


"""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


UpperCAmelCase__					=random.Random()


def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			List[str]=1.0					, UpperCamelCase__						:			List[str]=None					, UpperCamelCase__						:			Dict=None			):






 """simple docstring"""







 if rng is None:
  __lowercase						=						global_rng

 __lowercase						=						[]
 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  lowerCamelCase__    (		unittest.TestCase     ):
      def __init__(		self						:						Any   ,							A_						:						str   ,							A_						:						str=7   ,							A_						:						Union[str, Any]=4_0_0   ,							A_						:						Union[str, Any]=2_0_0_0   ,							A_						:						Dict=1   ,							A_						:						List[str]=0.0   ,							A_						:						Any=1_6_0_0_0   ,							A_						:						List[Any]=True   ,							A_						:						Dict=8_0   ,							A_						:						Dict=1_6   ,							A_						:						Optional[int]=6_4   ,							A_						:						Dict="hann_window"   ,							A_						:						Dict=8_0   ,							A_						:						Any=7_6_0_0   ,							A_						:						Union[str, Any]=1e-1_0   ,							A_						:						List[str]=True   ,							):






       '''simple docstring'''


       __lowercase						=						parent
       __lowercase						=						batch_size
       __lowercase						=						min_seq_length
       __lowercase						=						max_seq_length
       __lowercase						=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       __lowercase						=						feature_size
       __lowercase						=						padding_value
       __lowercase						=						sampling_rate
       __lowercase						=						do_normalize
       __lowercase						=						num_mel_bins
       __lowercase						=						hop_length
       __lowercase						=						win_length
       __lowercase						=						win_function
       __lowercase						=						fmin
       __lowercase						=						fmax
       __lowercase						=						mel_floor
       __lowercase						=						return_attention_mask
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Optional[int]=False   ,							A_						:						List[Any]=False				):






       '''simple docstring'''


       def _flatten(A_						:						List[str]				):
        return list(itertools.chain(*A_				)				)

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

       return speech_inputs






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]   ,							A_						:						Optional[Any]=False   ,							A_						:						str=False				):






       '''simple docstring'''


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

       return speech_inputs






@require_torch
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		SpeechTaFeatureExtractor
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						SpeechTaFeatureExtractionTester(self				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						Dict				):






       '''simple docstring'''


       self.assertTrue(np.all(np.mean(A_   ,							axis=0				) < 1e-3				)				)
       self.assertTrue(np.all(np.abs(np.var(A_   ,							axis=0				) - 1				) < 1e-3				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       # create three inputs of length 800, 1000, and 1200
       __lowercase						=						[floats_list((1, x)				)[0] for x in range(8_0_0   ,							1_4_0_0   ,							2_0_0				)]
       __lowercase						=						[np.asarray(A_				) for speech_input in speech_inputs]

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

       # Test batched
       __lowercase						=						feat_extract(A_   ,							return_tensors="""np"""				).input_values
       __lowercase						=						feat_extract(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						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       __lowercase						=						[floats_list((1, x)				)[0] for x in range(8_0_0   ,							1_4_0_0   ,							2_0_0				)]

       __lowercase						=						["""longest""", """max_length""", """do_not_pad"""]
       __lowercase						=						[None, 1_6_0_0, None]
       for max_length, padding in zip(A_   ,							A_				):
        __lowercase						=						feat_extract(A_   ,							padding=A_   ,							max_length=A_   ,							return_tensors="""np"""				)
        __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       __lowercase						=						range(8_0_0   ,							1_4_0_0   ,							2_0_0				)
       __lowercase						=						[floats_list((1, x)				)[0] for x in lengths]

       __lowercase						=						["""longest""", """max_length""", """do_not_pad"""]
       __lowercase						=						[None, 1_6_0_0, None]

       for max_length, padding in zip(A_   ,							A_				):
        __lowercase						=						feat_extract(A_   ,							max_length=A_   ,							padding=A_				)
        __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       __lowercase						=						[floats_list((1, x)				)[0] for x in range(8_0_0   ,							1_4_0_0   ,							2_0_0				)]
       __lowercase						=						feat_extract(
           A_   ,							truncation=A_   ,							max_length=1_0_0_0   ,							padding="""max_length"""   ,							return_tensors="""np"""				)
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       __lowercase						=						[floats_list((1, x)				)[0] for x in range(8_0_0   ,							1_4_0_0   ,							2_0_0				)]
       __lowercase						=						feat_extract(
           A_   ,							truncation=A_   ,							max_length=1_0_0_0   ,							padding="""longest"""   ,							return_tensors="""np"""				)
       __lowercase						=						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)				)

