
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_						: Any   =  get_tests_dir("""fixtures/test_sentencepiece.model""")

@require_sentencepiece
@require_tokenizers
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						str					      = ReformerTokenizer
			__UpperCamelCase      :						List[Any]					      = ReformerTokenizerFast
			__UpperCamelCase      :						List[str]					      = True
			__UpperCamelCase      :						List[str]					      = False
			__UpperCamelCase      :						List[Any]					      = True

			def       lowerCAmelCase__	(       self :       List[str]      ):
							super().setUp()

							UpperCamelCase_:							List[Any]	=						ReformerTokenizer(snake_case_						,	keep_accents=snake_case_      )
							tokenizer.save_pretrained(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Optional[Any]	=						"""<s>"""
							UpperCamelCase_:							Dict	=						1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_      )						,	snake_case_      )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_      )						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0]						,	"""<unk>"""      )
							self.assertEqual(vocab_keys[1]						,	"""<s>"""      )
							self.assertEqual(vocab_keys[-1]						,	"""j"""      )
							self.assertEqual(len(snake_case_      )						,	1000      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							self.assertEqual(self.get_tokenizer().vocab_size						,	1000      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							str	=						self.get_tokenizer()
							UpperCamelCase_:							Any	=						self.get_rust_tokenizer()

							UpperCamelCase_:							List[str]	=						"""I was born in 92000, and this is falsé."""

							UpperCamelCase_:							int	=						tokenizer.tokenize(snake_case_      )
							UpperCamelCase_:							Dict	=						rust_tokenizer.tokenize(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							Dict	=						tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							UpperCamelCase_:							List[str]	=						rust_tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Union[str, Any]	=						tokenizer.encode(snake_case_      )
							UpperCamelCase_:							str	=						rust_tokenizer.encode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Any=15      ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
															UpperCamelCase_:							str	=						self.rust_tokenizer_class.from_pretrained(snake_case_						,	**snake_case_      )

															# Simple input
															UpperCamelCase_:							Dict	=						"""This is a simple input"""
															UpperCamelCase_:							Optional[Any]	=						["""This is a simple input 1""", """This is a simple input 2"""]
															UpperCamelCase_:							Dict	=						("""This is a simple input""", """This is a pair""")
															UpperCamelCase_:							List[str]	=						[
															    ("""This is a simple input 1""", """This is a simple input 2"""),
															    ("""This is a simple pair 1""", """This is a simple pair 2"""),
															]

															# Simple input tests
															self.assertRaises(snake_case_						,	tokenizer_r.encode						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Simple input
															self.assertRaises(snake_case_						,	tokenizer_r.encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Simple input
															self.assertRaises(
															    snake_case_						,	tokenizer_r.batch_encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""						,	)

															# Pair input
															self.assertRaises(snake_case_						,	tokenizer_r.encode						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Pair input
															self.assertRaises(snake_case_						,	tokenizer_r.encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""      )

															# Pair input
															self.assertRaises(
															    snake_case_						,	tokenizer_r.batch_encode_plus						,	snake_case_						,	max_length=snake_case_						,	padding="""max_length"""						,	)

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							pass

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Any	=						ReformerTokenizer(snake_case_						,	keep_accents=snake_case_      )

							UpperCamelCase_:							int	=						tokenizer.tokenize("""This is a test"""      )
							self.assertListEqual(snake_case_						,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]      )

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(snake_case_      )						,	[285, 46, 10, 170, 382]						,	)

							UpperCamelCase_:							Optional[Any]	=						tokenizer.tokenize("""I was born in 92000, and this is falsé."""      )
							self.assertListEqual(
							    snake_case_						,	[
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """9""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """é""",
							        """.""",
							    ]						,	)
							UpperCamelCase_:							int	=						tokenizer.convert_tokens_to_ids(snake_case_      )
							self.assertListEqual(
							    snake_case_						,	[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,	)

							UpperCamelCase_:							str	=						tokenizer.convert_ids_to_tokens(snake_case_      )
							self.assertListEqual(
							    snake_case_						,	[
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """<unk>""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """<unk>""",
							        """.""",
							    ]						,	)

			@cached_property
			def       lowerCAmelCase__	(       self :       Tuple      ):
							return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""      )

			@slow
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Dict	=						"""Hello World!"""
							UpperCamelCase_:							str	=						[126, 32, 262, 152, 38, 72, 287]

							self.assertListEqual(snake_case_						,	self.big_tokenizer.encode(snake_case_      )      )

