'''simple docstring'''


import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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						:							Tuple    							=						random.Random()





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: Dict=1.0     ,       UpperCamelCase__					: List[str]=None     ,       UpperCamelCase__					: Optional[Any]=None							):


	"""simple docstring"""
	if rng is None:
		__UpperCAmelCase      					=      global_rng

	__UpperCAmelCase      					=      []
	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     A ( unittest.TestCase ):





							def __init__(   self					:				Union[str, Any]  ,		__a					:				Optional[int]  ,		__a					:				List[str]=7  ,		__a					:				List[Any]=4_0_0  ,		__a					:				str=2_0_0_0  ,		__a					:				Dict=1  ,		__a					:				Optional[Any]=0.0  ,		__a					:				Optional[int]=1_6_0_0_0  ,		__a					:				Optional[int]=True  ,		__a					:				Optional[int]=8_0  ,		__a					:				str=1_6  ,		__a					:				str=6_4  ,		__a					:				str="hann_window"  ,		__a					:				Dict=8_0  ,		__a					:				str=7_6_0_0  ,		__a					:				Dict=1e-10  ,		__a					:				Dict=True  ,		)				->						List[Any]:
								__UpperCAmelCase      					=      parent
								__UpperCAmelCase      					=      batch_size
								__UpperCAmelCase      					=      min_seq_length
								__UpperCAmelCase      					=      max_seq_length
								__UpperCAmelCase      					=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								__UpperCAmelCase      					=      feature_size
								__UpperCAmelCase      					=      padding_value
								__UpperCAmelCase      					=      sampling_rate
								__UpperCAmelCase      					=      do_normalize
								__UpperCAmelCase      					=      num_mel_bins
								__UpperCAmelCase      					=      hop_length
								__UpperCAmelCase      					=      win_length
								__UpperCAmelCase      					=      win_function
								__UpperCAmelCase      					=      fmin
								__UpperCAmelCase      					=      fmax
								__UpperCAmelCase      					=      mel_floor
								__UpperCAmelCase      					=      return_attention_mask





							def    snake_case__    (   self					:				List[str]						)				->						Optional[Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "do_normalize": self.do_normalize,
								    "num_mel_bins": self.num_mel_bins,
								    "hop_length": self.hop_length,
								    "win_length": self.win_length,
								    "win_function": self.win_function,
								    "fmin": self.fmin,
								    "fmax": self.fmax,
								    "mel_floor": self.mel_floor,
								    "return_attention_mask": self.return_attention_mask,
								}





							def    snake_case__    (   self					:				Any  ,		__a					:				Optional[Any]=False  ,		__a					:				Dict=False						)				->						str:
								def _flatten(__a					:				str						):
									return list(itertools.chain(*lowerCamelCase_						)						)

								if equal_length:
									__UpperCAmelCase      					=      floats_list((self.batch_size, self.max_seq_length)						)
								else:
									# make sure that inputs increase in size
									__UpperCAmelCase      					=      [
									    _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      					=      [np.asarray(lowerCamelCase_						) for x in speech_inputs]

								return speech_inputs






							def    snake_case__    (   self					:				Dict  ,		__a					:				Dict=False  ,		__a					:				Dict=False						)				->						Union[str, Any]:
								if equal_length:
									__UpperCAmelCase      					=      [floats_list((self.max_seq_length, self.num_mel_bins)						) for _ in range(self.batch_size						)]
								else:
									# make sure that inputs increase in size
									__UpperCAmelCase      					=      [
									    floats_list((x, self.num_mel_bins)						)
									    for x in range(self.min_seq_length  ,		self.max_seq_length  ,		self.seq_length_diff						)
									]

								if numpify:
									__UpperCAmelCase      					=      [np.asarray(lowerCamelCase_						) for x in speech_inputs]

								return speech_inputs






@require_torch
class     A ( a__							,						unittest.TestCase ):
							a_										=   SpeechTaFeatureExtractor





							def    snake_case__    (   self					:				Optional[Any]						)				->						List[str]:
								__UpperCAmelCase      					=      SpeechTaFeatureExtractionTester(self						)





							def    snake_case__    (   self					:				Dict  ,		__a					:				int						)				->						Tuple:
								self.assertTrue(np.all(np.mean(lowerCamelCase_  ,		axis=0						) < 1e-3						)						)
								self.assertTrue(np.all(np.abs(np.var(lowerCamelCase_  ,		axis=0						) - 1						) < 1e-3						)						)





							def    snake_case__    (   self					:				Tuple						)				->						Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								# create three inputs of length 800, 1000, and 1200
								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in range(8_0_0  ,		1_4_0_0  ,		2_0_0						)]
								__UpperCAmelCase      					=      [np.asarray(lowerCamelCase_						) for speech_input in speech_inputs]

