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


A   :     Dict       =				random.Random()


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=1.0   ,					__UpperCamelCase=None   ,					__UpperCamelCase=None ):
      if rng is None:
            SCREAMING_SNAKE_CASE_      =     global_rng

      SCREAMING_SNAKE_CASE_      =     []
      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    ):
  """simple docstring"""







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				int=7     ,   __magic_name__       :				int=400     ,   __magic_name__       :				List[str]=2_000     ,   __magic_name__       :				Union[str, Any]=1     ,   __magic_name__       :				Union[str, Any]=0.0     ,   __magic_name__       :				Optional[int]=16_000     ,   __magic_name__       :				int=True     ,   __magic_name__       :				List[str]=80     ,   __magic_name__       :				Any=16     ,   __magic_name__       :				Optional[int]=64     ,   __magic_name__       :				Dict="hann_window"     ,   __magic_name__       :				List[str]=80     ,   __magic_name__       :				Union[str, Any]=7_600     ,   __magic_name__       :				Tuple=1e-10     ,   __magic_name__       :				str=True     ,   )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     parent
        SCREAMING_SNAKE_CASE_      =     batch_size
        SCREAMING_SNAKE_CASE_      =     min_seq_length
        SCREAMING_SNAKE_CASE_      =     max_seq_length
        SCREAMING_SNAKE_CASE_      =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        SCREAMING_SNAKE_CASE_      =     feature_size
        SCREAMING_SNAKE_CASE_      =     padding_value
        SCREAMING_SNAKE_CASE_      =     sampling_rate
        SCREAMING_SNAKE_CASE_      =     do_normalize
        SCREAMING_SNAKE_CASE_      =     num_mel_bins
        SCREAMING_SNAKE_CASE_      =     hop_length
        SCREAMING_SNAKE_CASE_      =     win_length
        SCREAMING_SNAKE_CASE_      =     win_function
        SCREAMING_SNAKE_CASE_      =     fmin
        SCREAMING_SNAKE_CASE_      =     fmax
        SCREAMING_SNAKE_CASE_      =     mel_floor
        SCREAMING_SNAKE_CASE_      =     return_attention_mask







  def       __A   (							self       :				str      )     ->		str:
        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       __A   (							self       :				Tuple     ,   __magic_name__       :				List[str]=False     ,   __magic_name__       :				Tuple=False      )     ->		str:
        def _flatten(__magic_name__       :				Tuple      ):
              return list(itertools.chain(*__magic_name__      )      )

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

        return speech_inputs







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				List[Any]=False     ,   __magic_name__       :				Optional[int]=False      )     ->		List[Any]:
        if equal_length:
              SCREAMING_SNAKE_CASE_      =     [floats_list((self.max_seq_length, self.num_mel_bins)      ) for _ in range(self.batch_size      )]
        else:
              # make sure that inputs increase in size
              SCREAMING_SNAKE_CASE_      =     [
                  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:
              SCREAMING_SNAKE_CASE_      =     [np.asarray(__magic_name__      ) for x in speech_inputs]

        return speech_inputs



@require_torch
class      lowerCamelCase    (__lowerCamelCase     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  SpeechTaFeatureExtractor







  def       __A   (							self       :				Dict      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     SpeechTaFeatureExtractionTester(self      )







  def       __A   (							self       :				Any     ,   __magic_name__       :				str      )     ->		Tuple:
        self.assertTrue(np.all(np.mean(__magic_name__     ,   axis=0      ) < 1e-3      )      )
        self.assertTrue(np.all(np.abs(np.var(__magic_name__     ,   axis=0      ) - 1      ) < 1e-3      )      )







  def       __A   (							self       :				Any      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        # create three inputs of length 800, 1000, and 1200
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in range(800     ,   1_400     ,   200      )]
        SCREAMING_SNAKE_CASE_      =     [np.asarray(__magic_name__      ) for speech_input in speech_inputs]

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

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







  def       __A   (							self       :				Optional[int]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in range(800     ,   1_400     ,   200      )]

