



'''simple docstring'''

import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Dict   				=					LEDTokenizer
							lowerCAmelCase       :    List[str]   				=					LEDTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True







							def 							__lowercase			(							self					:       List[str]		):
									super().setUp()
									_a     :      str        =							[
									    'l',
									    'o',
									    'w',
									    'e',
									    'r',
									    's',
									    't',
									    'i',
									    'd',
									    'n',
									    '\u0120',
									    '\u0120l',
									    '\u0120n',
									    '\u0120lo',
									    '\u0120low',
									    'er',
									    '\u0120lowest',
									    '\u0120newer',
									    '\u0120wider',
									    '<unk>',
									]
									_a     :      Tuple        =							dict(zip(_UpperCAmelCase	,range(len(_UpperCAmelCase		)		)		)		)
									_a     :      List[str]        =							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
									_a     :      Tuple        =							{'unk_token': '<unk>'}

									_a     :      Tuple        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									_a     :      Optional[Any]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as fp:
											fp.write(json.dumps(_UpperCAmelCase		) + '\n'		)
									with open(self.merges_file	,'w'	,encoding='utf-8'		) as fp:
											fp.write('\n'.join(_UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       List[Any]	,**_UpperCAmelCase					:       List[Any]		):
									kwargs.update(self.special_tokens_map		)
									return self.tokenizer_class.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       str		):
									kwargs.update(self.special_tokens_map		)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Any		):
									return "lower newer", "lower newer"







							@cached_property
							def 							__lowercase			(							self					:       str		):
									return LEDTokenizer.from_pretrained('allenai/led-base-16384'		)







							@cached_property
							def 							__lowercase			(							self					:       Optional[Any]		):
									return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Optional[Any]        =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      List[Any]        =							tokenizer(_UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

											self.assertEqual((2, 9)	,batch.input_ids.shape		)
											self.assertEqual((2, 9)	,batch.attention_mask.shape		)
											_a     :      int        =							batch.input_ids.tolist()[0]
											self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Dict        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      int        =							tokenizer(_UpperCAmelCase	,padding=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIn('input_ids'	,_UpperCAmelCase		)
											self.assertIn('attention_mask'	,_UpperCAmelCase		)
											self.assertNotIn('labels'	,_UpperCAmelCase		)
											self.assertNotIn('decoder_attention_mask'	,_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       str		):
									_a     :      List[Any]        =							[
									    'Summary of the text.',
									    'Another summary.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Dict        =							tokenizer(text_target=_UpperCAmelCase	,max_length=32	,padding='max_length'	,return_tensors='pt'		)
											self.assertEqual(32	,targets['input_ids'].shape[1]		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Any        =							tokenizer(
											    ['I am a small frog' * 1024, 'I am a small frog']	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)
											self.assertEqual(batch.input_ids.shape	,(2, 5122)		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									_a     :      int        =							['A long paragraph for summarization.']
									_a     :      int        =							[
									    'Summary of the text.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Dict        =							tokenizer(_UpperCAmelCase	,return_tensors='pt'		)
											_a     :      List[str]        =							tokenizer(text_target=_UpperCAmelCase	,return_tensors='pt'		)
											_a     :      List[Any]        =							inputs['input_ids']
											_a     :      Tuple        =							targets['input_ids']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)







							@require_torch
							def 							__lowercase			(							self					:       Union[str, Any]		):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Optional[Any]        =							['Summary of the text.', 'Another summary.']
											_a     :      int        =							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

											_a     :      List[Any]        =							tokenizer(_UpperCAmelCase	,padding=_UpperCAmelCase		)
											_a     :      int        =							[[0] * len(_UpperCAmelCase		) for x in encoded_output['input_ids']]
											_a     :      List[Any]        =							tokenizer.pad(_UpperCAmelCase		)
											self.assertSequenceEqual(outputs['global_attention_mask']	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									pass







							def 							__lowercase			(							self					:       int		):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      Dict        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      int        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      Optional[Any]        =							'A, <mask> AllenNLP sentence.'
													_a     :      int        =							tokenizer_r.encode_plus(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase		)
													_a     :      List[Any]        =							tokenizer_p.encode_plus(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase		)
													self.assertEqual(sum(tokens_r['token_type_ids']		)	,sum(tokens_p['token_type_ids']		)		)
													self.assertEqual(
													    sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)	,sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)	,)

													_a     :      Any        =							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
													_a     :      Optional[Any]        =							tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)
													self.assertSequenceEqual(tokens_p['input_ids']	,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
													self.assertSequenceEqual(tokens_r['input_ids']	,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

													self.assertSequenceEqual(
													    _UpperCAmelCase	,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
													self.assertSequenceEqual(
													    _UpperCAmelCase	,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)









'''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()