								# Test not batched input
								__UpperCAmelCase      					=      feat_extract(speech_inputs[0]  ,		return_tensors='''np'''						).input_values
								__UpperCAmelCase      					=      feat_extract(np_speech_inputs[0]  ,		return_tensors='''np'''						).input_values
								self.assertTrue(np.allclose(lowerCamelCase_  ,		lowerCamelCase_  ,		atol=1e-3						)						)

								# Test batched
								__UpperCAmelCase      					=      feat_extract(lowerCamelCase_  ,		return_tensors='''np'''						).input_values
								__UpperCAmelCase      					=      feat_extract(lowerCamelCase_  ,		return_tensors='''np'''						).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCamelCase_  ,		lowerCamelCase_						):
									self.assertTrue(np.allclose(lowerCamelCase_  ,		lowerCamelCase_  ,		atol=1e-3						)						)





							def    snake_case__    (   self					:				Optional[Any]						)				->						int:
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in range(8_0_0  ,		1_4_0_0  ,		2_0_0						)]

								__UpperCAmelCase      					=      ['''longest''', '''max_length''', '''do_not_pad''']
								__UpperCAmelCase      					=      [None, 1_6_0_0, None]
								for max_length, padding in zip(lowerCamelCase_  ,		lowerCamelCase_						):
									__UpperCAmelCase      					=      feat_extract(lowerCamelCase_  ,		padding=lowerCamelCase_  ,		max_length=lowerCamelCase_  ,		return_tensors='''np'''						)
									__UpperCAmelCase      					=      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_0_0]						)
									self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6						)
									self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]						)
									self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6						)
									self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]						)





							def    snake_case__    (   self					:				Tuple						)				->						int:
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCAmelCase      					=      range(8_0_0  ,		1_4_0_0  ,		2_0_0						)
								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in lengths]

								__UpperCAmelCase      					=      ['''longest''', '''max_length''', '''do_not_pad''']
								__UpperCAmelCase      					=      [None, 1_6_0_0, None]

								for max_length, padding in zip(lowerCamelCase_  ,		lowerCamelCase_						):
									__UpperCAmelCase      					=      feat_extract(lowerCamelCase_  ,		max_length=lowerCamelCase_  ,		padding=lowerCamelCase_						)
									__UpperCAmelCase      					=      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_0_0]						)
									self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]						)
									self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]						)





							def    snake_case__    (   self					:				Optional[int]						)				->						Tuple:
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in range(8_0_0  ,		1_4_0_0  ,		2_0_0						)]
								__UpperCAmelCase      					=      feat_extract(
								    lowerCamelCase_  ,		truncation=lowerCamelCase_  ,		max_length=1_0_0_0  ,		padding='''max_length'''  ,		return_tensors='''np'''						)
								__UpperCAmelCase      					=      processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
								self._check_zero_mean_unit_variance(input_values[1]						)
								self._check_zero_mean_unit_variance(input_values[2]						)





							def    snake_case__    (   self					:				Optional[Any]						)				->						str:
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in range(8_0_0  ,		1_4_0_0  ,		2_0_0						)]
								__UpperCAmelCase      					=      feat_extract(
								    lowerCamelCase_  ,		truncation=lowerCamelCase_  ,		max_length=1_0_0_0  ,		padding='''longest'''  ,		return_tensors='''np'''						)
								__UpperCAmelCase      					=      processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
								self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]						)
								self._check_zero_mean_unit_variance(input_values[2]						)

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1_0_0_0)						)

								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in range(8_0_0  ,		1_4_0_0  ,		2_0_0						)]
								__UpperCAmelCase      					=      feat_extract(
								    lowerCamelCase_  ,		truncation=lowerCamelCase_  ,		max_length=2_0_0_0  ,		padding='''longest'''  ,		return_tensors='''np'''						)
								__UpperCAmelCase      					=      processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_0_0]						)
								self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]						)
								self._check_zero_mean_unit_variance(input_values[2]						)

