
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
   import torch


__lowerCamelCase							= random.Random()


def     UpperCamelCase (					__lowerCamelCase  :				Any       ,      __lowerCamelCase  :				Dict=1.0       ,      __lowerCamelCase  :				Dict=None       ,      __lowerCamelCase  :				str=None					):

       if rng is None:
              snake_case :     Any           =   global_rng

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

       return values






@require_torch
class 						UpperCAmelCase	(		unittest.TestCase				):



   def __init__(self	:      List[Any]					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[Any]=7					,		snake_case__	:      int=4_00					,		snake_case__	:      List[str]=20_00					,		snake_case__	:      int=1					,		snake_case__	:      int=0.0					,		snake_case__	:      Optional[Any]=2_40_00					,		snake_case__	:      Optional[Any]=True					,		)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Dict           =   parent
          snake_case :     Tuple           =   batch_size
          snake_case :     str           =   min_seq_length
          snake_case :     Optional[Any]           =   max_seq_length
          snake_case :     int           =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          snake_case :     List[Any]           =   feature_size
          snake_case :     int           =   padding_value
          snake_case :     Optional[Any]           =   sampling_rate
          snake_case :     List[str]           =   return_attention_mask



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			Optional[Any]:


          '''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,
          }



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      str=False					,		snake_case__	:      Optional[int]=False				)	->			int:


          '''simple docstring'''


          def _flatten(snake_case__	:      Union[str, Any]				):
                 return list(itertools.chain(*__A				)				)

          if equal_length:
                 snake_case :     Optional[int]           =   floats_list((self.batch_size, self.max_seq_length)				)
          else:
                 # make sure that inputs increase in size
                 snake_case :     Optional[Any]           =   [
                     _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:
                 snake_case :     int           =   [np.asarray(__A				) for x in audio_inputs]

          return audio_inputs






@require_torch
class 						UpperCAmelCase	(		a__ ,unittest.TestCase				):
   A__      :	Union[str, Any]		  =  EncodecFeatureExtractor



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			int:


          '''simple docstring'''


          snake_case :     str           =   EnCodecFeatureExtractionTester(self				)



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			int:


          '''simple docstring'''


          snake_case :     Optional[int]           =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
          # create three inputs of length 800, 1000, and 1200
          snake_case :     Optional[Any]           =   [floats_list((1, x)				)[0] for x in range(8_00					,		14_00					,		2_00				)]
          snake_case :     Optional[Any]           =   [np.asarray(__A				) for audio_input in audio_inputs]

          # Test not batched input
          snake_case :     List[str]           =   feat_extract(audio_inputs[0]					,		return_tensors="np"				).input_values
          snake_case :     Union[str, Any]           =   feat_extract(np_audio_inputs[0]					,		return_tensors="np"				).input_values
          self.assertTrue(np.allclose(__A					,		__A					,		atol=1e-3				)				)

          # Test batched
          snake_case :     Optional[int]           =   feat_extract(__A					,		padding=__A					,		return_tensors="np"				).input_values
          snake_case :     List[str]           =   feat_extract(__A					,		padding=__A					,		return_tensors="np"				).input_values
          for enc_seq_a, enc_seq_a in zip(__A					,		__A				):
                 self.assertTrue(np.allclose(__A					,		__A					,		atol=1e-3				)				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
          snake_case :     Tuple           =   np.random.rand(1_00				).astype(np.floataa				)
          snake_case :     Any           =   np_audio_inputs.tolist()

          for inputs in [py_audio_inputs, np_audio_inputs]:
                 snake_case :     int           =   feature_extractor.pad([{"input_values": inputs}]					,		return_tensors="np"				)
                 self.assertTrue(np_processed.input_values.dtype == np.floataa				)
                 snake_case :     List[Any]           =   feature_extractor.pad([{"input_values": inputs}]					,		return_tensors="pt"				)
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]					,		snake_case__	:      Optional[int]				)	->			int:


          '''simple docstring'''


          from datasets import load_dataset

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

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



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Tuple:


          '''simple docstring'''


