array    ):
							return (gray > 127) & (gray <= 255)






def      _lowercase(				__a							:	np.array						,    __a							:	np.array    ):
							a__     						=np.zeros_like(__a    )
							a__     						=np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

							# Copy image to padded image
							a__     						=image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]    ):
														for y in range(image.shape[0]    ):
																					a__     						=(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					a__     						=int(summation == 5    )
							return output


# kernel to be applied
_lowerCAmelCase:	Dict      							=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
	# read original image
	_lowerCAmelCase:	Optional[int]      							=					np.array(Image.open(R'..\image_data\lena.jpg'))
	# Apply erosion operation to a binary image
	_lowerCAmelCase:	Any      							=					erosion(grayabinary(rgbagray(image)), structuring_element)
	# Save the output image
	_lowerCAmelCase:	Dict      							=					Image.fromarray(output).convert('RGB')
	pil_img.save('result_erosion.png')

