






"""simple docstring"""

import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
			from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowercase           =							get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class     A_				(   unittest.TestCase	):







				def      _snake_case       (				self				:    str						)					->       Optional[int]:
										__magic_name__      =  tempfile.mkdtemp()

										__magic_name__      =  ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
										__magic_name__      =  dict(zip(__lowerCamelCase    ,     range(len(__lowerCamelCase						)						)						)						)
										__magic_name__      =  Path(self.tmpdirname						)
										save_json(__lowerCamelCase    ,     save_dir / VOCAB_FILES_NAMES["vocab_file"]						)
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																copyfile(__lowerCamelCase    ,     save_dir / VOCAB_FILES_NAMES["spm_file"]						)

										__magic_name__      =  SpeechaTextTokenizer.from_pretrained(self.tmpdirname						)
										tokenizer.save_pretrained(self.tmpdirname						)

										__magic_name__      =  {
										    "feature_size": 2_4,
										    "num_mel_bins": 2_4,
										    "padding_value": 0.0,
										    "sampling_rate": 1_6_0_0_0,
										    "return_attention_mask": False,
										    "do_normalize": True,
										}
										save_json(__lowerCamelCase    ,     save_dir / FEATURE_EXTRACTOR_NAME						)







				def      _snake_case       (				self				:    List[Any]    ,     **__lowerCamelCase				:    int						)					->       Tuple:
										return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    Optional[int]    ,     **__lowerCamelCase				:    Tuple						)					->       Optional[Any]:
										return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    Dict						)					->       List[Any]:
										shutil.rmtree(self.tmpdirname						)







				def      _snake_case       (				self				:    Optional[int]						)					->       Optional[Any]:
										__magic_name__      =  self.get_tokenizer()
										__magic_name__      =  self.get_feature_extractor()

										__magic_name__      =  SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,     feature_extractor=__lowerCamelCase						)

										processor.save_pretrained(self.tmpdirname						)
										__magic_name__      =  SpeechaTextProcessor.from_pretrained(self.tmpdirname						)

										self.assertEqual(processor.tokenizer.get_vocab()    ,     tokenizer.get_vocab()						)
										self.assertIsInstance(processor.tokenizer    ,     __lowerCamelCase						)

										self.assertEqual(processor.feature_extractor.to_json_string()    ,     feature_extractor.to_json_string()						)
										self.assertIsInstance(processor.feature_extractor    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    Dict						)					->       List[Any]:
										__magic_name__      =  SpeechaTextProcessor(
										    tokenizer=self.get_tokenizer()    ,     feature_extractor=self.get_feature_extractor()						)
										processor.save_pretrained(self.tmpdirname						)

										__magic_name__      =  self.get_tokenizer(bos_token="(BOS)"    ,     eos_token="(EOS)"						)
										__magic_name__      =  self.get_feature_extractor(do_normalize=__lowerCamelCase    ,     padding_value=1.0						)

										__magic_name__      =  SpeechaTextProcessor.from_pretrained(
										    self.tmpdirname    ,     bos_token="(BOS)"    ,     eos_token="(EOS)"    ,     do_normalize=__lowerCamelCase    ,     padding_value=1.0						)

										self.assertEqual(processor.tokenizer.get_vocab()    ,     tokenizer_add_kwargs.get_vocab()						)
										self.assertIsInstance(processor.tokenizer    ,     __lowerCamelCase						)

										self.assertEqual(processor.feature_extractor.to_json_string()    ,     feature_extractor_add_kwargs.to_json_string()						)
										self.assertIsInstance(processor.feature_extractor    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    int						)					->       Union[str, Any]:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,     feature_extractor=__lowerCamelCase						)

										__magic_name__      =  floats_list((3, 1_0_0_0)						)

										__magic_name__      =  feature_extractor(__lowerCamelCase    ,     return_tensors="np"						)
										__magic_name__      =  processor(__lowerCamelCase    ,     return_tensors="np"						)

										for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum()    ,     input_processor[key].sum()    ,     delta=1e-2						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[str]:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,     feature_extractor=__lowerCamelCase						)

										__magic_name__      =  "This is a test string"

										__magic_name__      =  processor(text=__lowerCamelCase						)

										__magic_name__      =  tokenizer(__lowerCamelCase						)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]    ,     encoded_processor[key]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[Any]:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,     feature_extractor=__lowerCamelCase						)

										__magic_name__      =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__magic_name__      =  processor.batch_decode(__lowerCamelCase						)
										__magic_name__      =  tokenizer.batch_decode(__lowerCamelCase						)

										self.assertListEqual(__lowerCamelCase    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    List[Any]						)					->       int:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,     feature_extractor=__lowerCamelCase						)

										self.assertListEqual(
										    processor.model_input_names    ,     feature_extractor.model_input_names    ,     msg="`processor` and `feature_extractor` model input names do not match"    ,     )











"""simple docstring"""

import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

@require_tokenizers
class     A_				(   unittest.TestCase	):
				UpperCAmelCase__										=					EsmTokenizer







				def      _snake_case       (				self				:    List[str]						)					->       Dict:
										super().setUp()
										__magic_name__      =  tempfile.mkdtemp()
										# fmt: off
										__magic_name__      =  ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
										# fmt: on
										__magic_name__      =  os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES["vocab_file"]						)
										with open(self.vocab_file    ,     "w"    ,     encoding="utf-8"						) as vocab_writer:
																vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)







				def      _snake_case       (				self				:    Optional[Any]    ,     **__lowerCamelCase				:    str						)					->       List[PreTrainedTokenizerBase]:
										return [self.get_tokenizer(**__lowerCamelCase						)]







				def      _snake_case       (				self				:    Dict    ,     **__lowerCamelCase				:    int						)					->       PreTrainedTokenizer:
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       Optional[int]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  tokenizer.tokenize("LAGVS"						)
										self.assertListEqual(__lowerCamelCase    ,     ["L", "A", "G", "V", "S"]						)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase						)    ,     [4, 5, 6, 7, 8]						)







				def      _snake_case       (				self				:    int						)					->       Dict:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  "LAGVS"
										self.assertListEqual(tokenizer.encode(__lowerCamelCase						)    ,     [0, 4, 5, 6, 7, 8, 2]						)







				def      _snake_case       (				self				:    List[Any]						)					->       Tuple:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[str]:
										__magic_name__      =  self.tokenizer_class(self.vocab_file						)

										__magic_name__      =  ["LAGVS", "WCB"]
										__magic_name__      =  tokenizer(__lowerCamelCase    ,     padding=__lowerCamelCase						)["input_ids"]

										self.assertListEqual(__lowerCamelCase    ,     [[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										__magic_name__      =  self.get_tokenizers(fast=__lowerCamelCase						)
										for tokenizer in tokenizers:
																with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
																						__magic_name__      =  "<unk>"
																						__magic_name__      =  "<mask>"

																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)
																						__magic_name__      =  tokenizer.tokenize(__lowerCamelCase						)

																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(len(__lowerCamelCase						)    ,     1						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)
																						self.assertEqual(token_a[0]    ,     __lowerCamelCase						)



