import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


UpperCAmelCase      		=					get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')




@require_torch
class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Tuple	)			->				Any:
        _UpperCamelCase  :							List[Any]	    =	tempfile.mkdtemp()

        _UpperCamelCase  :							str	    =	SpeechTaTokenizer(lowerCamelCase	)
        tokenizer.save_pretrained(self.tmpdirname	)

        _UpperCamelCase  :							Union[str, Any]	    =	{
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 1_6_0_0_0,
            'do_normalize': False,
            'num_mel_bins': 8_0,
            'hop_length': 1_6,
            'win_length': 6_4,
            'win_function': 'hann_window',
            'fmin': 8_0,
            'fmax': 7_6_0_0,
            'mel_floor': 1e-10,
            'reduction_factor': 2,
            'return_attention_mask': True,
        }

        _UpperCamelCase  :							List[str]	    =	os.path.join(self.tmpdirname							,      lowerCamelCase	)
        with open(self.feature_extraction_file							,      'w'							,      encoding='utf-8'	) as fp:
               fp.write(json.dumps(lowerCamelCase	) + '\n'	)

 def        __snake_case       (      self							:		Union[str, Any]							,      **lowerCamelCase							:		int	)			->				Tuple:
        return SpeechTaTokenizer.from_pretrained(self.tmpdirname							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]							,      **lowerCamelCase							:		Optional[int]	)			->				Optional[Any]:
        return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname							,      **lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				str:
        shutil.rmtree(self.tmpdirname	)

 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        _UpperCamelCase  :							int	    =	self.get_tokenizer()
        _UpperCamelCase  :							Dict	    =	self.get_feature_extractor()

        _UpperCamelCase  :							Optional[Any]	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        processor.save_pretrained(self.tmpdirname	)
        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor.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							:		str	)			->				int:
        _UpperCamelCase  :							Union[str, Any]	    =	SpeechTaProcessor(tokenizer=self.get_tokenizer()							,      feature_extractor=self.get_feature_extractor()	)
        processor.save_pretrained(self.tmpdirname	)

        _UpperCamelCase  :							Any	    =	self.get_tokenizer(bos_token='(BOS)'							,      eos_token='(EOS)'	)
        _UpperCamelCase  :							Any	    =	self.get_feature_extractor(do_normalize=lowerCamelCase							,      padding_value=1.0	)

        _UpperCamelCase  :							int	    =	SpeechTaProcessor.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							:		Union[str, Any]	)			->				Tuple:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							Dict	    =	self.get_tokenizer()

        _UpperCamelCase  :							Dict	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	floats_list((3, 1_0_0_0)	)

        _UpperCamelCase  :							Union[str, Any]	    =	feature_extractor(audio=lowerCamelCase							,      return_tensors='np'	)
        _UpperCamelCase  :							Optional[int]	    =	processor(audio=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							:		List[str]	)			->				int:
        _UpperCamelCase  :							List[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							int	    =	self.get_tokenizer()

        _UpperCamelCase  :							Any	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	floats_list((3, 1_0_0_0)	)

        _UpperCamelCase  :							Any	    =	feature_extractor(audio_target=lowerCamelCase							,      return_tensors='np'	)
        _UpperCamelCase  :							List[Any]	    =	processor(audio_target=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							:		Tuple	)			->				Any:
        _UpperCamelCase  :							Dict	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							List[str]	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	'This is a test string'

        _UpperCamelCase  :							Dict	    =	processor(text=lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	tokenizer(lowerCamelCase	)

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

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

        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	'This is a test string'

        _UpperCamelCase  :							Any	    =	processor(text_target=lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	tokenizer(lowerCamelCase	)

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

 def        __snake_case       (      self							:		List[str]	)			->				Union[str, Any]:
        _UpperCamelCase  :							Optional[Any]	    =	self.get_feature_extractor()
        _UpperCamelCase  :							str	    =	self.get_tokenizer()

        _UpperCamelCase  :							Tuple	    =	SpeechTaProcessor(tokenizer=lowerCamelCase							,      feature_extractor=lowerCamelCase	)

        _UpperCamelCase  :							str	    =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :							str	    =	processor.batch_decode(lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	tokenizer.batch_decode(lowerCamelCase	)

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		str	)			->				Any:
        _UpperCamelCase  :							int	    =	self.get_feature_extractor()
        _UpperCamelCase  :							List[Any]	    =	self.get_tokenizer()

        _UpperCamelCase  :							Union[str, Any]	    =	SpeechTaProcessor(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'							,      )






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`.'				)






