


"""simple docstring"""


import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

A     :   Any			    =			logging.get_logger(__name__)


A     :   int			    =			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A     :   Union[str, Any]			    =			{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

A     :   Any			    =			{"facebook/blenderbot-3B": 1_2_8}
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    int				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    str				      =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    Union[str, Any]				      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    Union[str, Any]				      =["""input_ids""", """attention_mask"""]
							__UpperCAmelCase						:    List[str]				      =BlenderbotTokenizer

							def __init__(     self    ,   __a=None    ,   __a=None    ,   __a=None    ,   __a="replace"    ,   __a="<s>"    ,   __a="</s>"    ,   __a="</s>"    ,   __a="<s>"    ,   __a="<unk>"    ,   __a="<pad>"    ,   __a="<mask>"    ,   __a=False    ,   __a=True    ,   **__a    ,   ):
										super().__init__(
										    __a    ,   __a    ,   tokenizer_file=__a    ,   errors=__a    ,   bos_token=__a    ,   eos_token=__a    ,   sep_token=__a    ,   cls_token=__a    ,   unk_token=__a    ,   pad_token=__a    ,   mask_token=__a    ,   add_prefix_space=__a    ,   trim_offsets=__a    ,   **__a    ,   )

										__lowerCAmelCase				    =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get("add_prefix_space"    ,   __a	) != add_prefix_space:
													__lowerCAmelCase				    =			getattr(__a    ,   pre_tok_state.pop("type"	)	)
													__lowerCAmelCase				    =			add_prefix_space
													__lowerCAmelCase				    =			pre_tok_class(**__a	)

										__lowerCAmelCase				    =			add_prefix_space

										__lowerCAmelCase				    =			"post_processor"
										__lowerCAmelCase				    =			getattr(self.backend_tokenizer    ,   __a    ,   __a	)
										if tokenizer_component_instance:
													__lowerCAmelCase				    =			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:
																__lowerCAmelCase				    =			tuple(state["sep"]	)
													if "cls" in state:
																__lowerCAmelCase				    =			tuple(state["cls"]	)

													__lowerCAmelCase				    =			False

													if state.get("add_prefix_space"    ,   __a	) != add_prefix_space:
																__lowerCAmelCase				    =			add_prefix_space
																__lowerCAmelCase				    =			True

													if state.get("trim_offsets"    ,   __a	) != trim_offsets:
																__lowerCAmelCase				    =			trim_offsets
																__lowerCAmelCase				    =			True

													if changes_to_apply:
																__lowerCAmelCase				    =			getattr(__a    ,   state.pop("type"	)	)
																__lowerCAmelCase				    =			component_class(**__a	)
																setattr(self.backend_tokenizer    ,   __a    ,   __a	)

							@property
							# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 			snake_case				(     self	):
										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    ,   __a	):
										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else value
										__lowerCAmelCase				    =			value

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										__lowerCAmelCase				    =			kwargs.get("is_split_into_words"    ,   __a	)
										assert self.add_prefix_space or not is_split_into_words, (
										    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
										    "to use it with pretokenized inputs."
										)

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

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										__lowerCAmelCase				    =			kwargs.get("is_split_into_words"    ,   __a	)

										assert self.add_prefix_space or not is_split_into_words, (
										    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
										    "to use it with pretokenized inputs."
										)

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

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			self._tokenizer.model.save(__a    ,   name=__a	)
										return tuple(__a	)

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			[self.sep_token_id]
										__lowerCAmelCase				    =			[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]

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										return token_ids_a + [self.eos_token_id]



							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[]
										for is_user, text in conversation.iter_texts():
													if is_user:
																# We need to space prefix as it's being done within blenderbot
																inputs.append(" " + text	)
													else:
																# Generated responses should contain them already.
																inputs.append(__a	)

										__lowerCAmelCase				    =			"  ".join(__a	)
										__lowerCAmelCase				    =			self.encode(__a	)
										if len(__a	) > self.model_max_length:
													__lowerCAmelCase				    =			input_ids[-self.model_max_length :]
													logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."	)
										return input_ids








"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(_UpperCamelCase    , 110      )      ).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			canny.gen_gaussian_kernel(9    , sigma=1.4      )
			# Assert ambiguous array
			assert resp.all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

			# Reading the image and converting it to grayscale.
			__lowerCAmelCase				    =			imread(_UpperCamelCase    , 0      )

			# Test for get_neighbors_pixel function() return not None
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			image[x_coordinate][y_coordinate]

			__lowerCAmelCase				    =			lbp.get_neighbors_pixel(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			__lowerCAmelCase				    =			np.zeros((image.shape[0], image.shape[1])      )

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0    , image.shape[0]      ):
						for j in range(0    , image.shape[1]      ):
									__lowerCAmelCase				    =			lbp.local_binary_value(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




