'''simple docstring'''


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


def 					_lowerCamelCase    (      lowerCamelCase_					:							Any	,     lowerCamelCase_					:							Union[str, Any]=1.0	,     lowerCamelCase_					:							Any=None	,     lowerCamelCase_					:							Any=None   ):



     """simple docstring"""





     if rng is None:
          UpperCAmelCase_							:   List[Any]   =      global_rng

     UpperCAmelCase_							:   List[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      __SCREAMING_SNAKE_CASE						(       unittest.TestCase       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		snake_case_=7      ,		snake_case_=4_0_0      ,		snake_case_=2_0_0_0      ,		snake_case_=1      ,		snake_case_=0.0      ,		snake_case_=2_4_0_0_0      ,		snake_case_=True      ,		):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self						):




       '''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						):




       '''simple docstring'''

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

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

       return audio_inputs







@require_torch
class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_						,	unittest.TestCase       ):







  '''simple docstring'''






  lowerCamelCase_		:str						    =		EncodecFeatureExtractor


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      EnCodecFeatureExtractionTester(self						)


  def _UpperCamelCase     ( self						):




       '''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_							:   Dict   =      [floats_list((1, x)						)[0] for x in range(8_0_0      ,		1_4_0_0      ,		2_0_0						)]
       UpperCAmelCase_							:   Optional[Any]   =      [np.asarray(snake_case_						) for audio_input in audio_inputs]

       # Test not batched input
       UpperCAmelCase_							:   str   =      feat_extract(audio_inputs[0]      ,		return_tensors='np'						).input_values
       UpperCAmelCase_							:   Dict   =      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_							:   List[Any]   =      feat_extract(snake_case_      ,		padding=snake_case_      ,		return_tensors='np'						).input_values
       UpperCAmelCase_							:   List[str]   =      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						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
       UpperCAmelCase_							:   List[str]   =      np.random.rand(1_0_0						).astype(np.floataa						)
       UpperCAmelCase_							:   List[str]   =      np_audio_inputs.tolist()

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


  def _UpperCamelCase     ( self      ,		snake_case_						):




       '''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_							:   List[str]   =      ds.sort('id'						).select(range(snake_case_						)						)[:num_samples]['audio']

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


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      torch.tensor(
           [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
            3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
            2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
            4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
            7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
            4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]						)
       # fmt: on
       UpperCAmelCase_							:   List[Any]   =      self._load_datasamples(1						)
       UpperCAmelCase_							:   int   =      EncodecFeatureExtractor()
       UpperCAmelCase_							:   List[Any]   =      feature_extractor(snake_case_      ,		return_tensors='pt'						).input_values
       self.assertEquals(input_values.shape      ,		(1, 1, 9_3_6_8_0)						)
       self.assertTrue(torch.allclose(input_values[0, 0, :3_0]      ,		snake_case_      ,		atol=1E-6						)						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      torch.tensor(
           [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
            3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
            2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
            4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
            7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
            4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]						)
       # fmt: on
       UpperCAmelCase_							:   Any   =      self._load_datasamples(1						)
       UpperCAmelCase_							:   Optional[int]   =      [np.tile(input_audio[0][None]      ,		reps=(2, 1)						)]
       input_audio[0][1] *= 0.5
       UpperCAmelCase_							:   Optional[Any]   =      EncodecFeatureExtractor(feature_size=2						)
       UpperCAmelCase_							:   int   =      feature_extractor(snake_case_      ,		return_tensors='pt'						).input_values
       self.assertEquals(input_values.shape      ,		(1, 2, 9_3_6_8_0)						)
       self.assertTrue(torch.allclose(input_values[0, 0, :3_0]      ,		snake_case_      ,		atol=1E-6						)						)
       self.assertTrue(torch.allclose(input_values[0, 1, :3_0]      ,		EXPECTED_INPUT_VALUES * 0.5      ,		atol=1E-6						)						)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      self._load_datasamples(2						)
       # would be easier if the stride was like
       UpperCAmelCase_							:   Optional[int]   =      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_							:   Union[str, Any]   =      feature_extractor(
                snake_case_      ,		padding='max_length'      ,		truncation=snake_case_      ,		return_tensors='pt'						).input_values

       # truncate to chunk
       UpperCAmelCase_							:   str   =      feature_extractor(snake_case_      ,		truncation=snake_case_      ,		return_tensors='pt'						).input_values
       self.assertEquals(truncated_outputs.shape      ,		(2, 1, 7_1_5_2_0)						)  # 2 chunks

       # force truncate to max_length
       UpperCAmelCase_							:   List[Any]   =      feature_extractor(
           snake_case_      ,		truncation=snake_case_      ,		max_length=4_8_0_0_0      ,		return_tensors='pt'						).input_values
       self.assertEquals(truncated_outputs.shape      ,		(2, 1, 4_8_0_0_0)						)

       # pad to chunk
       UpperCAmelCase_							:   Any   =      feature_extractor(snake_case_      ,		padding=snake_case_      ,		return_tensors='pt'						).input_values
       self.assertEquals(padded_outputs.shape      ,		(2, 1, 9_5_2_8_0)						)

       # pad to chunk
       UpperCAmelCase_							:   Optional[int]   =      feature_extractor(snake_case_      ,		return_tensors='pt'						).input_values
       self.assertEquals(truncated_outputs.shape      ,		(2, 1, 9_5_2_8_0)						)

       # force pad to max length
       UpperCAmelCase_							:   Dict   =      feature_extractor(
           snake_case_      ,		padding='max_length'      ,		max_length=1_0_0_0_0_0      ,		return_tensors='pt'						).input_values
       self.assertEquals(truncated_outputs.shape      ,		(2, 1, 1_0_0_0_0_0)						)

