


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_lowerCAmelCase:	int      							=					logging.get_logger(__name__)




@dataclass
class 			lowercase_				:
				snake_case  						=42
				snake_case  						=42
				snake_case  						=None
				snake_case  						=None





				def 	__UpperCamelCase						(							self)     ->   Dict:
											return json.dumps(dataclasses.asdict(self)					,     indent=2) + "\n"







@dataclass(frozen=lowercase__      )
class 			lowercase_				:
				snake_case  						=42
				snake_case  						=None
				snake_case  						=None
				snake_case  						=None





				def 	__UpperCamelCase						(							self)     ->   Optional[int]:
											return json.dumps(dataclasses.asdict(self)) + "\n"







class 			lowercase_				:





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Optional[Any]:
											raise NotImplementedError()





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Optional[int]:
											raise NotImplementedError()





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Any:
											raise NotImplementedError()





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Dict:
											raise NotImplementedError()





				def 	__UpperCamelCase						(							self)     ->   Tuple:
											raise NotImplementedError()





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Optional[Any]:
											if len(self.get_labels()) > 1:
																		a__     						=self.get_labels()[int(example.label)]
											return example





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     lowercase_=None)     ->   Any:
											with open(lowercase_					,     'r'					,     encoding='utf-8-sig') as f:
																		return list(csv.reader(lowercase_					,     delimiter='\t'					,     quotechar=lowercase_))







class 			lowercase_				(lowercase__      ):





				def __init__(							self					,     lowercase_=None					,     lowercase_=None					,     lowercase_="classification"					,     lowercase_=False)     ->   Dict:
											a__     						=[] if labels is None else labels
											a__     						=[] if examples is None else examples
											a__     						=mode
											a__     						=verbose





				def __len__(							self)     ->   Any:
											return len(self.examples)





				def __getitem__(							self					,     lowercase_)     ->   Tuple:
											if isinstance(lowercase_					,     lowercase_):
																		return SingleSentenceClassificationProcessor(labels=self.labels					,     examples=self.examples[idx])
											return self.examples[idx]





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     lowercase_=""					,     lowercase_=0					,     lowercase_=1					,     lowercase_=None					,     lowercase_=False					,     **lowercase_)     ->   Optional[int]:
											a__     						=cls(**lowercase_)
											processor.add_examples_from_csv(
											    lowercase_					,     split_name=lowercase_					,     column_label=lowercase_					,     column_text=lowercase_					,     column_id=lowercase_					,     skip_first_row=lowercase_					,     overwrite_labels=lowercase_					,     overwrite_examples=lowercase_					,     )
											return processor





				@classmethod
				def 	__UpperCamelCase						(							cls					,     lowercase_					,     lowercase_=None					,     **lowercase_)     ->   str:
											a__     						=cls(**lowercase_)
											processor.add_examples(lowercase_					,     labels=lowercase_)
											return processor





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_=""					,     lowercase_=0					,     lowercase_=1					,     lowercase_=None					,     lowercase_=False					,     lowercase_=False					,     lowercase_=False					,     )     ->   Union[str, Any]:
											a__     						=self._read_tsv(lowercase_)
											if skip_first_row:
																		a__     						=lines[1:]
											a__     						=[]
											a__     						=[]
											a__     						=[]
											for i, line in enumerate(lowercase_):
																		texts.append(line[column_text])
																		labels.append(line[column_label])
																		if column_id is not None:
																									ids.append(line[column_id])
																		else:
																									a__     						=F"""{split_name}-{i}""" if split_name else str(lowercase_)
																									ids.append(lowercase_)

											return self.add_examples(
											    lowercase_					,     lowercase_					,     lowercase_					,     overwrite_labels=lowercase_					,     overwrite_examples=lowercase_)





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_=False					,     lowercase_=False)     ->   Tuple:
											if labels is not None and len(lowercase_) != len(lowercase_):
																		raise ValueError(
																		    F"""Text and labels have mismatched lengths {len(lowercase_)} and {len(lowercase_)}""")
											if ids is not None and len(lowercase_) != len(lowercase_):
																		raise ValueError(F"""Text and ids have mismatched lengths {len(lowercase_)} and {len(lowercase_)}""")
											if ids is None:
																		a__     						=[None] * len(lowercase_)
											if labels is None:
																		a__     						=[None] * len(lowercase_)
											a__     						=[]
											a__     						=set()
											for text_or_text_and_label, label, guid in zip(lowercase_					,     lowercase_					,     lowercase_):
																		if isinstance(lowercase_					,     (tuple, list)) and label is None:
																									a__						,    a__     						=text_or_text_and_label
																		else:
																									a__     						=text_or_text_and_label
																		added_labels.add(lowercase_)
																		examples.append(InputExample(guid=lowercase_					,     text_a=lowercase_					,     text_b=lowercase_					,     label=lowercase_))