       __lowercase						=						[floats_list((1, x)				)[0] for x in range(8_0_0   ,							1_4_0_0   ,							2_0_0				)]
       __lowercase						=						feat_extract(
           A_   ,							truncation=A_   ,							max_length=2_0_0_0   ,							padding="""longest"""   ,							return_tensors="""np"""				)
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       __lowercase						=						np.random.rand(1_0_0				).astype(np.floataa				)
       __lowercase						=						np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
        __lowercase						=						feature_extractor.pad([{"""input_values""": inputs}]   ,							return_tensors="""np"""				)
        self.assertTrue(np_processed.input_values.dtype == np.floataa				)
        __lowercase						=						feature_extractor.pad([{"""input_values""": inputs}]   ,							return_tensors="""pt"""				)
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
       # create three inputs of length 800, 1000, and 1200
       __lowercase						=						[floats_list((1, x)				)[0] for x in range(8_0_0   ,							1_4_0_0   ,							2_0_0				)]
       __lowercase						=						[np.asarray(A_				) for speech_input in speech_inputs]

       # Test feature size
       __lowercase						=						feature_extractor(audio_target=A_   ,							padding=A_   ,							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
       __lowercase						=						feature_extractor(speech_inputs[0]   ,							return_tensors="""np"""				).input_values
       __lowercase						=						feature_extractor(np_speech_inputs[0]   ,							return_tensors="""np"""				).input_values
       self.assertTrue(np.allclose(A_   ,							A_   ,							atol=1e-3				)				)

       # Test batched
       __lowercase						=						feature_extractor(A_   ,							return_tensors="""np"""				).input_values
       __lowercase						=						feature_extractor(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				)				)

       # Test 2-D numpy arrays are batched.
       __lowercase						=						[floats_list((1, x)				)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
       __lowercase						=						np.asarray(A_				)
       __lowercase						=						feature_extractor(A_   ,							return_tensors="""np"""				).input_values
       __lowercase						=						feature_extractor(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						:						Any				):






       '''simple docstring'''


       __lowercase						=						self.feat_extract_tester.prepare_inputs_for_target()
       __lowercase						=						self.feature_extraction_class(**self.feat_extract_dict				)
       __lowercase						=						feat_extract.model_input_names[0]

       __lowercase						=						BatchFeature({input_name: speech_inputs}				)

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

       __lowercase						=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=A_				)
       __lowercase						=						BatchFeature({input_name: speech_inputs}   ,							tensor_type="""np"""				)

       __lowercase						=						processed_features[input_name]

       if len(batch_features_input.shape				) < 3:
        __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=A_				)
       __lowercase						=						self.feature_extraction_class(**self.feat_extract_dict				)
       __lowercase						=						feat_extract.model_input_names[0]

       __lowercase						=						BatchFeature({input_name: speech_inputs}   ,							tensor_type="""pt"""				)

       __lowercase						=						processed_features[input_name]

       if len(batch_features_input.shape				) < 3:
        __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						self.feature_extraction_class(**self.feat_extract_dict				)
       __lowercase						=						self.feat_extract_tester.prepare_inputs_for_target()
       __lowercase						=						feat_extract.model_input_names[0]

       __lowercase						=						BatchFeature({input_name: speech_inputs}				)

       __lowercase						=						feat_extract.num_mel_bins  # hack!

       __lowercase						=						feat_extract.pad(A_   ,							padding="""longest"""   ,							return_tensors="""np"""				)[input_name]
       __lowercase						=						feat_extract.pad(A_   ,							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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						self.feat_extract_dict
       __lowercase						=						True
       __lowercase						=						self.feature_extraction_class(**A_				)
       __lowercase						=						self.feat_extract_tester.prepare_inputs_for_target()
       __lowercase						=						[len(A_				) for x in speech_inputs]
       __lowercase						=						feat_extract.model_input_names[0]

       __lowercase						=						BatchFeature({input_name: speech_inputs}				)

       __lowercase						=						feat_extract.num_mel_bins  # hack!

       __lowercase						=						feat_extract.pad(A_   ,							padding="""longest"""   ,							return_tensors="""np"""				)
       self.assertIn("""attention_mask"""   ,							A_				)
       self.assertListEqual(list(processed.attention_mask.shape				)   ,							list(processed[input_name].shape[:2]				)				)
       self.assertListEqual(processed.attention_mask.sum(-1				).tolist()   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.feat_extract_dict
       __lowercase						=						True
       __lowercase						=						self.feature_extraction_class(**A_				)
       __lowercase						=						self.feat_extract_tester.prepare_inputs_for_target()
       __lowercase						=						[len(A_				) for x in speech_inputs]
       __lowercase						=						feat_extract.model_input_names[0]

