






'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase__          =						{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowerCAmelCase__          =						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

lowerCAmelCase__          =						'▁'







class       __lowercase					(UpperCamelCase__    ):
	_lowerCamelCase       			=  VOCAB_FILES_NAMES
	_lowerCamelCase       			=  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase       			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase       			=  ['''input_ids''', '''attention_mask''']

	def __init__(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						int="<s>"  ,					UpperCAmelCase_   :						Optional[int]="</s>"  ,					UpperCAmelCase_   :						Union[str, Any]="</s>"  ,					UpperCAmelCase_   :						str="<s>"  ,					UpperCAmelCase_   :						Optional[int]="<unk>"  ,					UpperCAmelCase_   :						Union[str, Any]="<pad>"  ,					UpperCAmelCase_   :						str="<mask>"  ,					UpperCAmelCase_   :						Union[str, Any] = None  ,					**UpperCAmelCase_   :						str  ,					):
			# Mask token behave like a normal word, i.e. include the space before it
			UpperCamelCase__				:					List[Any]					=					AddedToken(_a  ,					lstrip=_a  ,					rstrip=_a) if isinstance(_a  ,					_a) else mask_token

			UpperCamelCase__				:					List[str]					=					{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=_a  ,					eos_token=_a  ,					unk_token=_a  ,					sep_token=_a  ,					cls_token=_a  ,					pad_token=_a  ,					mask_token=_a  ,					sp_model_kwargs=self.sp_model_kwargs  ,					**_a  ,					)

			UpperCamelCase__				:					int					=					vocab_file
			UpperCamelCase__				:					Dict					=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(str(_a))

			UpperCamelCase__				:					Union[str, Any]					=					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

			UpperCamelCase__				:					Any					=					len(self.sp_model) - 1
			UpperCamelCase__				:					Optional[int]					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Any = None):

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			UpperCamelCase__				:					Optional[int]					=					[self.cls_token_id]
			UpperCamelCase__				:					Optional[Any]					=					[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						Optional[Any]  ,					UpperCAmelCase_   :						Tuple = None  ,					UpperCAmelCase_   :						List[Any] = False):
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_a  ,					token_ids_a=_a  ,					already_has_special_tokens=_a)

			if token_ids_a is None:
					return [1] + ([0] * len(_a)) + [1]
			return [1] + ([0] * len(_a)) + [1, 1] + ([0] * len(_a)) + [1]

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						List[Any] = None):
			UpperCamelCase__				:					int					=					[self.sep_token_id]
			UpperCamelCase__				:					Any					=					[self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]

	@property
	def      __UpperCamelCase							(      self   :						int):
			return len(self.sp_model)

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					Union[str, Any]					=					{self.convert_ids_to_tokens(_a): i for i in range(self.vocab_size)}
			vocab.update(self.added_tokens_encoder)
			return vocab

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						List[str]):
			return self.sp_model.encode(_a  ,					out_type=_a)

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						str):
			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			UpperCamelCase__				:					Optional[int]					=					self.sp_model.PieceToId(_a)

			return spm_id if spm_id else self.unk_token_id

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Optional[int]):
			if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(_a)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Union[str, Any]					=					[]
			UpperCamelCase__				:					str					=					""""""
			UpperCamelCase__				:					List[str]					=					False
			for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special:
									out_string += " "
							out_string += self.sp_model.decode(_a) + token
							UpperCamelCase__				:					Tuple					=					True
							UpperCamelCase__				:					Dict					=					[]
					else:
							current_sub_tokens.append(_a)
							UpperCamelCase__				:					Optional[Any]					=					False
			out_string += self.sp_model.decode(_a)
			return out_string.strip()

	def __getstate__(      self   :						Optional[int]):
			UpperCamelCase__				:					List[Any]					=					self.__dict__.copy()
			UpperCamelCase__				:					Union[str, Any]					=					None
			return state

	def __setstate__(      self   :						int  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					Optional[int]					=					d

			# for backward compatibility
			if not hasattr(self  ,					'sp_model_kwargs'):
					UpperCamelCase__				:					Tuple					=					{}

			UpperCamelCase__				:					Union[str, Any]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(self.vocab_file)





	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						List[Any] = None):
			if not os.path.isdir(_a):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory')
					return
			UpperCamelCase__				:					Optional[Any]					=					os.path.join(
			    _a  ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

			if os.path.abspath(self.vocab_file) != os.path.abspath(_a) and os.path.isfile(self.vocab_file):
					copyfile(self.vocab_file  ,					_a)
			elif not os.path.isfile(self.vocab_file):
					with open(_a  ,					'wb') as fi:
							UpperCamelCase__				:					Union[str, Any]					=					self.sp_model.serialized_model_proto()
							fi.write(_a)

			return (out_vocab_file,)












'''simple docstring'''




import numpy as np
from PIL import Image







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					List[Any]					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Tuple					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					str					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					int					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		UpperCamelCase__				:					Dict					=					np.zeros((maxpool_shape, maxpool_shape))

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						UpperCamelCase__				:					Dict					=					np.max(arr[i : i + size, j : j + size])
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				UpperCamelCase__				:					List[Any]					=					0
				UpperCamelCase__				:					Optional[int]					=					0

		return updated_arr







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					Tuple					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					List[str]					=					0
		UpperCamelCase__				:					List[Any]					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					str					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		UpperCamelCase__				:					Union[str, Any]					=					np.zeros((avgpool_shape, avgpool_shape))

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						UpperCamelCase__				:					List[Any]					=					int(np.average(arr[i : i + size, j : j + size]))
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				UpperCamelCase__				:					Union[str, Any]					=					0
				UpperCamelCase__				:					Optional[Any]					=					0

		return updated_arr


# Main Function
if __name__ == "__main__":
		from doctest import testmod

		testmod(name='avgpooling', verbose=True)

		# Loading the image
		lowerCAmelCase__          =						Image.open('path_to_image')

		# Converting the image to numpy array and maxpooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

		# Converting the image to numpy array and averagepooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()




