


'''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	(    lowercase__						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				DebertaTokenizer
				_snake_case						       =				True
				_snake_case						       =				DebertaTokenizerFast

				def 	a__		(     self							)    ->  Tuple:
					super().setUp()

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

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

				def 	a__		(     self							,      **a_							)    ->  int:
					kwargs.update(self.special_tokens_map							)
					return self.tokenizer_class.from_pretrained(self.tmpdirname							,      **lowercase_							)

				def 	a__		(     self							,      a_							)    ->  Optional[int]:
					lowercase						:		List[str]   					=		"lower newer"
					lowercase						:		Any   					=		"lower newer"
					return input_text, output_text

				def 	a__		(     self							)    ->  str:
					lowercase						:		Optional[Any]   					=		self.get_tokenizer()
					lowercase						:		List[str]   					=		"lower newer"
					lowercase						:		Optional[int]   					=		["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
					lowercase						:		Tuple   					=		tokenizer.tokenize(lowercase_							)
					self.assertListEqual(lowercase_							,      lowercase_							)

					lowercase						:		Optional[Any]   					=		tokens + [tokenizer.unk_token]
					lowercase						:		Tuple   					=		[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)							,      lowercase_							)

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		str   					=		self.get_tokenizer()
					lowercase						:		Any   					=		tokenizer("Hello"							,      "World"							)
					lowercase						:		Tuple   					=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
					self.assertListEqual(tokd["token_type_ids"]							,      lowercase_							)

				@slow
				def 	a__		(     self							)    ->  int:
					lowercase						:		List[str]   					=		self.tokenizer_class.from_pretrained("microsoft/deberta-base"							)

					lowercase						:		List[str]   					=		tokenizer.encode("sequence builders"							,      add_special_tokens=lowercase_							)
					lowercase						:		List[Any]   					=		tokenizer.encode("multi-sequence build"							,      add_special_tokens=lowercase_							)

					lowercase						:		Tuple   					=		tokenizer.encode(
					    "sequence builders"							,      add_special_tokens=lowercase_							,      add_prefix_space=lowercase_							)
					lowercase						:		Optional[Any]   					=		tokenizer.encode(
					    "sequence builders"							,      "multi-sequence build"							,      add_special_tokens=lowercase_							,      add_prefix_space=lowercase_							)

					lowercase						:		Optional[Any]   					=		tokenizer.build_inputs_with_special_tokens(lowercase_							)
					lowercase						:		str   					=		tokenizer.build_inputs_with_special_tokens(lowercase_							,      lowercase_							)

					assert encoded_sentence == encoded_text_from_decode
					assert encoded_pair == encoded_pair_from_decode


				@slow
				def 	a__		(     self							)    ->  str:
					lowercase						:		List[Any]   					=		[self.tokenizer_class]
					if self.test_rust_tokenizer:
						tokenizer_classes.append(self.rust_tokenizer_class							)

					for tokenizer_class in tokenizer_classes:
						lowercase						:		Optional[int]   					=		tokenizer_class.from_pretrained("microsoft/deberta-base"							)

						lowercase						:		Any   					=		[
						    "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.",
						]

						lowercase						:		Any   					=		tokenizer(lowercase_							,      padding=lowercase_							)
						lowercase						:		List[str]   					=		[tokenizer.decode(lowercase_							,      skip_special_tokens=lowercase_							) for seq in encoding["input_ids"]]

						# fmt: off
						lowercase						:		Optional[int]   					=		{
						    "input_ids": [
						        [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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

						lowercase						:		Optional[Any]   					=		[
						    "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							,      lowercase_							)

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