        SCREAMING_SNAKE_CASE_      =     ['''longest''', '''max_length''', '''do_not_pad''']
        SCREAMING_SNAKE_CASE_      =     [None, 1_600, None]
        for max_length, padding in zip(__magic_name__     ,   __magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     feat_extract(__magic_name__     ,   padding=__magic_name__     ,   max_length=__magic_name__     ,   return_tensors="np"      )
              SCREAMING_SNAKE_CASE_      =     processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:800]      )
              self.assertTrue(input_values[0][800:].sum() < 1e-6      )
              self._check_zero_mean_unit_variance(input_values[1][:1_000]      )
              self.assertTrue(input_values[0][1_000:].sum() < 1e-6      )
              self._check_zero_mean_unit_variance(input_values[2][:1_200]      )







  def       __A   (							self       :				Tuple      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        SCREAMING_SNAKE_CASE_      =     range(800     ,   1_400     ,   200      )
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in lengths]

        SCREAMING_SNAKE_CASE_      =     ['''longest''', '''max_length''', '''do_not_pad''']
        SCREAMING_SNAKE_CASE_      =     [None, 1_600, None]

        for max_length, padding in zip(__magic_name__     ,   __magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     feat_extract(__magic_name__     ,   max_length=__magic_name__     ,   padding=__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     processed.input_values

              self._check_zero_mean_unit_variance(input_values[0][:800]      )
              self._check_zero_mean_unit_variance(input_values[1][:1_000]      )
              self._check_zero_mean_unit_variance(input_values[2][:1_200]      )







  def       __A   (							self       :				Union[str, Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in range(800     ,   1_400     ,   200      )]
        SCREAMING_SNAKE_CASE_      =     feat_extract(
            __magic_name__     ,   truncation=__magic_name__     ,   max_length=1_000     ,   padding="max_length"     ,   return_tensors="np"      )
        SCREAMING_SNAKE_CASE_      =     processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]      )
        self._check_zero_mean_unit_variance(input_values[1]      )
        self._check_zero_mean_unit_variance(input_values[2]      )







  def       __A   (							self       :				int      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in range(800     ,   1_400     ,   200      )]
        SCREAMING_SNAKE_CASE_      =     feat_extract(
            __magic_name__     ,   truncation=__magic_name__     ,   max_length=1_000     ,   padding="longest"     ,   return_tensors="np"      )
        SCREAMING_SNAKE_CASE_      =     processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]      )
        self._check_zero_mean_unit_variance(input_values[1, :1_000]      )
        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_000)      )

        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in range(800     ,   1_400     ,   200      )]
        SCREAMING_SNAKE_CASE_      =     feat_extract(
            __magic_name__     ,   truncation=__magic_name__     ,   max_length=2_000     ,   padding="longest"     ,   return_tensors="np"      )
        SCREAMING_SNAKE_CASE_      =     processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800]      )
        self._check_zero_mean_unit_variance(input_values[1, :1_000]      )
        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_200)      )







  def       __A   (							self       :				Any      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        SCREAMING_SNAKE_CASE_      =     np.random.rand(100      ).astype(np.floataa      )
        SCREAMING_SNAKE_CASE_      =     np_speech_inputs.tolist()

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







  def       __A   (							self       :				List[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
        # create three inputs of length 800, 1000, and 1200
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in range(800     ,   1_400     ,   200      )]
        SCREAMING_SNAKE_CASE_      =     [np.asarray(__magic_name__      ) for speech_input in speech_inputs]

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

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

        # Test 2-D numpy arrays are batched.
        SCREAMING_SNAKE_CASE_      =     [floats_list((1, x)      )[0] for x in (800, 800, 800)]
        SCREAMING_SNAKE_CASE_      =     np.asarray(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     feature_extractor(__magic_name__     ,   return_tensors="np"      ).input_values
        SCREAMING_SNAKE_CASE_      =     feature_extractor(__magic_name__     ,   return_tensors="np"      ).input_values
        for enc_seq_a, enc_seq_a in zip(__magic_name__     ,   __magic_name__      ):
              self.assertTrue(np.allclose(__magic_name__     ,   __magic_name__     ,   atol=1e-3      )      )







  def       __A   (							self       :				List[str]      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     self.feat_extract_tester.prepare_inputs_for_target()
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_dict      )
        SCREAMING_SNAKE_CASE_      =     feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_      =     BatchFeature({input_name: speech_inputs}      )

