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


lowercase_  							=			random.Random()







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Optional[int]		,							__SCREAMING_SNAKE_CASE       :					int=1.0		,							__SCREAMING_SNAKE_CASE       :					Union[str, Any]=None		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]=None   ):







    '''simple docstring'''
    if rng is None:
        __snake_case       :				Union[str, 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      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):

       def __init__(							self					:     int			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     Dict=7			,			_lowerCAmelCase					:     int=4_00			,			_lowerCAmelCase					:     int=20_00			,			_lowerCAmelCase					:     Union[str, Any]=1			,			_lowerCAmelCase					:     Any=0.0			,			_lowerCAmelCase					:     Tuple=2_40_00			,			_lowerCAmelCase					:     List[str]=True			,			):
           __snake_case       :				str 			=    parent
           __snake_case       :				Dict 			=    batch_size
           __snake_case       :				Union[str, Any] 			=    min_seq_length
           __snake_case       :				Optional[Any] 			=    max_seq_length
           __snake_case       :				Dict 			=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           __snake_case       :				List[Any] 			=    feature_size
           __snake_case       :				Optional[Any] 			=    padding_value
           __snake_case       :				List[Any] 			=    sampling_rate
           __snake_case       :				List[Any] 			=    return_attention_mask

       def 			snake_case__    (							self					:     Optional[Any]			):
           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }





       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     int=False			,			_lowerCAmelCase					:     int=False			):
           def _flatten(_lowerCAmelCase					:     Union[str, Any]			):
               return list(itertools.chain(*UpperCamelCase_			)			)

           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       :				List[str] 			=    [
                   _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       :				List[Any] 			=    [np.asarray(UpperCamelCase_			) for x in audio_inputs]

           return audio_inputs







@require_torch
class      SCREAMING_SNAKE_CASE__					(  lowerCAmelCase_   ,							unittest.TestCase    ):
       A							:	Optional[Any]          =	EncodecFeatureExtractor

       def 			snake_case__    (							self					:     Optional[Any]			):
           __snake_case       :				Optional[Any] 			=    EnCodecFeatureExtractionTester(self			)

       def 			snake_case__    (							self					:     Optional[Any]			):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           __snake_case       :				List[str] 			=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           # create three inputs of length 800, 1000, and 1200
           __snake_case       :				int 			=    [floats_list((1, x)			)[0] for x in range(8_00			,			14_00			,			2_00			)]
           __snake_case       :				Any 			=    [np.asarray(UpperCamelCase_			) for audio_input in audio_inputs]

           # Test not batched input
           __snake_case       :				Tuple 			=    feat_extract(audio_inputs[0]			,			return_tensors="""np"""			).input_values
           __snake_case       :				str 			=    feat_extract(np_audio_inputs[0]			,			return_tensors="""np"""			).input_values
           self.assertTrue(np.allclose(UpperCamelCase_			,			UpperCamelCase_			,			atol=1e-3			)			)

           # Test batched
           __snake_case       :				Tuple 			=    feat_extract(UpperCamelCase_			,			padding=UpperCamelCase_			,			return_tensors="""np"""			).input_values
           __snake_case       :				List[Any] 			=    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			)			)

       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				List[Any] 			=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
           __snake_case       :				Optional[Any] 			=    np.random.rand(1_00			).astype(np.floataa			)
           __snake_case       :				Dict 			=    np_audio_inputs.tolist()

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

       def 			snake_case__    (							self					:     List[Any]			,			_lowerCAmelCase					:     int			):
           from datasets import load_dataset

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

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

       def 			snake_case__    (							self					:     Optional[int]			):
           # fmt: off
           __snake_case       :				Union[str, 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       :				Any 			=    self._load_datasamples(1			)
           __snake_case       :				List[str] 			=    EncodecFeatureExtractor()
           __snake_case       :				Any 			=    feature_extractor(UpperCamelCase_			,			return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape			,			(1, 1, 9_36_80)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]			,			UpperCamelCase_			,			atol=1e-6			)			)

       def 			snake_case__    (							self					:     List[str]			):
           # fmt: off
           __snake_case       :				Optional[int] 			=    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       :				str 			=    self._load_datasamples(1			)
           __snake_case       :				Any 			=    [np.tile(input_audio[0][None]			,			reps=(2, 1)			)]
           input_audio[0][1] *= 0.5
           __snake_case       :				Any 			=    EncodecFeatureExtractor(feature_size=2			)
           __snake_case       :				int 			=    feature_extractor(UpperCamelCase_			,			return_tensors="""pt"""			).input_values
           self.assertEquals(input_values.shape			,			(1, 2, 9_36_80)			)
           self.assertTrue(torch.allclose(input_values[0, 0, :30]			,			UpperCamelCase_			,			atol=1e-6			)			)
           self.assertTrue(torch.allclose(input_values[0, 1, :30]			,			EXPECTED_INPUT_VALUES * 0.5			,			atol=1e-6			)			)





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

           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               UpperCamelCase_			,			"""^Both padding and truncation were set. Make sure you only set one.$"""			,			):
               __snake_case       :				Optional[Any] 			=    feature_extractor(
                   UpperCamelCase_			,			padding="""max_length"""			,			truncation=UpperCamelCase_			,			return_tensors="""pt"""			).input_values

           # truncate to chunk
           __snake_case       :				Any 			=    feature_extractor(UpperCamelCase_			,			truncation=UpperCamelCase_			,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape			,			(2, 1, 7_15_20)			)  # 2 chunks

           # force truncate to max_length
           __snake_case       :				Union[str, Any] 			=    feature_extractor(
               UpperCamelCase_			,			truncation=UpperCamelCase_			,			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       :				Dict 			=    feature_extractor(UpperCamelCase_			,			padding=UpperCamelCase_			,			return_tensors="""pt"""			).input_values
           self.assertEquals(padded_outputs.shape			,			(2, 1, 9_52_80)			)

           # pad to chunk
           __snake_case       :				str 			=    feature_extractor(UpperCamelCase_			,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape			,			(2, 1, 9_52_80)			)

           # force pad to max length
           __snake_case       :				str 			=    feature_extractor(
               UpperCamelCase_			,			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(
               UpperCamelCase_			,			"""^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$"""			,			):
               __snake_case       :				List[Any] 			=    feature_extractor(UpperCamelCase_			,			padding=UpperCamelCase_			,			return_tensors="""pt"""			).input_values

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

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

           __snake_case       :				Dict 			=    feature_extractor(input_audio[0]			,			padding=UpperCamelCase_			,			return_tensors="""pt"""			).input_values
           self.assertEquals(truncated_outputs.shape			,			(1, 1, 9_36_80)			)

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

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





'''simple docstring'''






from __future__ import annotations







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		if len(_SCREAMING_SNAKE_CASE  ) == 0:
				raise ValueError('''find_min() arg is an empty sequence'''  )
		if (
		    left >= len(_SCREAMING_SNAKE_CASE  )
		    or left < -len(_SCREAMING_SNAKE_CASE  )
		    or right >= len(_SCREAMING_SNAKE_CASE  )
		    or right < -len(_SCREAMING_SNAKE_CASE  )
		):
				raise IndexError('''list index out of range'''  )
		if left == right:
				return nums[left]
		lowerCAmelCase_   :       Dict    							=(left + right) >> 1  # the middle
		lowerCAmelCase_   :       Optional[int]    							=find_min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[left, mid]
		lowerCAmelCase_   :       Any    							=find_min(_SCREAMING_SNAKE_CASE  ,					mid + 1  ,					_SCREAMING_SNAKE_CASE  )  # find min in range[mid + 1, right]

		return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)