											# Update examples
											if overwrite_examples:
																		a__     						=examples
											else:
																		self.examples.extend(lowercase_)

											# Update labels
											if overwrite_labels:
																		a__     						=list(lowercase_)
											else:
																		a__     						=list(set(self.labels).union(lowercase_))

											return self.examples





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_=None					,     lowercase_=False					,     lowercase_=0					,     lowercase_=True					,     lowercase_=None					,     )     ->   List[str]:
											if max_length is None:
																		a__     						=tokenizer.max_len

											a__     						={label: i for i, label in enumerate(self.labels)}

											a__     						=[]
											for ex_index, example in enumerate(self.examples):
																		if ex_index % 10000 == 0:
																									logger.info(F"""Tokenizing example {ex_index}""")

																		a__     						=tokenizer.encode(
																		    example.text_a					,     add_special_tokens=lowercase_					,     max_length=min(lowercase_					,     tokenizer.max_len)					,     )
																		all_input_ids.append(lowercase_)

											a__     						=max(len(lowercase_) for input_ids in all_input_ids)

											a__     						=[]
											for ex_index, (input_ids, example) in enumerate(zip(lowercase_					,     self.examples)):
																		if ex_index % 10000 == 0:
																									logger.info(F"""Writing example {ex_index}/{len(self.examples)}""")
																		# The mask has 1 for real tokens and 0 for padding tokens. Only real
																		# tokens are attended to.
																		a__     						=[1 if mask_padding_with_zero else 0] * len(lowercase_)

																		# Zero-pad up to the sequence length.
																		a__     						=batch_length - len(lowercase_)
																		if pad_on_left:
																									a__     						=([pad_token] * padding_length) + input_ids
																									a__     						=([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
																		else:
																									a__     						=input_ids + ([pad_token] * padding_length)
																									a__     						=attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

																		if len(lowercase_) != batch_length:
																									raise ValueError(F"""Error with input length {len(lowercase_)} vs {batch_length}""")
																		if len(lowercase_) != batch_length:
																									raise ValueError(F"""Error with input length {len(lowercase_)} vs {batch_length}""")

																		if self.mode == "classification":
																									a__     						=label_map[example.label]
																		elif self.mode == "regression":
																									a__     						=float(example.label)
																		else:
																									raise ValueError(self.mode)

																		if ex_index < 5 and self.verbose:
																									logger.info('*** Example ***')
																									logger.info(F"""guid: {example.guid}""")
																									logger.info(F"""input_ids: {' '.join([str(lowercase_) for x in input_ids])}""")
																									logger.info(F"""attention_mask: {' '.join([str(lowercase_) for x in attention_mask])}""")
																									logger.info(F"""label: {example.label} (id = {label})""")

																		features.append(InputFeatures(input_ids=lowercase_					,     attention_mask=lowercase_					,     label=lowercase_))

											if return_tensors is None:
																		return features
											elif return_tensors == "tf":
																		if not is_tf_available():
																									raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported')
																		import tensorflow as tf

																		def gen():
																									for ex in features:
																																yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

																		a__     						=tf.data.Dataset.from_generator(
																		    lowercase_					,     ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)					,     ({'input_ids': tf.TensorShape([None]), 'attention_mask': tf.TensorShape([None])}, tf.TensorShape([]))					,     )
																		return dataset
											elif return_tensors == "pt":
																		if not is_torch_available():
																									raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported')
																		import torch
																		from torch.utils.data import TensorDataset

																		a__     						=torch.tensor([f.input_ids for f in features]					,     dtype=torch.long)
																		a__     						=torch.tensor([f.attention_mask for f in features]					,     dtype=torch.long)
																		if self.mode == "classification":
																									a__     						=torch.tensor([f.label for f in features]					,     dtype=torch.long)
																		elif self.mode == "regression":
																									a__     						=torch.tensor([f.label for f in features]					,     dtype=torch.float)

																		a__     						=TensorDataset(lowercase_					,     lowercase_					,     lowercase_)
																		return dataset
											else:
																		raise ValueError('return_tensors should be one of \'tf\' or \'pt\'')





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')

