




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

lowerCamelCase       :	int									=     random.Random()

def     _SCREAMING_SNAKE_CASE    (						lowercase			: str			,							lowercase			: Tuple=1.0			,							lowercase			: Optional[int]=None			,							lowercase			: List[Any]=None  ):


      '''simple docstring'''



      if rng is None:
            lowerCamelCase_	  =					global_rng

      lowerCamelCase_	  =					[]
      for batch_idx in range(shape[0]  ):
            values.append([]  )
            for _ in range(shape[1]  ):
                  values[-1].append(rng.random() * scale  )

      return values







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[Any]		,							A_      :						str		,							A_      :						int=7		,							A_      :						Union[str, Any]=400		,							A_      :						Dict=2000		,							A_      :						Optional[int]=2048		,							A_      :						List[Any]=128		,							A_      :						str=1		,							A_      :						Optional[int]=512		,							A_      :						Optional[int]=30		,							A_      :						List[Any]=44100		,							)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					min_seq_length
       lowerCamelCase_	  =					max_seq_length
       lowerCamelCase_	  =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       lowerCamelCase_	  =					spectrogram_length
       lowerCamelCase_	  =					feature_size
       lowerCamelCase_	  =					num_audio_channels
       lowerCamelCase_	  =					hop_length
       lowerCamelCase_	  =					chunk_length
       lowerCamelCase_	  =					sampling_rate

 def        a__				(       self      :						Union[str, Any]						)     ->			str:

       """simple docstring"""






       return {
           "spectrogram_length": self.spectrogram_length,
           "feature_size": self.feature_size,
           "num_audio_channels": self.num_audio_channels,
           "hop_length": self.hop_length,
           "chunk_length": self.chunk_length,
           "sampling_rate": self.sampling_rate,
       }





 def        a__				(       self      :						Any		,							A_      :						Dict=False		,							A_      :						List[str]=False						)     ->			List[str]:

       """simple docstring"""






       def _flatten(A_      :						List[Any]						):
             return list(itertools.chain(*A_						)						)

       if equal_length:
             lowerCamelCase_	  =					[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
       else:
             # make sure that inputs increase in size
             lowerCamelCase_	  =					[
                 floats_list((x, self.feature_size)						)
                 for x in range(self.min_seq_length		,							self.max_seq_length		,							self.seq_length_diff						)
             ]
       if numpify:
             lowerCamelCase_	  =					[np.asarray(A_						) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class 						A(		UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		TvltFeatureExtractor

 def        a__				(       self      :						Any						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					TvltFeatureExtractionTester(self						)

 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.feature_extraction_class(**self.feat_extract_dict						)
       self.assertTrue(hasattr(A_		,							'spectrogram_length'						)						)
       self.assertTrue(hasattr(A_		,							'feature_size'						)						)
       self.assertTrue(hasattr(A_		,							'num_audio_channels'						)						)
       self.assertTrue(hasattr(A_		,							'hop_length'						)						)
       self.assertTrue(hasattr(A_		,							'chunk_length'						)						)
       self.assertTrue(hasattr(A_		,							'sampling_rate'						)						)

 def        a__				(       self      :						List[str]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.feature_extraction_class(**self.feat_extract_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase_	  =					feat_extract_first.save_pretrained(A_						)[0]
             check_json_file_has_correct_format(A_						)
             lowerCamelCase_	  =					self.feature_extraction_class.from_pretrained(A_						)

       lowerCamelCase_	  =					feat_extract_first.to_dict()
       lowerCamelCase_	  =					feat_extract_second.to_dict()
       lowerCamelCase_	  =					dict_first.pop('mel_filters'						)
       lowerCamelCase_	  =					dict_second.pop('mel_filters'						)
       self.assertTrue(np.allclose(A_		,							A_						)						)
       self.assertEqual(A_		,							A_						)

