



'''simple docstring'''







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


SCREAMING_SNAKE_CASE__     : Union[str, Any]				=   get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_torch
class 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      tempfile.mkdtemp()

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      SpeechTaTokenizer(lowercase__	)
					tokenizer.save_pretrained(self.tmpdirname	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      {
					    'feature_size': 1,
					    'padding_value': 0.0,
					    'sampling_rate': 16_000,
					    'do_normalize': False,
					    'num_mel_bins': 80,
					    'hop_length': 16,
					    'win_length': 64,
					    'win_function': 'hann_window',
					    'fmin': 80,
					    'fmax': 7_600,
					    'mel_floor': 1E-10,
					    'reduction_factor': 2,
					    'return_attention_mask': True,
					}

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      os.path.join(self.tmpdirname      ,       lowercase__	)
					with open(self.feature_extraction_file      ,       'w'      ,       encoding='utf-8'	) as fp:
							fp.write(json.dumps(lowercase__	) + '\n'	)






			def 				_lowercase	(   self  :							str      ,       **SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







					return SpeechTaTokenizer.from_pretrained(self.tmpdirname      ,       **lowercase__	)






			def 				_lowercase	(   self  :							Tuple      ,       **SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







					return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname      ,       **lowercase__	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					shutil.rmtree(self.tmpdirname	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_tokenizer()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.get_feature_extractor()

					SCREAMING_SNAKE_CASE_						:Any										=      SpeechTaProcessor(tokenizer=lowercase__      ,       feature_extractor=lowercase__	)

					processor.save_pretrained(self.tmpdirname	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      SpeechTaProcessor.from_pretrained(self.tmpdirname	)

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

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






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SpeechTaProcessor(tokenizer=self.get_tokenizer()      ,       feature_extractor=self.get_feature_extractor()	)
					processor.save_pretrained(self.tmpdirname	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_tokenizer(bos_token='(BOS)'      ,       eos_token='(EOS)'	)
					SCREAMING_SNAKE_CASE_						:int										=      self.get_feature_extractor(do_normalize=lowercase__      ,       padding_value=1.0	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SpeechTaProcessor.from_pretrained(
					    self.tmpdirname      ,       bos_token='(BOS)'      ,       eos_token='(EOS)'      ,       do_normalize=lowercase__      ,       padding_value=1.0	)

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

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






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_feature_extractor()
					SCREAMING_SNAKE_CASE_						:int										=      self.get_tokenizer()

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      SpeechTaProcessor(tokenizer=lowercase__      ,       feature_extractor=lowercase__	)

					SCREAMING_SNAKE_CASE_						:str										=      floats_list((3, 1_000)	)

					SCREAMING_SNAKE_CASE_						:int										=      feature_extractor(audio=lowercase__      ,       return_tensors='np'	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      processor(audio=lowercase__      ,       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 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_feature_extractor()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_tokenizer()

					SCREAMING_SNAKE_CASE_						:List[Any]										=      SpeechTaProcessor(tokenizer=lowercase__      ,       feature_extractor=lowercase__	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      floats_list((3, 1_000)	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      feature_extractor(audio_target=lowercase__      ,       return_tensors='np'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      processor(audio_target=lowercase__      ,       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 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.get_feature_extractor()
					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_tokenizer()

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      SpeechTaProcessor(tokenizer=lowercase__      ,       feature_extractor=lowercase__	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      'This is a test string'

					SCREAMING_SNAKE_CASE_						:Dict										=      processor(text=lowercase__	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      tokenizer(lowercase__	)

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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.get_feature_extractor()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_tokenizer()

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      SpeechTaProcessor(tokenizer=lowercase__      ,       feature_extractor=lowercase__	)

					SCREAMING_SNAKE_CASE_						:Any										=      'This is a test string'

					SCREAMING_SNAKE_CASE_						:Tuple										=      processor(text_target=lowercase__	)
					SCREAMING_SNAKE_CASE_						:Any										=      tokenizer(lowercase__	)

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






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.get_feature_extractor()
					SCREAMING_SNAKE_CASE_						:str										=      self.get_tokenizer()

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      SpeechTaProcessor(tokenizer=lowercase__      ,       feature_extractor=lowercase__	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					SCREAMING_SNAKE_CASE_						:Any										=      processor.batch_decode(lowercase__	)
					SCREAMING_SNAKE_CASE_						:str										=      tokenizer.batch_decode(lowercase__	)

					self.assertListEqual(lowercase__      ,       lowercase__	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.get_feature_extractor()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.get_tokenizer()

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SpeechTaProcessor(tokenizer=lowercase__      ,       feature_extractor=lowercase__	)

					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 Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   'longt5'
 UpperCAmelCase_      					=   ['past_key_values']
 UpperCAmelCase_      					=   {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    Union[str, Any]=32_128     ,       lowercase__   :    Union[str, Any]=512     ,       lowercase__   :    List[Any]=64     ,       lowercase__   :    Tuple=2_048     ,       lowercase__   :    List[Any]=6     ,       lowercase__   :    Any=None     ,       lowercase__   :    List[str]=8     ,       lowercase__   :    str=127     ,       lowercase__   :    Any=16     ,       lowercase__   :    str=32     ,       lowercase__   :    str=128     ,       lowercase__   :    Union[str, Any]=0.1     ,       lowercase__   :    List[Any]=1e-6     ,       lowercase__   :    Optional[Any]=1.0     ,       lowercase__   :    str="relu"     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any="local"     ,       lowercase__   :    str=True     ,       lowercase__   :    Union[str, Any]=0     ,       lowercase__   :    Optional[int]=1     ,       **lowercase__   :    str     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   vocab_size
        lowerCAmelCase__	      =   d_model
        lowerCAmelCase__	      =   d_kv
        lowerCAmelCase__	      =   d_ff
        lowerCAmelCase__	      =   num_layers
        # default = symmetry
        lowerCAmelCase__	      =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   local_radius
        lowerCAmelCase__	      =   global_block_size
        lowerCAmelCase__	      =   relative_attention_num_buckets
        lowerCAmelCase__	      =   relative_attention_max_distance
        lowerCAmelCase__	      =   dropout_rate
        lowerCAmelCase__	      =   layer_norm_epsilon
        lowerCAmelCase__	      =   initializer_factor
        lowerCAmelCase__	      =   feed_forward_proj
        lowerCAmelCase__	      =   encoder_attention_type
        lowerCAmelCase__	      =   use_cache

        lowerCAmelCase__	      =   self.feed_forward_proj.split('-')
        lowerCAmelCase__	      =   act_info[-1]
        lowerCAmelCase__	      =   act_info[0] == 'gated'

        if len(lowercase__) > 1 and act_info[0] != "gated" or len(lowercase__) > 2:
               raise ValueError(
                   F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                   'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                   '\'gated-gelu\' or \'relu\'')

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
               lowerCAmelCase__	      =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase__     ,       eos_token_id=lowercase__     ,       is_encoder_decoder=lowercase__     ,       **lowercase__     ,       )






class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 @property
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
               lowerCAmelCase__	      =   'past_encoder_sequence + sequence'
               lowerCAmelCase__	      =   {0: 'batch'}
               lowerCAmelCase__	      =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
               lowerCAmelCase__	      =   {0: 'batch', 1: 'decoder_sequence'}
               lowerCAmelCase__	      =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
               self.fill_with_past_key_values_(lowercase__     ,       direction='inputs')

        return common_inputs




 @property
 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        return 13