			@slow
			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Any	=						(
							    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
							    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
							)
							UpperCamelCase_:							Union[str, Any]	=						[
							    108,
							    265,
							    24,
							    111,
							    4,
							    258,
							    156,
							    35,
							    28,
							    275,
							    3,
							    259,
							    297,
							    260,
							    84,
							    4,
							    35,
							    110,
							    44,
							    8,
							    259,
							    91,
							    268,
							    21,
							    11,
							    209,
							    274,
							    109,
							    266,
							    277,
							    117,
							    86,
							    93,
							    315,
							    258,
							    278,
							    258,
							    277,
							    258,
							    0,
							    258,
							    288,
							    258,
							    319,
							    258,
							    0,
							    258,
							    0,
							    258,
							    0,
							    258,
							    0,
							    258,
							    287,
							    258,
							    315,
							    258,
							    289,
							    258,
							    278,
							    99,
							    269,
							    266,
							    262,
							    8,
							    259,
							    241,
							    4,
							    217,
							    230,
							    268,
							    266,
							    55,
							    168,
							    106,
							    75,
							    193,
							    266,
							    223,
							    27,
							    49,
							    26,
							    282,
							    25,
							    264,
							    299,
							    19,
							    26,
							    0,
							    258,
							    277,
							    117,
							    86,
							    93,
							    176,
							    183,
							    270,
							    11,
							    262,
							    42,
							    61,
							    265,
							]

							self.assertListEqual(snake_case_						,	self.big_tokenizer.encode(snake_case_      )      )

			@require_torch
			@slow
			def       lowerCAmelCase__	(       self :       List[str]      ):
							import torch

							from transformers import ReformerConfig, ReformerModel

							# Build sequence
							UpperCamelCase_:							List[Any]	=						list(self.big_tokenizer.get_vocab().keys()      )[:10]
							UpperCamelCase_:							Optional[int]	=						""" """.join(snake_case_      )
							UpperCamelCase_:							Tuple	=						self.big_tokenizer.encode_plus(snake_case_						,	return_tensors="""pt"""      )
							UpperCamelCase_:							Dict	=						self.big_tokenizer.batch_encode_plus([sequence, sequence]						,	return_tensors="""pt"""      )

							UpperCamelCase_:							int	=						ReformerConfig()
							# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
							UpperCamelCase_:							int	=						encoded_sequence["""input_ids"""].shape
							UpperCamelCase_:							Optional[int]	=						ReformerModel(snake_case_      )

							# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
							assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

							with torch.no_grad():
											model(**snake_case_      )
											model(**snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       str      ):
							# fmt: off
							UpperCamelCase_:							List[Any]	=						{"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# This tokenizer does not know some characters like ")".
							# That is the reason why we use very simple texts here.
							# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
							UpperCamelCase_:							Optional[Any]	=						[
							    """This is a very simple sentence.""",
							    """The quick brown fox jumps over the lazy dog.""",
							]

							self.tokenizer_integration_test_util(
							    expected_encoding=snake_case_						,	model_name="""google/reformer-crime-and-punishment"""						,	revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""						,	padding=snake_case_						,	sequences=snake_case_						,	)








import cva
import numpy as np

class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Dict						,	snake_case_ :       float						,	snake_case_ :       int      ):

							if k in (0.04, 0.06):
											UpperCamelCase_:							Union[str, Any]	=						k
											UpperCamelCase_:							Union[str, Any]	=						window_size
							else:
											raise ValueError("""invalid k value"""      )

			def __str__(       self :       int      ):
							return str(self.k      )




			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						cva.imread(snake_case_						,	0      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						img.shape
							UpperCamelCase_:							list[list[int]]	=						[]
							UpperCamelCase_:							int	=						img.copy()
							UpperCamelCase_:							Any	=						cva.cvtColor(snake_case_						,	cva.COLOR_GRAY2RGB      )
							UpperCamelCase_,   UpperCamelCase_:							List[Any]	=						np.gradient(snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						dx**2
							UpperCamelCase_:							Dict	=						dy**2
							UpperCamelCase_:							Optional[Any]	=						dx * dy
							UpperCamelCase_:							str	=						0.04
							UpperCamelCase_:							int	=						self.window_size // 2
							for y in range(snake_case_						,	h - offset      ):
											for x in range(snake_case_						,	w - offset      ):
															UpperCamelCase_:							List[Any]	=						ixx[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							int	=						iyy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()
															UpperCamelCase_:							List[str]	=						ixy[
															    y - offset : y + offset + 1, x - offset : x + offset + 1
															].sum()

															UpperCamelCase_:							List[str]	=						(wxx * wyy) - (wxy**2)
															UpperCamelCase_:							Optional[int]	=						wxx + wyy
															UpperCamelCase_:							Dict	=						det - k * (trace**2)
															# Can change the value
															if r > 0.5:
																			corner_list.append([x, y, r]      )
																			color_img.itemset((y, x, 0)						,	0      )
																			color_img.itemset((y, x, 1)						,	0      )
																			color_img.itemset((y, x, 2)						,	255      )
							return color_img, corner_list


if __name__ == "__main__":
							lowerCamelCase_						: Optional[Any]         =  HarrisCorner(0.04, 3)
							lowerCamelCase_   ,       lowerCamelCase_						: Any         =  edge_detect.detect("""path_to_image""")
							cva.imwrite("""detect.png""", color_img)






