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


__snake_case         =     random.Random()





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=1.0		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None    )->	Optional[Any]:






      '''simple docstring'''





      if rng is None:
            UpperCAmelCase     :			Optional[int]     	=global_rng

      UpperCAmelCase     :			str     	=[]
      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       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=7		, snake_case__=400		, snake_case__=2000		, snake_case__=1		, snake_case__=0.0		, snake_case__=2_4000		, snake_case__=True		, )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=parent
       UpperCAmelCase     :			int     	=batch_size
       UpperCAmelCase     :			List[Any]     	=min_seq_length
       UpperCAmelCase     :			Dict     	=max_seq_length
       UpperCAmelCase     :			List[str]     	=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCAmelCase     :			List[str]     	=feature_size
       UpperCAmelCase     :			List[str]     	=padding_value
       UpperCAmelCase     :			Tuple     	=sampling_rate
       UpperCAmelCase     :			List[Any]     	=return_attention_mask
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''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 						UpperCAmelCase__							(	self		, snake_case__=False		, snake_case__=False    )						->				Tuple:
       '''simple docstring'''





       def _flatten(snake_case__    ):
             return list(itertools.chain(*snake_case__    )    )

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

       return audio_inputs







@require_torch
class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Optional[int] 	=   EncodecFeatureExtractor
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=EnCodecFeatureExtractionTester(self    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
       # create three inputs of length 800, 1000, and 1200
       UpperCAmelCase     :			List[str]     	=[floats_list((1, x)    )[0] for x in range(800		, 1400		, 200    )]
       UpperCAmelCase     :			Optional[Any]     	=[np.asarray(snake_case__    ) for audio_input in audio_inputs]

       # Test not batched input
       UpperCAmelCase     :			Optional[Any]     	=feat_extract(audio_inputs[0]		, return_tensors='''np'''    ).input_values
       UpperCAmelCase     :			Tuple     	=feat_extract(np_audio_inputs[0]		, return_tensors='''np'''    ).input_values
       self.assertTrue(np.allclose(snake_case__		, snake_case__		, atol=1e-3    )    )

       # Test batched
       UpperCAmelCase     :			Dict     	=feat_extract(snake_case__		, padding=snake_case__		, return_tensors='''np'''    ).input_values
       UpperCAmelCase     :			Union[str, Any]     	=feat_extract(snake_case__		, padding=snake_case__		, return_tensors='''np'''    ).input_values
       for enc_seq_a, enc_seq_a in zip(snake_case__		, snake_case__    ):
             self.assertTrue(np.allclose(snake_case__		, snake_case__		, atol=1e-3    )    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
       UpperCAmelCase     :			Any     	=np.random.rand(100    ).astype(np.floataa    )
       UpperCAmelCase     :			Any     	=np_audio_inputs.tolist()

       for inputs in [py_audio_inputs, np_audio_inputs]:
             UpperCAmelCase     :			int     	=feature_extractor.pad([{'''input_values''': inputs}]		, return_tensors='''np'''    )
             self.assertTrue(np_processed.input_values.dtype == np.floataa    )
             UpperCAmelCase     :			str     	=feature_extractor.pad([{'''input_values''': inputs}]		, return_tensors='''pt'''    )
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       from datasets import load_dataset

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

       return [x["array"] for x in audio_samples]
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=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
       UpperCAmelCase     :			List[str]     	=self._load_datasamples(1    )
       UpperCAmelCase     :			Any     	=EncodecFeatureExtractor()
       UpperCAmelCase     :			Union[str, Any]     	=feature_extractor(snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(input_values.shape		, (1, 1, 9_3680)    )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]		, snake_case__		, atol=1e-6    )    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=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
       UpperCAmelCase     :			Optional[Any]     	=self._load_datasamples(1    )
       UpperCAmelCase     :			str     	=[np.tile(input_audio[0][None]		, reps=(2, 1)    )]
       input_audio[0][1] *= 0.5
       UpperCAmelCase     :			List[Any]     	=EncodecFeatureExtractor(feature_size=2    )
       UpperCAmelCase     :			Union[str, Any]     	=feature_extractor(snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(input_values.shape		, (1, 2, 9_3680)    )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]		, snake_case__		, atol=1e-6    )    )
       self.assertTrue(torch.allclose(input_values[0, 1, :30]		, EXPECTED_INPUT_VALUES * 0.5		, atol=1e-6    )    )






 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





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

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

       # truncate to chunk
       UpperCAmelCase     :			List[str]     	=feature_extractor(snake_case__		, truncation=snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(truncated_outputs.shape		, (2, 1, 7_1520)    )  # 2 chunks