          snake_case :     Dict           =   torch.tensor(
              [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
               3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
               2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
               4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
               7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
               4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]				)
          # fmt: on
          snake_case :     Union[str, Any]           =   self._load_datasamples(1				)
          snake_case :     str           =   EncodecFeatureExtractor()
          snake_case :     Optional[int]           =   feature_extractor(__A					,		return_tensors="pt"				).input_values
          self.assertEquals(input_values.shape					,		(1, 1, 9_36_80)				)
          self.assertTrue(torch.allclose(input_values[0, 0, :30]					,		__A					,		atol=1e-6				)				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Tuple:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   torch.tensor(
              [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
               3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
               2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
               4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
               7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
               4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]				)
          # fmt: on
          snake_case :     Union[str, Any]           =   self._load_datasamples(1				)
          snake_case :     str           =   [np.tile(input_audio[0][None]					,		reps=(2, 1)				)]
          input_audio[0][1] *= 0.5
          snake_case :     Optional[int]           =   EncodecFeatureExtractor(feature_size=2				)
          snake_case :     str           =   feature_extractor(__A					,		return_tensors="pt"				).input_values
          self.assertEquals(input_values.shape					,		(1, 2, 9_36_80)				)
          self.assertTrue(torch.allclose(input_values[0, 0, :30]					,		__A					,		atol=1e-6				)				)
          self.assertTrue(torch.allclose(input_values[0, 1, :30]					,		EXPECTED_INPUT_VALUES * 0.5					,		atol=1e-6				)				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     str           =   self._load_datasamples(2				)
          # would be easier if the stride was like
          snake_case :     List[Any]           =   EncodecFeatureExtractor(feature_size=1					,		chunk_length_s=1					,		overlap=0.01				)

          # pad and trunc raise an error ?
          with self.assertRaisesRegex(
              __A					,		"^Both padding and truncation were set. Make sure you only set one.$"					,		):
                 snake_case :     Dict           =   feature_extractor(
                     __A					,		padding="max_length"					,		truncation=__A					,		return_tensors="pt"				).input_values

          # truncate to chunk
          snake_case :     Optional[Any]           =   feature_extractor(__A					,		truncation=__A					,		return_tensors="pt"				).input_values
          self.assertEquals(truncated_outputs.shape					,		(2, 1, 7_15_20)				)  # 2 chunks

          # force truncate to max_length
          snake_case :     List[str]           =   feature_extractor(
              __A					,		truncation=__A					,		max_length=4_80_00					,		return_tensors="pt"				).input_values
          self.assertEquals(truncated_outputs.shape					,		(2, 1, 4_80_00)				)

          # pad to chunk
          snake_case :     List[str]           =   feature_extractor(__A					,		padding=__A					,		return_tensors="pt"				).input_values
          self.assertEquals(padded_outputs.shape					,		(2, 1, 9_52_80)				)

          # pad to chunk
          snake_case :     Optional[Any]           =   feature_extractor(__A					,		return_tensors="pt"				).input_values
          self.assertEquals(truncated_outputs.shape					,		(2, 1, 9_52_80)				)

          # force pad to max length
          snake_case :     int           =   feature_extractor(
              __A					,		padding="max_length"					,		max_length=10_00_00					,		return_tensors="pt"				).input_values
          self.assertEquals(truncated_outputs.shape					,		(2, 1, 10_00_00)				)

          # force no pad
          with self.assertRaisesRegex(
              __A					,		"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"					,		):
                 snake_case :     Optional[Any]           =   feature_extractor(__A					,		padding=__A					,		return_tensors="pt"				).input_values

          snake_case :     int           =   feature_extractor(input_audio[0]					,		padding=__A					,		return_tensors="pt"				).input_values
          self.assertEquals(truncated_outputs.shape					,		(1, 1, 9_36_80)				)

          # no pad if no chunk_length_s
          snake_case :     Any           =   None
          with self.assertRaisesRegex(
              __A					,		"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"					,		):
                 snake_case :     int           =   feature_extractor(__A					,		padding=__A					,		return_tensors="pt"				).input_values

          snake_case :     Any           =   feature_extractor(input_audio[0]					,		padding=__A					,		return_tensors="pt"				).input_values
          self.assertEquals(truncated_outputs.shape					,		(1, 1, 9_36_80)				)

          # no pad if no overlap
          snake_case :     List[Any]           =   2
          snake_case :     Any           =   None
          with self.assertRaisesRegex(
              __A					,		"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"					,		):
                 snake_case :     Optional[int]           =   feature_extractor(__A					,		padding=__A					,		return_tensors="pt"				).input_values

          snake_case :     List[str]           =   feature_extractor(input_audio[0]					,		padding=__A					,		return_tensors="pt"				).input_values
          self.assertEquals(truncated_outputs.shape					,		(1, 1, 9_36_80)				)







import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand


def     UpperCamelCase      (					__lowerCamelCase  :				int					):
       return EnvironmentCommand()






class 						UpperCAmelCase	(		A_				):



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      ArgumentParser				)	->			Any:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   parser.add_parser("env"					,		help="Print relevant system environment info."				)
          download_parser.set_defaults(func=snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     List[str]           =   {
              "`datasets` version": version,
              "Platform": platform.platform(),
              "Python version": platform.python_version(),
              "Huggingface_hub version": huggingface_hub.__version__,
              "PyArrow version": pyarrow.__version__,
              "Pandas version": pandas.__version__,
          }

          print("\nCopy-and-paste the text below in your GitHub issue.\n"				)
          print(self.format_dict(snake_case__				)				)

          return info



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Tuple				)	->			Any:


          '''simple docstring'''


          return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]				) + "\n"





