import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__      						=  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__      						=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__      						=  {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}



class   lowerCAmelCase_    (			_UpperCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Optional[int]			  =						VOCAB_FILES_NAMES
				_lowerCAmelCase     :    Optional[Any]			  =						PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase     :    Optional[Any]			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase     :    List[Any]			  =						["""input_ids""", """attention_mask"""]
				_lowerCAmelCase     :    Union[str, Any]			  =						BartTokenizer

				def __init__(				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    lowerCAmelCase="replace"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="</s>"		,    lowerCAmelCase="<s>"		,    lowerCAmelCase="<unk>"		,    lowerCAmelCase="<pad>"		,    lowerCAmelCase="<mask>"		,    lowerCAmelCase=False		,    lowerCAmelCase=True		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    lowercase__		,    lowercase__		,    tokenizer_file=lowercase__		,    errors=lowercase__		,    bos_token=lowercase__		,    eos_token=lowercase__		,    sep_token=lowercase__		,    cls_token=lowercase__		,    unk_token=lowercase__		,    pad_token=lowercase__		,    mask_token=lowercase__		,    add_prefix_space=lowercase__		,    trim_offsets=lowercase__		,    **lowercase__		,    )

										snake_case								=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
										if pre_tok_state.get('add_prefix_space'		,    lowercase__    ) != add_prefix_space:
																snake_case								=			getattr(lowercase__		,    pre_tok_state.pop('type'    )    )
																snake_case								=			add_prefix_space
																snake_case								=			pre_tok_class(**lowercase__    )

										snake_case								=			add_prefix_space

										# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
										snake_case								=			"post_processor"
										snake_case								=			getattr(self.backend_tokenizer		,    lowercase__		,    lowercase__    )
										if tokenizer_component_instance:
																snake_case								=			json.loads(tokenizer_component_instance.__getstate__()    )

																# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																if "sep" in state:
																						snake_case								=			tuple(state['sep']    )
																if "cls" in state:
																						snake_case								=			tuple(state['cls']    )

																snake_case								=			False

																if state.get('add_prefix_space'		,    lowercase__    ) != add_prefix_space:
																						snake_case								=			add_prefix_space
																						snake_case								=			True

																if state.get('trim_offsets'		,    lowercase__    ) != trim_offsets:
																						snake_case								=			trim_offsets
																						snake_case								=			True

																if changes_to_apply:
																						snake_case								=			getattr(lowercase__		,    state.pop('type'    )    )
																						snake_case								=			component_class(**lowercase__    )
																						setattr(self.backend_tokenizer		,    lowercase__		,    lowercase__    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										if self._mask_token is None:
																if self.verbose:
																						logger.error('Using mask_token, but it is not set yet.'    )
																return None
										return str(self._mask_token    )

				@mask_token.setter
				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			AddedToken(lowercase__		,    lstrip=lowercase__		,    rstrip=lowercase__    ) if isinstance(lowercase__		,    lowercase__    ) else value
										snake_case								=			value

				def 		snake_case (				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			kwargs.get('is_split_into_words'		,    lowercase__    )

										if is_split_into_words and not self.add_prefix_space:
																raise ValueError(
																    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																    'to use it with pretokenized inputs.'    )

										return super()._batch_encode_plus(*lowercase__		,    **lowercase__    )

				def 		snake_case (				self		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			kwargs.get('is_split_into_words'		,    lowercase__    )

										if is_split_into_words and not self.add_prefix_space:
																raise ValueError(
																    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																    'to use it with pretokenized inputs.'    )

										return super()._encode_plus(*lowercase__		,    **lowercase__    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			self._tokenizer.model.save(lowercase__		,    name=lowercase__    )
										return tuple(lowercase__    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=None    ):






										"""simple docstring"""

										snake_case								=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

										snake_case								=			[self.sep_token_id]
										snake_case								=			[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]



"""simple docstring"""
import numpy as np
from PIL import Image





def    lowerCAmelCase__    (			_UpperCamelCase       :				np.ndarray ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				int					)					->				np.ndarray:





						"""simple docstring"""






						snake_case								=			np.array(_UpperCamelCase					)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'					)
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0

						# compute the shape of the output matrix
						snake_case								=			(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape maxpool_shape
						snake_case								=			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
																		snake_case								=			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
												snake_case								=			0
												snake_case								=			0

						return updated_arr





def    lowerCAmelCase__    (			_UpperCamelCase       :				np.ndarray ,			_UpperCamelCase       :				int ,			_UpperCamelCase       :				int					)					->				np.ndarray:





						"""simple docstring"""






						snake_case								=			np.array(_UpperCamelCase					)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'					)
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0
						snake_case								=			0

						# compute the shape of the output matrix
						snake_case								=			(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape avgpool_shape
						snake_case								=			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
																		snake_case								=			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
												snake_case								=			0
												snake_case								=			0

						return updated_arr


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

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

					# Loading the image
					SCREAMING_SNAKE_CASE__      						=  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()


