



"""simple docstring"""






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

snake_case   					=				random.Random()




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=1.0  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None	)						->				List[str]:
  if rng is None:
    _snake_case   	=      global_rng

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

  return values



class     UpperCAmelCase    (	unittest.TestCase  ):



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[Any]=7							,       __lowerCamelCase    :	List[Any]=4_0_0							,       __lowerCamelCase    :	Any=2_0_0_0							,       __lowerCamelCase    :	Any=2_0_4_8							,       __lowerCamelCase    :	Any=1_2_8							,       __lowerCamelCase    :	Any=1							,       __lowerCamelCase    :	Optional[int]=5_1_2							,       __lowerCamelCase    :	Tuple=3_0							,       __lowerCamelCase    :	List[Any]=4_4_1_0_0							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      min_seq_length
     _snake_case   	=      max_seq_length
     _snake_case   	=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     _snake_case   	=      spectrogram_length
     _snake_case   	=      feature_size
     _snake_case   	=      num_audio_channels
     _snake_case   	=      hop_length
     _snake_case   	=      chunk_length
     _snake_case   	=      sampling_rate



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Any=False							,       __lowerCamelCase    :	int=False				):



     """simple docstring"""
     def _flatten(__lowerCamelCase    :	List[str]				):
       return list(itertools.chain(*__lowerCamelCase				)				)

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



@require_torch
@require_torchaudio
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Tuple           =  TvltFeatureExtractor



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      TvltFeatureExtractionTester(self				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''spectrogram_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''feature_size'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''num_audio_channels'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''hop_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''chunk_length'''				)				)
     self.assertTrue(hasattr(__lowerCamelCase							,       '''sampling_rate'''				)				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

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

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



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

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

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



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     # Initialize feature_extractor
     _snake_case   	=      self.feature_extraction_class(**self.feat_extract_dict				)

     # create three inputs of length 800, 1000, and 1200
     _snake_case   	=      [floats_list((1, x)				)[0] for x in range(8_0_0							,       1_4_0_0							,       2_0_0				)]
     _snake_case   	=      [np.asarray(__lowerCamelCase				) for speech_input in speech_inputs]

     # Test not batched input
     _snake_case   	=      feature_extractor(np_speech_inputs[0]							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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
     _snake_case   	=      feature_extractor(
         __lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0							,       mask_audio=__lowerCamelCase				).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.
     _snake_case   	=      [floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     _snake_case   	=      np.asarray(__lowerCamelCase				)
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''np'''							,       sampling_rate=4_4_1_0_0				).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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]				):



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

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





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self._load_datasamples(1				)
     _snake_case   	=      TvltFeatureExtractor()
     _snake_case   	=      feature_extractor(__lowerCamelCase							,       return_tensors='''pt'''				).audio_values

     self.assertEquals(audio_values.shape							,       (1, 1, 1_9_2, 1_2_8)				)

     _snake_case   	=      torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]				)
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,       __lowerCamelCase							,       atol=1E-4				)				)








from collections import OrderedDict
from typing import Mapping

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)