

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


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





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


							"""simple docstring"""





							def 							A__	(			self				):
												__lowerCAmelCase       =				tempfile.mkdtemp()

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

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

												__lowerCAmelCase       =				{
												    '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(_A       ,    save_dir / FEATURE_EXTRACTOR_NAME				)




							def 							A__	(			self       ,    **_A				):
												return SpeechaTextTokenizer.from_pretrained(self.tmpdirname       ,    **_A				)




							def 							A__	(			self       ,    **_A				):
												return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname       ,    **_A				)




							def 							A__	(			self				):
												shutil.rmtree(self.tmpdirname				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_tokenizer()
												__lowerCAmelCase       =				self.get_feature_extractor()

												__lowerCAmelCase       =				SpeechaTextProcessor(tokenizer=_A       ,    feature_extractor=_A				)

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

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

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




							def 							A__	(			self				):
												__lowerCAmelCase       =				SpeechaTextProcessor(
												    tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()				)
												processor.save_pretrained(self.tmpdirname				)

												__lowerCAmelCase       =				self.get_tokenizer(bos_token='(BOS)'       ,    eos_token='(EOS)'				)
												__lowerCAmelCase       =				self.get_feature_extractor(do_normalize=_A       ,    padding_value=1.0				)

												__lowerCAmelCase       =				SpeechaTextProcessor.from_pretrained(
												    self.tmpdirname       ,    bos_token='(BOS)'       ,    eos_token='(EOS)'       ,    do_normalize=_A       ,    padding_value=1.0				)

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

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




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_feature_extractor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				SpeechaTextProcessor(tokenizer=_A       ,    feature_extractor=_A				)

												__lowerCAmelCase       =				floats_list((3, 1_0_0_0)				)

												__lowerCAmelCase       =				feature_extractor(_A       ,    return_tensors='np'				)
												__lowerCAmelCase       =				processor(_A       ,    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 							A__	(			self				):
												__lowerCAmelCase       =				self.get_feature_extractor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				SpeechaTextProcessor(tokenizer=_A       ,    feature_extractor=_A				)

												__lowerCAmelCase       =				'This is a test string'

												__lowerCAmelCase       =				processor(text=_A				)

												__lowerCAmelCase       =				tokenizer(_A				)

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




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_feature_extractor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				SpeechaTextProcessor(tokenizer=_A       ,    feature_extractor=_A				)

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

												__lowerCAmelCase       =				processor.batch_decode(_A				)
												__lowerCAmelCase       =				tokenizer.batch_decode(_A				)

												self.assertListEqual(_A       ,    _A				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.get_feature_extractor()
												__lowerCAmelCase       =				self.get_tokenizer()

												__lowerCAmelCase       =				SpeechaTextProcessor(tokenizer=_A       ,    feature_extractor=_A				)

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









from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""prophetnet"""
							__UpperCAmelCase      =["""past_key_values"""]
							__UpperCAmelCase      ={
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(			self       ,    _A = 0.1       ,    _A = "gelu"       ,    _A = 3_0_5_2_2       ,    _A = 1_0_2_4       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 0.1       ,    _A = 0.1       ,    _A = 5_1_2       ,    _A = 0.02       ,    _A = True       ,    _A = True       ,    _A = 0       ,    _A = 2       ,    _A = 3_2       ,    _A = 1_2_8       ,    _A = False       ,    _A = 0.0       ,    _A = True       ,    _A = 0       ,    _A = 1       ,    _A = 2       ,    **_A       ,    ):
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				hidden_size
												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				num_encoder_layers
												__lowerCAmelCase       =				num_encoder_attention_heads
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				num_decoder_layers
												__lowerCAmelCase       =				num_decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												# parameters for prophetnet
												__lowerCAmelCase       =				ngram
												__lowerCAmelCase       =				num_buckets
												__lowerCAmelCase       =				relative_max_distance
												__lowerCAmelCase       =				disable_ngram_loss
												__lowerCAmelCase       =				eps

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache

												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    is_encoder_decoder=_A       ,    add_cross_attention=_A       ,    decoder_start_token_id=_A       ,    **_A       ,    )




							@property
							def 							A__	(			self				):
												return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 							A__	(			self       ,    _A				):
												raise NotImplementedError(
												    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
												    ' `num_decoder_layers`.'				)






