


"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


a__				:							Union[str, Any]						=     logging.get_logger(__name__)

a__				:							str						=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__				:							Dict						=     {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

a__				:							Dict						=     {
    '''gpt-neox-20b''': 2_0_4_8,
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  List[Any]    					=			VOCAB_FILES_NAMES
	snake_case__				:  Dict    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Union[str, Any]    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  Union[str, Any]    					=			["input_ids", "attention_mask"]




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				List[str]=None			,			UpperCAmelCase__			:				str=None			,			UpperCAmelCase__			:				Union[str, Any]="<|endoftext|>"			,			UpperCAmelCase__			:				Optional[Any]="<|endoftext|>"			,			UpperCAmelCase__			:				List[str]="<|endoftext|>"			,			UpperCAmelCase__			:				List[Any]=False			,			**UpperCAmelCase__			:				Optional[int]			,			)       ->	Optional[Any]:
						super().__init__(
						    UpperCAmelCase__			,			UpperCAmelCase__			,			tokenizer_file=UpperCAmelCase__			,			unk_token=UpperCAmelCase__			,			bos_token=UpperCAmelCase__			,			eos_token=UpperCAmelCase__			,			add_prefix_space=UpperCAmelCase__			,			**UpperCAmelCase__			,			)

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

						__SCREAMING_SNAKE_CASE						   =				add_prefix_space




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[str] = None   )       ->	Tuple[str]:
						__SCREAMING_SNAKE_CASE						   =				self._tokenizer.model.save(UpperCAmelCase__			,			name=UpperCAmelCase__   )
						return tuple(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				"Conversation"   )       ->	List[int]:
						__SCREAMING_SNAKE_CASE						   =				[]
						for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   ) + [self.eos_token_id]   )

						if len(UpperCAmelCase__   ) > self.model_max_length:
											__SCREAMING_SNAKE_CASE						   =				input_ids[-self.model_max_length :]
						return input_ids





'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




