
'''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 	__lowercase				(  _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		str	      =			LEDTokenizer
			lowerCamelCase			:		Optional[Any]	      =			LEDTokenizerFast
			lowerCamelCase			:		Any	      =			True

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

								lowerCamelCase_					:      List[Any]     =		os.path.join(self.tmpdirname			,   VOCAB_FILES_NAMES['''vocab_file''']      )
								lowerCamelCase_					:      Union[str, 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(A      ) + '''\n'''      )
								with open(self.merges_file			,   '''w'''			,   encoding='''utf-8'''      ) as fp:
													fp.write('''\n'''.join(A      )      )

			def        UpperCAmelCase__ (self			,   **A      ):
								kwargs.update(self.special_tokens_map      )
								return self.tokenizer_class.from_pretrained(self.tmpdirname			,   **A      )

			def        UpperCAmelCase__ (self			,   **A      ):
								kwargs.update(self.special_tokens_map      )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,   **A      )

			def        UpperCAmelCase__ (self			,   A      ):
								return "lower newer", "lower newer"

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

			@cached_property
			def        UpperCAmelCase__ (self      ):
								return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''      )

			@require_torch
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								lowerCamelCase_					:      List[Any]     =		[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase_					:      int     =		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      )
													lowerCamelCase_					:      Dict     =		batch.input_ids.tolist()[0]
													self.assertListEqual(A			,   A      )

			@require_torch
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase_					:      int     =		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        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[Any]     =		[
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase_					:      Dict     =		tokenizer(text_target=A			,   max_length=3_2			,   padding='''max_length'''			,   return_tensors='''pt'''      )
													self.assertEqual(3_2			,   targets['''input_ids'''].shape[1]      )

			@require_torch
			def        UpperCAmelCase__ (self      ):
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase_					:      Optional[Any]     =		tokenizer(
													    ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']			,   padding=A			,   truncation=A			,   return_tensors='''pt'''      )
													self.assertIsInstance(A			,   A      )
													self.assertEqual(batch.input_ids.shape			,   (2, 5_1_2_2)      )

			@require_torch
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		['''A long paragraph for summarization.''']
								lowerCamelCase_					:      Optional[int]     =		[
								    '''Summary of the text.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase_					:      str     =		tokenizer(A			,   return_tensors='''pt'''      )
													lowerCamelCase_					:      List[Any]     =		tokenizer(text_target=A			,   return_tensors='''pt'''      )
													lowerCamelCase_					:      List[Any]     =		inputs['''input_ids''']
													lowerCamelCase_					:      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        UpperCAmelCase__ (self      ):
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase_					:      Tuple     =		['''Summary of the text.''', '''Another summary.''']
													lowerCamelCase_					:      Union[str, Any]     =		[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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

			def        UpperCAmelCase__ (self      ):
								pass






			def        UpperCAmelCase__ (self      ):
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																		lowerCamelCase_					:      List[str]     =		self.rust_tokenizer_class.from_pretrained(A			,   **A      )
																		lowerCamelCase_					:      Any     =		self.tokenizer_class.from_pretrained(A			,   **A      )
																		lowerCamelCase_					:      Any     =		'''A, <mask> AllenNLP sentence.'''
																		lowerCamelCase_					:      Union[str, Any]     =		tokenizer_r.encode_plus(A			,   add_special_tokens=A			,   return_token_type_ids=A      )
																		lowerCamelCase_					:      Union[str, Any]     =		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''']      )			,   )

																		lowerCamelCase_					:      Dict     =		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
																		lowerCamelCase_					:      Optional[int]     =		tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )
																		self.assertSequenceEqual(tokens_p['''input_ids''']			,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
																		self.assertSequenceEqual(tokens_r['''input_ids''']			,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

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




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


