






'''simple docstring'''







import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       DebertaTokenizer
		_UpperCamelCase						=       True
		_UpperCamelCase						=       DebertaTokenizerFast






		def       UpperCamelCase_       (   self							):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase__								=	[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """[UNK]""",
								]
								lowerCamelCase__								=	dict(zip(_lowerCAmelCase ,range(len(_lowerCAmelCase							)							)							)							)
								lowerCamelCase__								=	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								lowerCamelCase__								=	{"""unk_token""": """[UNK]"""}

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






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								kwargs.update(self.special_tokens_map							)
								return self.tokenizer_class.from_pretrained(self.tmpdirname ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								lowerCamelCase__								=	"""lower newer"""
								lowerCamelCase__								=	"""lower newer"""
								return input_text, output_text






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_tokenizer()
								lowerCamelCase__								=	"""lower newer"""
								lowerCamelCase__								=	["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								lowerCamelCase__								=	tokenizer.tokenize(_lowerCAmelCase							)
								self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	tokens + [tokenizer.unk_token]
								lowerCamelCase__								=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase							) ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.get_tokenizer()
								lowerCamelCase__								=	tokenizer("""Hello""" ,"""World"""							)
								lowerCamelCase__								=	[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
								self.assertListEqual(tokd["""token_type_ids"""] ,_lowerCAmelCase							)






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""							)

								lowerCamelCase__								=	tokenizer.encode("""sequence builders""" ,add_special_tokens=_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.encode("""multi-sequence build""" ,add_special_tokens=_lowerCAmelCase							)

								lowerCamelCase__								=	tokenizer.encode(
								    """sequence builders""" ,add_special_tokens=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.encode(
								    """sequence builders""" ,"""multi-sequence build""" ,add_special_tokens=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase							)

								lowerCamelCase__								=	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase							)
								lowerCamelCase__								=	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase ,_lowerCAmelCase							)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	[self.tokenizer_class]
								if self.test_rust_tokenizer:
														tokenizer_classes.append(self.rust_tokenizer_class							)

								for tokenizer_class in tokenizer_classes:
														lowerCamelCase__								=	tokenizer_class.from_pretrained("""microsoft/deberta-base"""							)

														lowerCamelCase__								=	[
														    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
														    """ALBERT incorporates two parameter reduction techniques""",
														    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
														    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
														    """ vocabulary embedding.""",
														]

														lowerCamelCase__								=	tokenizer(_lowerCAmelCase ,padding=_lowerCAmelCase							)
														lowerCamelCase__								=	[tokenizer.decode(_lowerCAmelCase ,skip_special_tokens=_lowerCAmelCase							) for seq in encoding["""input_ids"""]]

														# fmt: off
														lowerCamelCase__								=	{
														    """input_ids""": [
														        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
														        [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
														        [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
														    ],
														    """token_type_ids""": [
														        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
														        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
														        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
														    ],
														    """attention_mask""": [
														        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
														        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
														        [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, 1, 1, 1]
														    ]
														}
														# fmt: on

														lowerCamelCase__								=	[
														    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
														    """ALBERT incorporates two parameter reduction techniques""",
														    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
														    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
														    """ vocabulary embedding.""",
														]

														self.assertDictEqual(encoding.data ,_lowerCAmelCase							)

														for expected, decoded in zip(_lowerCAmelCase ,_lowerCAmelCase							):
																				self.assertEqual(_lowerCAmelCase ,_lowerCAmelCase							)


import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






