
import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__lowerCAmelCase			:     int           =   random.Random()

if is_torch_available():
    import torch

def a__    (       A_,     A_=1.0,     A_=None,     A_=None					):






  '''simple docstring'''





  if rng is None:
    __magic_name__				     =       global_rng

  __magic_name__				     =       []
  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     ):


  '''simple docstring'''







  def __init__( self			: int			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: str=7			,							UpperCamelCase__			: Union[str, Any]=400			,							UpperCamelCase__			: List[str]=2000			,							UpperCamelCase__			: Optional[int]=1			,							UpperCamelCase__			: List[str]=0.0			,							UpperCamelCase__			: Optional[int]=1_6000			,							UpperCamelCase__			: str=True			,							UpperCamelCase__			: Tuple=True			,							)			->	Any:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       min_seq_length
    __magic_name__				     =       max_seq_length
    __magic_name__				     =       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
    __magic_name__				     =       feature_size
    __magic_name__				     =       padding_value
    __magic_name__				     =       sampling_rate
    __magic_name__				     =       return_attention_mask
    __magic_name__				     =       do_normalize
  def 							_lowercase							( self			: Optional[int]    )			->	Tuple:







    """simple docstring"""






    return {
        "feature_size": self.feature_size,
        "padding_value": self.padding_value,
        "sampling_rate": self.sampling_rate,
        "return_attention_mask": self.return_attention_mask,
        "do_normalize": self.do_normalize,
    }





  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: Union[str, Any]=False			,							UpperCamelCase__			: Dict=False    )			->	List[str]:







    """simple docstring"""






    def _flatten(UpperCamelCase__			: List[str]    ):
      return list(itertools.chain(*UpperCamelCase__    )    )

    if equal_length:
      __magic_name__				     =       floats_list((self.batch_size, self.max_seq_length)    )
    else:
      # make sure that inputs increase in size
      __magic_name__				     =       [
          _flatten(floats_list((x, self.feature_size)    )    )
          for x in range(self.min_seq_length			,							self.max_seq_length			,							self.seq_length_diff    )
      ]

    if numpify:
      __magic_name__				     =       [np.asarray(UpperCamelCase__    ) for x in speech_inputs]

    return speech_inputs







@require_torch
@require_torchaudio
class        UpperCAmelCase_			( _A  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		ASTFeatureExtractor
  def 							_lowercase							( self			: Optional[int]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       ASTFeatureExtractionTester(self    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
    # create three inputs of length 800, 1000, and 1200
    __magic_name__				     =       [floats_list((1, x)    )[0] for x in range(800			,							1400			,							200    )]
    __magic_name__				     =       [np.asarray(UpperCamelCase__    ) for speech_input in speech_inputs]

    # Test not batched input
    __magic_name__				     =       feat_extract(speech_inputs[0]			,							return_tensors="""np"""    ).input_values
    __magic_name__				     =       feat_extract(np_speech_inputs[0]			,							return_tensors="""np"""    ).input_values
    self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-3    )    )

    # Test batched
    __magic_name__				     =       feat_extract(UpperCamelCase__			,							padding=UpperCamelCase__			,							return_tensors="""np"""    ).input_values
    __magic_name__				     =       feat_extract(UpperCamelCase__			,							padding=UpperCamelCase__			,							return_tensors="""np"""    ).input_values
    for enc_seq_a, enc_seq_a in zip(UpperCamelCase__			,							UpperCamelCase__    ):
      self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-3    )    )

    # Test 2-D numpy arrays are batched.
    __magic_name__				     =       [floats_list((1, x)    )[0] for x in (800, 800, 800)]
    __magic_name__				     =       np.asarray(UpperCamelCase__    )
    __magic_name__				     =       feat_extract(UpperCamelCase__			,							return_tensors="""np"""    ).input_values
    __magic_name__				     =       feat_extract(UpperCamelCase__			,							return_tensors="""np"""    ).input_values
    for enc_seq_a, enc_seq_a in zip(UpperCamelCase__			,							UpperCamelCase__    ):
      self.assertTrue(np.allclose(UpperCamelCase__			,							UpperCamelCase__			,							atol=1E-3    )    )
  @require_torch
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    import torch

    __magic_name__				     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
    __magic_name__				     =       np.random.rand(100    ).astype(np.floataa    )
    __magic_name__				     =       np_speech_inputs.tolist()

    for inputs in [py_speech_inputs, np_speech_inputs]:
      __magic_name__				     =       feature_extractor.pad([{"""input_values""": inputs}]			,							return_tensors="""np"""    )
      self.assertTrue(np_processed.input_values.dtype == np.floataa    )
      __magic_name__				     =       feature_extractor.pad([{"""input_values""": inputs}]			,							return_tensors="""pt"""    )
      self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: str    )			->	Tuple:







    """simple docstring"""






    from datasets import load_dataset

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

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





  @require_torch
  def 							_lowercase							( self			: Any    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       torch.tensor(
        [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
         -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
         -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
         -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]    )
    # fmt: on

    __magic_name__				     =       self._load_datasamples(1    )
    __magic_name__				     =       ASTFeatureExtractor()
    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""pt"""    ).input_values
    self.assertEquals(input_values.shape			,							(1, 1024, 128)    )
    self.assertTrue(torch.allclose(input_values[0, 0, :30]			,							UpperCamelCase__			,							atol=1E-4    )    )








import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				parser.parse_args()

					with open(args.src_path					,						'r'  ) as src_file, open(args.evaluation_set					,						'w'  ) as eval_file, open(
					    args.gold_data_path					,						'w'  ) as gold_file:
										__A	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