       # force truncate to max_length
       UpperCAmelCase     :			Dict     	=feature_extractor(
           snake_case__		, truncation=snake_case__		, max_length=4_8000		, return_tensors='''pt'''    ).input_values
       self.assertEquals(truncated_outputs.shape		, (2, 1, 4_8000)    )

       # pad to chunk
       UpperCAmelCase     :			Union[str, Any]     	=feature_extractor(snake_case__		, padding=snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(padded_outputs.shape		, (2, 1, 9_5280)    )

       # pad to chunk
       UpperCAmelCase     :			Optional[Any]     	=feature_extractor(snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(truncated_outputs.shape		, (2, 1, 9_5280)    )

       # force pad to max length
       UpperCAmelCase     :			Tuple     	=feature_extractor(
           snake_case__		, padding='''max_length'''		, max_length=10_0000		, return_tensors='''pt'''    ).input_values
       self.assertEquals(truncated_outputs.shape		, (2, 1, 10_0000)    )

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

       UpperCAmelCase     :			Union[str, Any]     	=feature_extractor(input_audio[0]		, padding=snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(truncated_outputs.shape		, (1, 1, 9_3680)    )

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

       UpperCAmelCase     :			int     	=feature_extractor(input_audio[0]		, padding=snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(truncated_outputs.shape		, (1, 1, 9_3680)    )

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

       UpperCAmelCase     :			Union[str, Any]     	=feature_extractor(input_audio[0]		, padding=snake_case__		, return_tensors='''pt'''    ).input_values
       self.assertEquals(truncated_outputs.shape		, (1, 1, 9_3680)    )

'''simple docstring'''
def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float			)							->	list[int]:
  if hue < 0 or hue > 360:
    raise Exception('''hue should be between 0 and 360'''			)

  if saturation < 0 or saturation > 1:
    raise Exception('''saturation should be between 0 and 1'''			)

  if value < 0 or value > 1:
    raise Exception('''value should be between 0 and 1'''			)

  lowercase				:							List[str]									=   value * saturation
  lowercase				:							Optional[Any]									=   hue / 60
  lowercase				:							Dict									=   chroma * (1 - abs(hue_section % 2 - 1			))
  lowercase				:							List[Any]									=   value - chroma

  if hue_section >= 0 and hue_section <= 1:
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							int									=   round(255 * (match_value)			)
  elif hue_section > 1 and hue_section <= 2:
    lowercase				:							Optional[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Any									=   round(255 * (chroma + match_value)			)
    lowercase				:							Any									=   round(255 * (match_value)			)
  elif hue_section > 2 and hue_section <= 3:
    lowercase				:							Dict									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
  elif hue_section > 3 and hue_section <= 4:
    lowercase				:							List[str]									=   round(255 * (match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (chroma + match_value)			)
  elif hue_section > 4 and hue_section <= 5:
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
  else:
    lowercase				:							int									=   round(255 * (chroma + match_value)			)
    lowercase				:							Optional[Any]									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)

  return [red, green, blue]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[float]:
  if red < 0 or red > 255:
    raise Exception('''red should be between 0 and 255'''			)

  if green < 0 or green > 255:
    raise Exception('''green should be between 0 and 255'''			)

  if blue < 0 or blue > 255:
    raise Exception('''blue should be between 0 and 255'''			)

  lowercase				:							str									=   red / 255
  lowercase				:							Union[str, Any]									=   green / 255
  lowercase				:							Union[str, Any]									=   blue / 255
  lowercase				:							str									=   max(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							int									=   value - min(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							Optional[int]									=   0 if value == 0 else chroma / value

  if chroma == 0:
    lowercase				:							Dict									=   0.0
  elif value == float_red:
    lowercase				:							Tuple									=   60 * (0 + (float_green - float_blue) / chroma)
  elif value == float_green:
    lowercase				:							Optional[int]									=   60 * (2 + (float_blue - float_red) / chroma)
  else:
    lowercase				:							Dict									=   60 * (4 + (float_red - float_green) / chroma)

  lowercase				:							List[str]									=   (hue + 360) % 360

  return [hue, saturation, value]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list[float] ,   __UpperCAmelCase  :					list[float]			)							->	bool:
  lowercase				:							int									=   abs(hsv_a[0] - hsv_a[0]			) < 0.2
  lowercase				:							Optional[int]									=   abs(hsv_a[1] - hsv_a[1]			) < 0.002
  lowercase				:							Dict									=   abs(hsv_a[2] - hsv_a[2]			) < 0.002

  return check_hue and check_saturation and check_value