





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 	UpperCamelCase_							(  __lowercase					,	unittest.TestCase     ):
							lowerCamelCase_							     =       LEDTokenizer
							lowerCamelCase_							     =       LEDTokenizerFast
							lowerCamelCase_							     =       True


							def   _snake_case	(				self     :Dict		) -> Optional[Any]:



									"""simple docstring"""



									super().setUp()
									SCREAMING_SNAKE_CASE__								=   [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									SCREAMING_SNAKE_CASE__								=   dict(zip(_A				,					range(len(_A		)		)		)		)
									SCREAMING_SNAKE_CASE__								=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									SCREAMING_SNAKE_CASE__								=   {"""unk_token""": """<unk>"""}

									SCREAMING_SNAKE_CASE__								=   os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__								=   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(_A		) + """\n"""		)
									with open(self.merges_file				,					"""w"""				,					encoding="""utf-8"""		) as fp:
											fp.write("""\n""".join(_A		)		)


							def   _snake_case	(				self     :List[Any]				,					**__A     :Dict		) -> Dict:



									"""simple docstring"""



									kwargs.update(self.special_tokens_map		)
									return self.tokenizer_class.from_pretrained(self.tmpdirname				,					**_A		)


							def   _snake_case	(				self     :Any				,					**__A     :Any		) -> List[Any]:



									"""simple docstring"""



									kwargs.update(self.special_tokens_map		)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,					**_A		)


							def   _snake_case	(				self     :Tuple				,					__A     :str		) -> Tuple:



									"""simple docstring"""



									return "lower newer", "lower newer"


							@cached_property
							def   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""		)


							@cached_property
							def   _snake_case	(				self     :Any		) -> Tuple:



									"""simple docstring"""



									return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""		)


							@require_torch
							def   _snake_case	(				self     :Tuple		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									SCREAMING_SNAKE_CASE__								=   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=   tokenizer(_A				,					max_length=len(_A		)				,					padding=_A				,					return_tensors="""pt"""		)
											self.assertIsInstance(_A				,					_A		)

											self.assertEqual((2, 9)				,					batch.input_ids.shape		)
											self.assertEqual((2, 9)				,					batch.attention_mask.shape		)
											SCREAMING_SNAKE_CASE__								=   batch.input_ids.tolist()[0]
											self.assertListEqual(_A				,					_A		)


							@require_torch
							def   _snake_case	(				self     :List[Any]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=   tokenizer(_A				,					padding=_A				,					return_tensors="""pt"""		)
											self.assertIn("""input_ids"""				,					_A		)
											self.assertIn("""attention_mask"""				,					_A		)
											self.assertNotIn("""labels"""				,					_A		)
											self.assertNotIn("""decoder_attention_mask"""				,					_A		)


							@require_torch
							def   _snake_case	(				self     :List[Any]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=   tokenizer(text_target=_A				,					max_length=32				,					padding="""max_length"""				,					return_tensors="""pt"""		)
											self.assertEqual(32				,					targets["""input_ids"""].shape[1]		)


							@require_torch
							def   _snake_case	(				self     :List[Any]		) -> List[str]:



									"""simple docstring"""



									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=   tokenizer(
											    ["""I am a small frog""" * 1024, """I am a small frog"""]				,					padding=_A				,					truncation=_A				,					return_tensors="""pt"""		)
											self.assertIsInstance(_A				,					_A		)
											self.assertEqual(batch.input_ids.shape				,					(2, 5122)		)


							@require_torch
							def   _snake_case	(				self     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ["""A long paragraph for summarization."""]
									SCREAMING_SNAKE_CASE__								=   [
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=   tokenizer(_A				,					return_tensors="""pt"""		)
											SCREAMING_SNAKE_CASE__								=   tokenizer(text_target=_A				,					return_tensors="""pt"""		)
											SCREAMING_SNAKE_CASE__								=   inputs["""input_ids"""]
											SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :str		) -> Tuple:



									"""simple docstring"""



									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											SCREAMING_SNAKE_CASE__								=   ["""Summary of the text.""", """Another summary."""]
											SCREAMING_SNAKE_CASE__								=   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

											SCREAMING_SNAKE_CASE__								=   tokenizer(_A				,					padding=_A		)
											SCREAMING_SNAKE_CASE__								=   [[0] * len(_A		) for x in encoded_output["""input_ids"""]]
											SCREAMING_SNAKE_CASE__								=   tokenizer.pad(_A		)
											self.assertSequenceEqual(outputs["""global_attention_mask"""]				,					_A		)


							def   _snake_case	(				self     :Any		) -> int:



									"""simple docstring"""



									pass


							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__								=   self.rust_tokenizer_class.from_pretrained(_A				,					**_A		)
													SCREAMING_SNAKE_CASE__								=   self.tokenizer_class.from_pretrained(_A				,					**_A		)
													SCREAMING_SNAKE_CASE__								=   """A, <mask> AllenNLP sentence."""
													SCREAMING_SNAKE_CASE__								=   tokenizer_r.encode_plus(_A				,					add_special_tokens=_A				,					return_token_type_ids=_A		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_p.encode_plus(_A				,					add_special_tokens=_A				,					return_token_type_ids=_A		)
													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"""]		)				,					)

													SCREAMING_SNAKE_CASE__								=   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
													SCREAMING_SNAKE_CASE__								=   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)
													self.assertSequenceEqual(tokens_p["""input_ids"""]				,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
													self.assertSequenceEqual(tokens_r["""input_ids"""]				,					[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

													self.assertSequenceEqual(
													    _A				,					["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
													self.assertSequenceEqual(
													    _A				,					["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

					try:
										if dataset.shape[1] != value_array.shape[1]:
															_UpperCamelCase			     =     (
															    '''Wrong input data\'s shape... '''
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(__snake_case						)
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError('''Wrong shape'''						)

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

					return answer
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return np.dot(__snake_case  ,     __snake_case						) / (norm(__snake_case						) * norm(__snake_case						))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