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1_2_0_0)						)





							def    snake_case__    (   self					:				Optional[int]						)				->						int:
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								__UpperCAmelCase      					=      np.random.rand(1_0_0						).astype(np.floataa						)
								__UpperCAmelCase      					=      np_speech_inputs.tolist()

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





							def    snake_case__    (   self					:				str						)				->						int:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
								# create three inputs of length 800, 1000, and 1200
								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in range(8_0_0  ,		1_4_0_0  ,		2_0_0						)]
								__UpperCAmelCase      					=      [np.asarray(lowerCamelCase_						) for speech_input in speech_inputs]

								# Test feature size
								__UpperCAmelCase      					=      feature_extractor(audio_target=lowerCamelCase_  ,		padding=lowerCamelCase_  ,		return_tensors='''np'''						).input_values
								self.assertTrue(input_values.ndim == 3						)
								self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins						)

								# Test not batched input
								__UpperCAmelCase      					=      feature_extractor(speech_inputs[0]  ,		return_tensors='''np'''						).input_values
								__UpperCAmelCase      					=      feature_extractor(np_speech_inputs[0]  ,		return_tensors='''np'''						).input_values
								self.assertTrue(np.allclose(lowerCamelCase_  ,		lowerCamelCase_  ,		atol=1e-3						)						)

								# Test batched
								__UpperCAmelCase      					=      feature_extractor(lowerCamelCase_  ,		return_tensors='''np'''						).input_values
								__UpperCAmelCase      					=      feature_extractor(lowerCamelCase_  ,		return_tensors='''np'''						).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCamelCase_  ,		lowerCamelCase_						):
									self.assertTrue(np.allclose(lowerCamelCase_  ,		lowerCamelCase_  ,		atol=1e-3						)						)

								# Test 2-D numpy arrays are batched.
								__UpperCAmelCase      					=      [floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								__UpperCAmelCase      					=      np.asarray(lowerCamelCase_						)
								__UpperCAmelCase      					=      feature_extractor(lowerCamelCase_  ,		return_tensors='''np'''						).input_values
								__UpperCAmelCase      					=      feature_extractor(lowerCamelCase_  ,		return_tensors='''np'''						).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCamelCase_  ,		lowerCamelCase_						):
									self.assertTrue(np.allclose(lowerCamelCase_  ,		lowerCamelCase_  ,		atol=1e-3						)						)





							def    snake_case__    (   self					:				Union[str, Any]						)				->						Dict:
								__UpperCAmelCase      					=      self.feat_extract_tester.prepare_inputs_for_target()
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_dict						)
								__UpperCAmelCase      					=      feat_extract.model_input_names[0]

								__UpperCAmelCase      					=      BatchFeature({input_name: speech_inputs}						)

								self.assertTrue(all(len(lowerCamelCase_						) == len(lowerCamelCase_						) for x, y in zip(lowerCamelCase_  ,		processed_features[input_name]						)						)						)

								__UpperCAmelCase      					=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase_						)
								__UpperCAmelCase      					=      BatchFeature({input_name: speech_inputs}  ,		tensor_type='''np'''						)

								__UpperCAmelCase      					=      processed_features[input_name]

								if len(batch_features_input.shape						) < 3:
									__UpperCAmelCase      					=      batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.num_mel_bins)						)





							@require_torch
							def    snake_case__    (   self					:				str						)				->						List[Any]:
								__UpperCAmelCase      					=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase_						)
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_dict						)
								__UpperCAmelCase      					=      feat_extract.model_input_names[0]

								__UpperCAmelCase      					=      BatchFeature({input_name: speech_inputs}  ,		tensor_type='''pt'''						)

								__UpperCAmelCase      					=      processed_features[input_name]

								if len(batch_features_input.shape						) < 3:
									__UpperCAmelCase      					=      batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]						), self.feat_extract_tester.num_mel_bins)						)





							@require_torch
							def    snake_case__    (   self					:				List[str]						)				->						Dict:
								__UpperCAmelCase      					=      self.feature_extraction_class(**self.feat_extract_dict						)
								__UpperCAmelCase      					=      self.feat_extract_tester.prepare_inputs_for_target()
								__UpperCAmelCase      					=      feat_extract.model_input_names[0]

