import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
  from transformers import WhisperFeatureExtractor

if is_torch_available():
  import torch

SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		random.Random()



def 	__A		(      _A				,					_A=1.0				,					_A=None				,					_A=None       ):



 """simple docstring"""


 if rng is None:
  __a							=	global_rng

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

 return values
@require_torch
@require_torchaudio
class  A_			(							unittest.TestCase			):

  def __init__(		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=7  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=4_00  ,				__SCREAMING_SNAKE_CASE			:   List[str]=20_00  ,				__SCREAMING_SNAKE_CASE			:   int=10  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1_60  ,				__SCREAMING_SNAKE_CASE			:   Tuple=8  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=40_00  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=False  ,				__SCREAMING_SNAKE_CASE			:   int=True  ,				):
   __a							=	parent
   __a							=	batch_size
   __a							=	min_seq_length
   __a							=	max_seq_length
   __a							=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __a							=	padding_value
   __a							=	sampling_rate
   __a							=	return_attention_mask
   __a							=	do_normalize
   __a							=	feature_size
   __a							=	chunk_length
   __a							=	hop_length

  def 		_UpperCAmelCase (		self			:   Tuple							):
   return {
       "feature_size": self.feature_size,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "return_attention_mask": self.return_attention_mask,
       "do_normalize": self.do_normalize,
   }

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=False  ,				__SCREAMING_SNAKE_CASE			:   Tuple=False							):
   def _flatten(__SCREAMING_SNAKE_CASE			:   int							):
    return list(itertools.chain(*__SCREAMING_SNAKE_CASE							)							)

   if equal_length:
    __a							=	[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
   else:
    # make sure that inputs increase in size
    __a							=	[
        floats_list((x, self.feature_size)							)
        for x in range(self.min_seq_length  ,				self.max_seq_length  ,				self.seq_length_diff							)
    ]
   if numpify:
    __a							=	[np.asarray(__SCREAMING_SNAKE_CASE							) for x in speech_inputs]
   return speech_inputs
@require_torch
@require_torchaudio
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      WhisperFeatureExtractor if is_speech_available() else None

  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	WhisperFeatureExtractionTester(self							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self.feature_extraction_class(**self.feat_extract_dict							)

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

   __a							=	feat_extract_first.to_dict()
   __a							=	feat_extract_second.to_dict()
   __a							=	feat_extract_first.mel_filters
   __a							=	feat_extract_second.mel_filters
   self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)
   self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.feature_extraction_class(**self.feat_extract_dict							)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"feat_extract.json"							)
    feat_extract_first.to_json_file(__SCREAMING_SNAKE_CASE							)
    __a							=	self.feature_extraction_class.from_json_file(__SCREAMING_SNAKE_CASE							)

   __a							=	feat_extract_first.to_dict()
   __a							=	feat_extract_second.to_dict()
   __a							=	feat_extract_first.mel_filters
   __a							=	feat_extract_second.mel_filters
   self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)							)
   self.assertEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __a							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
   # create three inputs of length 800, 1000, and 1200
   __a							=	[floats_list((1, x)							)[0] for x in range(8_00  ,				14_00  ,				2_00							)]
   __a							=	[np.asarray(__SCREAMING_SNAKE_CASE							) for speech_input in speech_inputs]

   # Test feature size
   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				padding="max_length"  ,				return_tensors="np"							).input_features
   self.assertTrue(input_features.ndim == 3							)
   self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames							)
   self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size							)

   # Test not batched input
   __a							=	feature_extractor(speech_inputs[0]  ,				return_tensors="np"							).input_features
   __a							=	feature_extractor(np_speech_inputs[0]  ,				return_tensors="np"							).input_features
   self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-3							)							)

   # Test batched
   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							).input_features
   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							).input_features
   for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-3							)							)

   # Test 2-D numpy arrays are batched.
   __a							=	[floats_list((1, x)							)[0] for x in (8_00, 8_00, 8_00)]
   __a							=	np.asarray(__SCREAMING_SNAKE_CASE							)
   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							).input_features
   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							).input_features
   for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-3							)							)

   # Test truncation required
   __a							=	[floats_list((1, x)							)[0] for x in range(2_00  ,				(feature_extractor.n_samples + 5_00)  ,				2_00							)]
   __a							=	[np.asarray(__SCREAMING_SNAKE_CASE							) for speech_input in speech_inputs]

   __a							=	[x[: feature_extractor.n_samples] for x in speech_inputs]
   __a							=	[np.asarray(__SCREAMING_SNAKE_CASE							) for speech_input in speech_inputs_truncated]

   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							).input_features
   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				return_tensors="np"							).input_features
   for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-3							)							)

  def 		_UpperCAmelCase (		self			:   List[str]							):
   import torch

   __a							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
   __a							=	np.random.rand(1_00  ,				32							).astype(np.floataa							)
   __a							=	np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    __a							=	feature_extractor.pad([{"input_features": inputs}]  ,				return_tensors="np"							)
    self.assertTrue(np_processed.input_features.dtype == np.floataa							)
    __a							=	feature_extractor.pad([{"input_features": inputs}]  ,				return_tensors="pt"							)
    self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   __a							=	load_dataset("hf-internal-testing/librispeech_asr_dummy"  ,				"clean"  ,				split="validation"							)
   # automatic decoding with librispeech
   __a							=	ds.sort("id"							).select(range(__SCREAMING_SNAKE_CASE							)							)[:num_samples]["audio"]

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

  def 		_UpperCAmelCase (		self			:   Any							):
   # fmt: off
   __a							=	torch.tensor(
       [
           0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
           0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
           0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
           -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
       ]							)
   # fmt: on

   __a							=	self._load_datasamples(1							)
   __a							=	WhisperFeatureExtractor()
   __a							=	feature_extractor(__SCREAMING_SNAKE_CASE  ,				return_tensors="pt"							).input_features
   self.assertEqual(input_features.shape  ,				(1, 80, 30_00)							)
   self.assertTrue(torch.allclose(input_features[0, 0, :30]  ,				__SCREAMING_SNAKE_CASE  ,				atol=1E-4							)							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
   __a							=	self._load_datasamples(1							)[0]
   __a							=	((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
   __a							=	feat_extract.zero_mean_unit_var_norm([audio]  ,				attention_mask=__SCREAMING_SNAKE_CASE							)[0]

   self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE							) < 1E-3							)							)
   self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE							) - 1							) < 1E-3							)							)



import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (abs(source - target       ) / target) < 0.01






@pytest.mark.integration
def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	_TestCommandArgs(dataset=_A				,					all_configs=_A				,					save_infos=_A       )
 __a							=	TestCommand(*_A       )
 test_command.run()
 __a							=	os.path.join(_A				,					"README.md"       )
 assert os.path.exists(_A       )
 __a							=	DatasetInfosDict.from_directory(_A       )
 __a							=	DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"       )       ),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]       )       ),
                     "langs": Sequence(Value("string"       )       ),
                     "spans": Sequence(Value("string"       )       ),
                 }       )				,					splits=[
                 {
                     "name": "train",
                     "num_bytes": 235_1563,
                     "num_examples": 1_0000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 23_8418,
                     "num_examples": 1000,
                 },
             ]				,					download_size=394_0680				,					dataset_size=258_9981				,					)
     }       )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  __a							,  __a							=	getattr(dataset_infos["default"]				,					_A       ), getattr(expected_dataset_infos["default"]				,					_A       )
  if key == "num_bytes":
   assert is_apercent_close(_A				,					_A       )
  elif key == "splits":
   assert list(_A       ) == list(_A       )
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes				,					expected[split].num_bytes       )
  else:
   result == expected