       # 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_							:   str   =      feature_extractor(snake_case_      ,		padding=snake_case_      ,		return_tensors='pt'						).input_values

       UpperCAmelCase_							:   Tuple   =      feature_extractor(input_audio[0]      ,		padding=snake_case_      ,		return_tensors='pt'						).input_values
       self.assertEquals(truncated_outputs.shape      ,		(1, 1, 9_3_6_8_0)						)

       # no pad if no chunk_length_s
       UpperCAmelCase_							:   Optional[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_							:   Union[str, 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_3_6_8_0)						)

       # no pad if no overlap
       UpperCAmelCase_							:   int   =      2
       UpperCAmelCase_							:   List[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_							:   Optional[Any]   =      feature_extractor(snake_case_      ,		padding=snake_case_      ,		return_tensors='pt'						).input_values

       UpperCAmelCase_							:   Optional[Any]   =      feature_extractor(input_audio[0]      ,		padding=snake_case_      ,		return_tensors='pt'						).input_values
       self.assertEquals(truncated_outputs.shape      ,		(1, 1, 9_3_6_8_0)						)







'''simple docstring'''


from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize






class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						    =		1
  lowerCamelCase_		:List[Any]						    =		2
  lowerCamelCase_		:Optional[Any]						    =		3


  def __repr__( self						):




       '''simple docstring'''

       return F'''{self.__class__.__name__}.{self.name}'''







def 					_lowerCamelCase    (      lowerCamelCase_					:							tuple[int, int]	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   Any   =      point
     return degrees(atana(y - miny	,     x - minx   )   )


def 					_lowerCamelCase    (      lowerCamelCase_					:							tuple[int, int]	,     lowerCamelCase_					:							tuple[int, int]	,     lowerCamelCase_					:							tuple[int, int]   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   str   =      starting
     UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[int]   =      via
     UpperCAmelCase_   ,			UpperCAmelCase_							:   Dict   =      target
     UpperCAmelCase_							:   Optional[Any]   =      degrees(atana(ya - ya	,     xa - xa   )   )
     via_angle %= 360
     UpperCAmelCase_							:   Optional[Any]   =      degrees(atana(ya - ya	,     xa - xa   )   )
     target_angle %= 360
     # t-
     #  \ \
     #   \ v
     #    \|
     #     s
     # via_angle is always lower than target_angle, if direction is left.
     # If they are same, it means they are on a same line of convex hull.
     if target_angle > via_angle:
          return Direction.left
     elif target_angle == via_angle:
          return Direction.straight
     else:
          return Direction.right





def 					_lowerCamelCase    (      lowerCamelCase_					:							list[tuple[int, int]]   ):



     """simple docstring"""





     if len(lowerCamelCase_   ) <= 2:
          # There is no convex hull
          raise ValueError('graham_scan: argument must contain more than 3 points.'   )
     if len(lowerCamelCase_   ) == 3:
          return points
     # find the lowest and the most left point
     UpperCAmelCase_							:   Dict   =      0
     UpperCAmelCase_   ,			UpperCAmelCase_							:   Union[str, Any]   =      maxsize, maxsize
     for i, point in enumerate(lowerCamelCase_   ):
          UpperCAmelCase_							:   Union[str, Any]   =      point[0]
          UpperCAmelCase_							:   List[Any]   =      point[1]
          if y < miny:
               UpperCAmelCase_							:   Dict   =      y
               UpperCAmelCase_							:   Optional[int]   =      x
               UpperCAmelCase_							:   Optional[Any]   =      i
          if y == miny and x < minx:
               UpperCAmelCase_							:   Union[str, Any]   =      x
               UpperCAmelCase_							:   Dict   =      i

    # remove the lowest and the most left point from points for preparing for sort
     points.pop(lowerCamelCase_   )

     UpperCAmelCase_							:   List[str]   =      sorted(lowerCamelCase_	,     key=lambda lowerCamelCase_   : angle_comparer(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )   )
     # This insert actually costs complexity,
     # and you should instead add (minx, miny) into stack later.
     # I'm using insert just for easy understanding.
     sorted_points.insert(0	,     (minx, miny)   )

     UpperCAmelCase_							:   deque[tuple[int, int]]   =      deque()
     stack.append(sorted_points[0]   )
     stack.append(sorted_points[1]   )
     stack.append(sorted_points[2]   )
     # In any ways, the first 3 points line are towards left.
     # Because we sort them the angle from minx, miny.
     UpperCAmelCase_							:   List[Any]   =      Direction.left

     for i in range(3	,     len(lowerCamelCase_   )   ):
          while True:
               UpperCAmelCase_							:   Optional[Any]   =      stack[-2]
               UpperCAmelCase_							:   Optional[int]   =      stack[-1]
               UpperCAmelCase_							:   str   =      sorted_points[i]
               UpperCAmelCase_							:   Union[str, Any]   =      check_direction(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

               if next_direction == Direction.left:
                    UpperCAmelCase_							:   Union[str, Any]   =      Direction.left
                    break
               if next_direction == Direction.straight:
                    if current_direction == Direction.left:
                         # We keep current_direction as left.
                         # Because if the straight line keeps as straight,
                         # we want to know if this straight line is towards left.
                         break
                    elif current_direction == Direction.right:
                         # If the straight line is towards right,
                         # every previous points on those straigh line is not convex hull.
                         stack.pop()
               if next_direction == Direction.right:
                    stack.pop()
          stack.append(sorted_points[i]   )
     return list(lowerCamelCase_   )