       __lowercase						=						BatchFeature({input_name: speech_inputs}				)
       __lowercase						=						min(A_				)

       __lowercase						=						feat_extract.num_mel_bins  # hack!

       __lowercase						=						feat_extract.pad(
           A_   ,							padding="""max_length"""   ,							max_length=A_   ,							truncation=A_   ,							return_tensors="""np"""				)
       self.assertIn("""attention_mask"""   ,							A_				)
       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 SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						Dict				):






       '''simple docstring'''


       from datasets import load_dataset

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

       return [x["array"] for x in speech_samples]
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						torch.tensor(
           [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
            3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
            2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
            4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
            7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
            4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]				)
       # fmt: on

       __lowercase						=						self._load_datasamples(1				)
       __lowercase						=						SpeechTaFeatureExtractor()
       __lowercase						=						feature_extractor(A_   ,							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]   ,							A_   ,							atol=1e-6				)				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						torch.tensor(
           [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
            -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
            -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
            -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]				)
       # fmt: on

       __lowercase						=						self._load_datasamples(1				)
       __lowercase						=						SpeechTaFeatureExtractor()
       __lowercase						=						feature_extractor(audio_target=A_   ,							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]   ,							A_   ,							atol=1e-4				)				)




"""simple docstring"""

import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch


def lowerCAmelCase_   (					UpperCamelCase__						:			int			):  # picklable for multiprocessing






 """simple docstring"""







 return x.sum()


def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]			):  # picklable for multiprocessing






 """simple docstring"""







 return i + 1





@dataclass
class  lowerCamelCase__    :
      a			:  int
      a			:  str






class  lowerCamelCase__    (		_a     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						{}
       __lowercase						=						[]
       __lowercase						=						1
       __lowercase						=						[1, 2]
       __lowercase						=						{"""a""": 1, """b""": 2}
       __lowercase						=						{"""a""": [1, 2], """b""": [3, 4]}
       __lowercase						=						{"""a""": {"""1""": 1}, """b""": 2}
       __lowercase						=						{"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
       __lowercase						=						{}
       __lowercase						=						[]
       __lowercase						=						2
       __lowercase						=						[2, 3]
       __lowercase						=						{"""a""": 2, """b""": 3}
       __lowercase						=						{"""a""": [2, 3], """b""": [4, 5]}
       __lowercase						=						{"""a""": {"""1""": 2}, """b""": 3}
       __lowercase						=						{"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_				)   ,							A_				)

       __lowercase						=						2
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(map_nested(A_   ,							A_   ,							num_proc=A_				)   ,							A_				)

       __lowercase						=						{"""a""": np.eye(2				), """b""": np.zeros(3				), """c""": np.ones(2				)}
       __lowercase						=						{"""a""": 2, """b""": 0, """c""": 2}
       __lowercase						=						{
           """a""": np.eye(2				).astype(A_				),
           """b""": np.zeros(3				).astype(A_				),
           """c""": np.ones(2				).astype(A_				),
       }
       self.assertEqual(map_nested(A_   ,							A_   ,							map_numpy=A_				)   ,							A_				)
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(A_   ,							A_   ,							map_numpy=A_				).items()}   ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}   ,							)
       self.assertEqual(map_nested(A_   ,							A_   ,							map_numpy=A_   ,							num_proc=A_				)   ,							A_				)
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(A_   ,							A_   ,							map_numpy=A_   ,							num_proc=A_				).items()}   ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}   ,							)
       with self.assertRaises(A_				):  # can't pickle a local lambda
        map_nested(lambda A_				: x + 1   ,							A_   ,							num_proc=A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						{"""a""": 1, """b""": 2}
       __lowercase						=						{"""a""": 3, """b""": 4}
       __lowercase						=						{"""a""": 5, """b""": 6}
       __lowercase						=						sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]				)
       self.assertEqual(sorted(zip_dict(A_   ,							A_   ,							A_				)				)   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       class  lowerCamelCase__    :
             a			:  int           =		"""bar"""






       __lowercase						=						Foo()
       self.assertEqual(foo.my_attr   ,							"""bar"""				)
       with temporary_assignment(A_   ,							"""my_attr"""   ,							"""BAR"""				):
        self.assertEqual(foo.my_attr   ,							"""BAR"""				)
       self.assertEqual(foo.my_attr   ,							"""bar"""				)






