





"""simple docstring"""





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


_lowerCamelCase  					=     logging.get_logger(__name__)

_lowerCamelCase  					=     {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}





class       snake_case		(       A__       ):
      lowerCamelCase__      =			'''pegasus_x'''
      lowerCamelCase__      =			['''past_key_values''']
      lowerCamelCase__      =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

      def __init__(      self       :Dict						,							_lowerCamelCase       :List[Any]=9_6_1_0_3						,							_lowerCamelCase       :int=1_6_3_8_4						,							_lowerCamelCase       :Tuple=1_6						,							_lowerCamelCase       :Any=4_0_9_6						,							_lowerCamelCase       :List[str]=1_6						,							_lowerCamelCase       :Dict=1_6						,							_lowerCamelCase       :Union[str, Any]=4_0_9_6						,							_lowerCamelCase       :Optional[Any]=1_6						,							_lowerCamelCase       :Any=0.0						,							_lowerCamelCase       :List[str]=0.0						,							_lowerCamelCase       :Any=True						,							_lowerCamelCase       :Union[str, Any]=True						,							_lowerCamelCase       :int="gelu"						,							_lowerCamelCase       :Dict=1_0_2_4						,							_lowerCamelCase       :Union[str, Any]=0.1						,							_lowerCamelCase       :Optional[Any]=0.0						,							_lowerCamelCase       :int=0.0						,							_lowerCamelCase       :int=0.0_2						,							_lowerCamelCase       :int=0						,							_lowerCamelCase       :int=True						,							_lowerCamelCase       :Optional[int]=0						,							_lowerCamelCase       :Union[str, Any]=1						,							_lowerCamelCase       :Union[str, Any]=1						,							_lowerCamelCase       :Optional[Any]=3_2						,							_lowerCamelCase       :Any=5_1_2						,							_lowerCamelCase       :List[Any]=True						,							**_lowerCamelCase       :List[Any]						,							):
       __SCREAMING_SNAKE_CASE			:      int 							=	vocab_size
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	max_position_embeddings
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	d_model
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	encoder_ffn_dim
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	encoder_layers
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	encoder_attention_heads
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	decoder_ffn_dim
       __SCREAMING_SNAKE_CASE			:      str 							=	decoder_layers
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	decoder_attention_heads
       __SCREAMING_SNAKE_CASE			:      int 							=	dropout
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	attention_dropout
       __SCREAMING_SNAKE_CASE			:      Dict 							=	activation_dropout
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	activation_function
       __SCREAMING_SNAKE_CASE			:      Dict 							=	init_std
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	encoder_layerdrop
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	decoder_layerdrop
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	use_cache
       __SCREAMING_SNAKE_CASE			:      str 							=	encoder_layers
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	scale_embedding  # scale factor will be sqrt(d_model) if True

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	num_global_tokens
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	block_size
       __SCREAMING_SNAKE_CASE			:      str 							=	stagger_local_blocks

       super().__init__(
           pad_token_id=__snake_case						,							eos_token_id=__snake_case						,							is_encoder_decoder=__snake_case						,							decoder_start_token_id=__snake_case						,							forced_eos_token_id=__snake_case						,							**__snake_case						,							)

      @property
      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       return self.encoder_attention_heads



      @property
      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       return self.d_model








"""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


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





@require_torch
class       snake_case		(       unittest.TestCase       ):

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	tempfile.mkdtemp()

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	SpeechTaTokenizer(_lowerCamelCase						)
       tokenizer.save_pretrained(self.tmpdirname						)

       __SCREAMING_SNAKE_CASE			:      Optional[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,
       }

       __SCREAMING_SNAKE_CASE			:      Any 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :str						,							**_lowerCamelCase       :str						):
       return SpeechTaTokenizer.from_pretrained(self.tmpdirname						,							**_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						,							**_lowerCamelCase       :Dict						):
       return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname						,							**_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						):
       shutil.rmtree(self.tmpdirname						)

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

       __SCREAMING_SNAKE_CASE			:      Any 							=	SpeechTaProcessor(tokenizer=_lowerCamelCase						,							feature_extractor=_lowerCamelCase						)

       processor.save_pretrained(self.tmpdirname						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Union[str, 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			:      List[str] 							=	self.get_feature_extractor(do_normalize=_lowerCamelCase						,							padding_value=1.0						)

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

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	SpeechTaProcessor(tokenizer=_lowerCamelCase						,							feature_extractor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      int 							=	floats_list((3, 1_0_0_0)						)

       __SCREAMING_SNAKE_CASE			:      int 							=	feature_extractor(audio=_lowerCamelCase						,							return_tensors='''np'''						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.get_feature_extractor()
       __SCREAMING_SNAKE_CASE			:      int 							=	self.get_tokenizer()

       __SCREAMING_SNAKE_CASE			:      Any 							=	SpeechTaProcessor(tokenizer=_lowerCamelCase						,							feature_extractor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	floats_list((3, 1_0_0_0)						)

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	feature_extractor(audio_target=_lowerCamelCase						,							return_tensors='''np'''						)
       __SCREAMING_SNAKE_CASE			:      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 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.get_feature_extractor()
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.get_tokenizer()

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	SpeechTaProcessor(tokenizer=_lowerCamelCase						,							feature_extractor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      int 							=	'''This is a test string'''

       __SCREAMING_SNAKE_CASE			:      str 							=	processor(text=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	tokenizer(_lowerCamelCase						)

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

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

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	SpeechTaProcessor(tokenizer=_lowerCamelCase						,							feature_extractor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Dict 							=	'''This is a test string'''

       __SCREAMING_SNAKE_CASE			:      str 							=	processor(text_target=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	tokenizer(_lowerCamelCase						)

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

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.get_feature_extractor()
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	self.get_tokenizer()

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	SpeechTaProcessor(tokenizer=_lowerCamelCase						,							feature_extractor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	processor.batch_decode(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	tokenizer.batch_decode(_lowerCamelCase						)

       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)



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

       __SCREAMING_SNAKE_CASE			:      str 							=	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'''						,							)