 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.feature_extraction_class(**self.feat_extract_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase_	  =					os.path.join(A_		,							'feat_extract.json'						)
             feat_extract_first.to_json_file(A_						)
             lowerCamelCase_	  =					self.feature_extraction_class.from_json_file(A_						)

       lowerCamelCase_	  =					feat_extract_first.to_dict()
       lowerCamelCase_	  =					feat_extract_second.to_dict()
       lowerCamelCase_	  =					dict_first.pop('mel_filters'						)
       lowerCamelCase_	  =					dict_second.pop('mel_filters'						)
       self.assertTrue(np.allclose(A_		,							A_						)						)
       self.assertEqual(A_		,							A_						)

 def        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.feature_extraction_class(**self.feat_extract_dict						)

       # create three inputs of length 800, 1000, and 1200
       lowerCamelCase_	  =					[floats_list((1, x)						)[0] for x in range(800		,							1400		,							200						)]
       lowerCamelCase_	  =					[np.asarray(A_						) for speech_input in speech_inputs]

       # Test not batched input
       lowerCamelCase_	  =					feature_extractor(np_speech_inputs[0]		,							return_tensors='np'		,							sampling_rate=44100						).audio_values

       self.assertTrue(encoded_audios.ndim == 4						)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)

       # Test batched
       lowerCamelCase_	  =					feature_extractor(A_		,							return_tensors='np'		,							sampling_rate=44100						).audio_values

       self.assertTrue(encoded_audios.ndim == 4						)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)

       # Test audio masking
       lowerCamelCase_	  =					feature_extractor(
           A_		,							return_tensors='np'		,							sampling_rate=44100		,							mask_audio=A_						).audio_values

       self.assertTrue(encoded_audios.ndim == 4						)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)

       # Test 2-D numpy arrays are batched.
       lowerCamelCase_	  =					[floats_list((1, x)						)[0] for x in (800, 800, 800)]
       lowerCamelCase_	  =					np.asarray(A_						)
       lowerCamelCase_	  =					feature_extractor(A_		,							return_tensors='np'		,							sampling_rate=44100						).audio_values
       self.assertTrue(encoded_audios.ndim == 4						)
       self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size						)
       self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length						)
       self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels						)

 def        a__				(       self      :						Union[str, Any]		,							A_      :						Union[str, Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					load_dataset('hf-internal-testing/librispeech_asr_dummy'		,							'clean'		,							split='validation'						)
       # automatic decoding with librispeech
       lowerCamelCase_	  =					ds.sort('id'						).select(range(A_						)						)[:num_samples]['audio']

       return [x["array"] for x in speech_samples]





 def        a__				(       self      :						str						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self._load_datasamples(1						)
       lowerCamelCase_	  =					TvltFeatureExtractor()
       lowerCamelCase_	  =					feature_extractor(A_		,							return_tensors='pt'						).audio_values

       self.assertEquals(audio_values.shape		,							(1, 1, 192, 128)						)

       lowerCamelCase_	  =					torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]						)
       self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,							A_		,							atol=1E-4						)						)









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


lowerCamelCase       :	str									=     logging.get_logger(__name__)

lowerCamelCase       :	List[str]									=     {
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		'''gpt_neox_japanese'''

 def __init__(       self      :						int		,							A_      :						Dict=32000		,							A_      :						List[Any]=2560		,							A_      :						Dict=32		,							A_      :						Union[str, Any]=32		,							A_      :						List[Any]=4		,							A_      :						List[str]="gelu"		,							A_      :						Dict=1.00		,							A_      :						int=10000		,							A_      :						Dict=2048		,							A_      :						Dict=0.02		,							A_      :						Any=1E-5		,							A_      :						Union[str, Any]=True		,							A_      :						int=31996		,							A_      :						List[str]=31999		,							A_      :						List[Any]=0.1		,							A_      :						List[Any]=0.0		,							**A_      :						Tuple		,							)     ->			Dict:

       """simple docstring"""






       super().__init__(bos_token_id=A_		,							eos_token_id=A_		,							**A_						)
       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_multiple_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					rotary_pct
       lowerCamelCase_	  =					rotary_emb_base
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					layer_norm_eps
       lowerCamelCase_	  =					use_cache
       lowerCamelCase_	  =					attention_dropout
       lowerCamelCase_	  =					hidden_dropout