@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""					, [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]					, )
def lowerCAmelCase_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 with patch("""datasets.utils.py_utils._single_map_nested"""			) as mock_single_map_nested, patch(
     """datasets.parallel.parallel.Pool"""			) as mock_multiprocessing_pool:
  __lowercase						=						{f'''{i}''': i for i in range(UpperCamelCase__			)}
  __lowercase						=						map_nested(lambda UpperCamelCase__			: x + 10					, UpperCamelCase__					, num_proc=UpperCamelCase__					, parallel_min_length=16			)
  if expected_num_proc == 1:
   assert mock_single_map_nested.called
   assert not mock_multiprocessing_pool.called
  else:
   assert not mock_single_map_nested.called
   assert mock_multiprocessing_pool.called
   assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc





class  lowerCamelCase__    (		_a     ):
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       import tensorflow as tf
       from tensorflow.keras import layers

       __lowercase						=						layers.Dense(2				)

       def gen_random_output():
        __lowercase						=						tf.random.uniform((1, 3)				)
        return model(A_				).numpy()

       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_tensorflow=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

       np.testing.assert_equal(A_   ,							A_				)
       self.assertGreater(np.abs(outa - outa				).sum()   ,							0				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       import torch

       def gen_random_output():
        __lowercase						=						torch.nn.Linear(3   ,							2				)
        __lowercase						=						torch.rand(1   ,							3				)
        return model(A_				).detach().numpy()

       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2   ,							set_pytorch=A_				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

       np.testing.assert_equal(A_   ,							A_				)
       self.assertGreater(np.abs(outa - outa				).sum()   ,							0				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       def gen_random_output():
        return np.random.rand(1   ,							3				)

       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       with temp_seed(4_2				):
        __lowercase						=						gen_random_output()
       __lowercase						=						gen_random_output()

       np.testing.assert_equal(A_   ,							A_				)
       self.assertGreater(np.abs(outa - outa				).sum()   ,							0				)






@pytest.mark.parametrize("""input_data"""					, [{}]			)
def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).data
 assert output_data == input_data


@pytest.mark.parametrize(
    """data, expected_output"""					, [
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]					, )
def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						NestedDataStructure(UpperCamelCase__			).flatten()
 assert output == expected_output


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						A(x=1					, y="""foobar"""			)
 __lowercase						=						{"""x""": 1, """y""": """foobar"""}
 assert asdict(UpperCamelCase__			) == expected_output

 __lowercase						=						{"""a""": {"""b""": A(x=10					, y="""foo"""			)}, """c""": [A(x=20					, y="""bar"""			)]}
 __lowercase						=						{"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
 assert asdict(UpperCamelCase__			) == expected_output

 with pytest.raises(UpperCamelCase__			):
  asdict([1, A(x=10					, y="""foo"""			)]			)


def lowerCAmelCase_   (					UpperCamelCase__						:			str			):






 """simple docstring"""







 return text.split()


def lowerCAmelCase_   (					UpperCamelCase__						:			Dict			):






 """simple docstring"""







 yield (time.time(), content)
 time.sleep(2			)
 yield (time.time(), content)


def lowerCAmelCase_   (					):






 """simple docstring"""







 with Pool(2			) as pool:
  __lowercase						=						list(iflatmap_unordered(UpperCamelCase__					, _split_text					, kwargs_iterable=[{"""text""": """hello there"""}] * 10			)			)
  assert out.count("""hello"""			) == 10
  assert out.count("""there"""			) == 10
  assert len(UpperCamelCase__			) == 20

 # check multiprocess from pathos (uses dill for pickling)
 with multiprocess.Pool(2			) as pool:
  __lowercase						=						list(iflatmap_unordered(UpperCamelCase__					, _split_text					, kwargs_iterable=[{"""text""": """hello there"""}] * 10			)			)
  assert out.count("""hello"""			) == 10
  assert out.count("""there"""			) == 10
  assert len(UpperCamelCase__			) == 20

 # check that we get items as fast as possible
 with Pool(2			) as pool:
  __lowercase						=						[]
  for yield_time, content in iflatmap_unordered(
      UpperCamelCase__					, _aseconds_generator_of_aitems_with_timing					, kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]			):
   assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
   out.append(UpperCamelCase__			)
  assert out.count("""a"""			) == 2
  assert out.count("""b"""			) == 2
  assert len(UpperCamelCase__			) == 4