								__UpperCAmelCase      					=      BatchFeature({input_name: speech_inputs}						)

								__UpperCAmelCase      					=      feat_extract.num_mel_bins  # hack!

								__UpperCAmelCase      					=      feat_extract.pad(lowerCamelCase_  ,		padding='''longest'''  ,		return_tensors='''np'''						)[input_name]
								__UpperCAmelCase      					=      feat_extract.pad(lowerCamelCase_  ,		padding='''longest'''  ,		return_tensors='''pt'''						)[input_name]

								self.assertTrue(abs(input_np.astype(np.floataa						).sum() - input_pt.numpy().astype(np.floataa						).sum()						) < 1e-2						)





							def    snake_case__    (   self					:				Any						)				->						List[Any]:
								__UpperCAmelCase      					=      self.feat_extract_dict
								__UpperCAmelCase      					=      True
								__UpperCAmelCase      					=      self.feature_extraction_class(**lowerCamelCase_						)
								__UpperCAmelCase      					=      self.feat_extract_tester.prepare_inputs_for_target()
								__UpperCAmelCase      					=      [len(lowerCamelCase_						) for x in speech_inputs]
								__UpperCAmelCase      					=      feat_extract.model_input_names[0]

								__UpperCAmelCase      					=      BatchFeature({input_name: speech_inputs}						)

								__UpperCAmelCase      					=      feat_extract.num_mel_bins  # hack!

								__UpperCAmelCase      					=      feat_extract.pad(lowerCamelCase_  ,		padding='''longest'''  ,		return_tensors='''np'''						)
								self.assertIn('''attention_mask'''  ,		lowerCamelCase_						)
								self.assertListEqual(list(processed.attention_mask.shape						)  ,		list(processed[input_name].shape[:2]						)						)
								self.assertListEqual(processed.attention_mask.sum(-1						).tolist()  ,		lowerCamelCase_						)





							def    snake_case__    (   self					:				Optional[Any]						)				->						str:
								__UpperCAmelCase      					=      self.feat_extract_dict
								__UpperCAmelCase      					=      True
								__UpperCAmelCase      					=      self.feature_extraction_class(**lowerCamelCase_						)
								__UpperCAmelCase      					=      self.feat_extract_tester.prepare_inputs_for_target()
								__UpperCAmelCase      					=      [len(lowerCamelCase_						) for x in speech_inputs]
								__UpperCAmelCase      					=      feat_extract.model_input_names[0]

								__UpperCAmelCase      					=      BatchFeature({input_name: speech_inputs}						)
								__UpperCAmelCase      					=      min(lowerCamelCase_						)

								__UpperCAmelCase      					=      feat_extract.num_mel_bins  # hack!

								__UpperCAmelCase      					=      feat_extract.pad(
								    lowerCamelCase_  ,		padding='''max_length'''  ,		max_length=lowerCamelCase_  ,		truncation=lowerCamelCase_  ,		return_tensors='''np'''						)
								self.assertIn('''attention_mask'''  ,		lowerCamelCase_						)
								self.assertListEqual(
								    list(processed_pad.attention_mask.shape						)  ,		[processed_pad[input_name].shape[0], max_length]						)
								self.assertListEqual(
								    processed_pad.attention_mask[:, :max_length].sum(-1						).tolist()  ,		[max_length for x in speech_inputs]						)





							def    snake_case__    (   self					:				int  ,		__a					:				Optional[int]						)				->						Dict:
								from datasets import load_dataset

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

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





							def    snake_case__    (   self					:				Union[str, Any]						)				->						Tuple:
								# fmt: off
								__UpperCAmelCase      					=      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      					=      self._load_datasamples(1						)
								__UpperCAmelCase      					=      SpeechTaFeatureExtractor()
								__UpperCAmelCase      					=      feature_extractor(lowerCamelCase_  ,		return_tensors='''pt'''						).input_values
								self.assertEquals(input_values.shape  ,		(1, 9_3_6_8_0)						)
								self.assertTrue(torch.allclose(input_values[0, :3_0]  ,		lowerCamelCase_  ,		atol=1e-6						)						)






							def    snake_case__    (   self					:				Union[str, Any]						)				->						Dict:
								# fmt: off
								__UpperCAmelCase      					=      torch.tensor(
								    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
								     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
								     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
								     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]						)
								# fmt: on

								__UpperCAmelCase      					=      self._load_datasamples(1						)
								__UpperCAmelCase      					=      SpeechTaFeatureExtractor()
								__UpperCAmelCase      					=      feature_extractor(audio_target=lowerCamelCase_  ,		return_tensors='''pt'''						).input_values
								self.assertEquals(input_values.shape  ,		(1, 3_6_6, 8_0)						)
								self.assertTrue(torch.allclose(input_values[0, 0, :3_0]  ,		lowerCamelCase_  ,		atol=1e-4						)						)




'''simple docstring'''






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__UpperCAmelCase       							=				TypeVar('''KEY''')
__UpperCAmelCase       							=				TypeVar('''VAL''')




@dataclass(frozen=a__							,							slots=a__    )
class 						a__							(				Generic[KEY, VAL]    ):





							'''simple docstring'''






							lowercase__     :  KEY
							lowercase__     :  VAL




class 						a__							(				_Item    ):





							'''simple docstring'''






							def __init__(						self  )			->				None:
														super().__init__(lowerCamelCase_   ,       lowerCamelCase_  )







							def __bool__(						self  )			->				bool:
														return False

__UpperCAmelCase       							=				_DeletedItem()




class 						a__							(				MutableMapping[KEY, VAL]    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_ = 8   ,       lowerCamelCase_ = 0.75  )			->				None:
														lowerCAmelCase__			  =      initial_block_size
														lowerCAmelCase__			  =      [None] * initial_block_size
														assert 0.0 < capacity_factor < 1.0
														lowerCAmelCase__			  =      capacity_factor
														lowerCAmelCase__			  =      0







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return hash(lowerCamelCase_  ) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return (ind + 1) % len(self._buckets  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				bool:
														lowerCAmelCase__			  =      self._buckets[ind]
														if not stored:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					self._len += 1
																					return True
														elif stored.key == key:
																					lowerCAmelCase__			  =      _Item(lowerCamelCase_   ,       lowerCamelCase_  )
																					return True
														else:
																					return False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor
														return len(self  ) >= int(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				bool:
														if len(self._buckets  ) <= self._initial_block_size:
																					return False
														lowerCAmelCase__			  =      len(self._buckets  ) * self._capacity_factor / 2
														return len(self  ) < limit







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				None:
														lowerCAmelCase__			  =      self._buckets
														lowerCAmelCase__			  =      [None] * new_size
														lowerCAmelCase__			  =      0
														for item in old_buckets:
																					if item:
																												self._add_item(item.key   ,       item.val  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) * 2  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				None:
														self._resize(len(self._buckets  ) // 2  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Iterator[int]:
														lowerCAmelCase__			  =      self._get_bucket_index(lowerCamelCase_  )
														for _ in range(len(self._buckets  )  ):
																					yield ind
																					lowerCAmelCase__			  =      self._get_next_ind(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					if self._try_set(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																												break







							def __setitem__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				None:
														if self._is_full():
																					self._size_up()

														self._add_item(lowerCamelCase_   ,       lowerCamelCase_  )







							def __delitem__(						self   ,       lowerCamelCase_  )			->				None:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												raise KeyError(lowerCamelCase_  )
																					if item is _deleted:
																												continue
																					if item.key == key:
																												lowerCAmelCase__			  =      _deleted
																												self._len -= 1
																												break
														if self._is_sparse():
																					self._size_down()







							def __getitem__(						self   ,       lowerCamelCase_  )			->				VAL:
														for ind in self._iterate_buckets(lowerCamelCase_  ):
																					lowerCAmelCase__			  =      self._buckets[ind]
																					if item is None:
																												break
																					if item is _deleted:
																												continue
																					if item.key == key:
																												return item.val
														raise KeyError(lowerCamelCase_  )







							def __len__(						self  )			->				int:
														return self._len







							def __iter__(						self  )			->				Iterator[KEY]:
														yield from (item.key for item in self._buckets if item)







							def __repr__(						self  )			->				str:
														lowerCAmelCase__			  =      ''' ,'''.join(
														    F"""{item.key}: {item.val}""" for item in self._buckets if item  )
														return F"""HashMap({val_string})"""