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

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

        SCREAMING_SNAKE_CASE_      =     processed_features[input_name]

        if len(batch_features_input.shape      ) < 3:
              SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				Tuple      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     self.feat_extract_tester.prepare_inputs_for_target(equal_length=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_dict      )
        SCREAMING_SNAKE_CASE_      =     feat_extract.model_input_names[0]

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

        SCREAMING_SNAKE_CASE_      =     processed_features[input_name]

        if len(batch_features_input.shape      ) < 3:
              SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				Tuple      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**self.feat_extract_dict      )
        SCREAMING_SNAKE_CASE_      =     self.feat_extract_tester.prepare_inputs_for_target()
        SCREAMING_SNAKE_CASE_      =     feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_      =     BatchFeature({input_name: speech_inputs}      )

        SCREAMING_SNAKE_CASE_      =     feat_extract.num_mel_bins  # hack!

        SCREAMING_SNAKE_CASE_      =     feat_extract.pad(__magic_name__     ,   padding="longest"     ,   return_tensors="np"      )[input_name]
        SCREAMING_SNAKE_CASE_      =     feat_extract.pad(__magic_name__     ,   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       __A   (							self       :				List[str]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     self.feat_extract_dict
        SCREAMING_SNAKE_CASE_      =     True
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.feat_extract_tester.prepare_inputs_for_target()
        SCREAMING_SNAKE_CASE_      =     [len(__magic_name__      ) for x in speech_inputs]
        SCREAMING_SNAKE_CASE_      =     feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_      =     BatchFeature({input_name: speech_inputs}      )

        SCREAMING_SNAKE_CASE_      =     feat_extract.num_mel_bins  # hack!

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







  def       __A   (							self       :				Optional[Any]      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.feat_extract_dict
        SCREAMING_SNAKE_CASE_      =     True
        SCREAMING_SNAKE_CASE_      =     self.feature_extraction_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.feat_extract_tester.prepare_inputs_for_target()
        SCREAMING_SNAKE_CASE_      =     [len(__magic_name__      ) for x in speech_inputs]
        SCREAMING_SNAKE_CASE_      =     feat_extract.model_input_names[0]

        SCREAMING_SNAKE_CASE_      =     BatchFeature({input_name: speech_inputs}      )
        SCREAMING_SNAKE_CASE_      =     min(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     feat_extract.num_mel_bins  # hack!

        SCREAMING_SNAKE_CASE_      =     feat_extract.pad(
            __magic_name__     ,   padding="max_length"     ,   max_length=__magic_name__     ,   truncation=__magic_name__     ,   return_tensors="np"      )
        self.assertIn("attention_mask"     ,   __magic_name__      )
        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       __A   (							self       :				Any     ,   __magic_name__       :				int      )     ->		Any:
        from datasets import load_dataset

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

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







  def       __A   (							self       :				List[Any]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     torch.tensor(
            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]      )
        # fmt: on

        SCREAMING_SNAKE_CASE_      =     self._load_datasamples(1      )
        SCREAMING_SNAKE_CASE_      =     SpeechTaFeatureExtractor()
        SCREAMING_SNAKE_CASE_      =     feature_extractor(__magic_name__     ,   return_tensors="pt"      ).input_values
        self.assertEquals(input_values.shape     ,   (1, 93_680)      )
        self.assertTrue(torch.allclose(input_values[0, :30]     ,   __magic_name__     ,   atol=1e-6      )      )







  def       __A   (							self       :				List[Any]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     torch.tensor(
            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]      )
        # fmt: on

        SCREAMING_SNAKE_CASE_      =     self._load_datasamples(1      )
        SCREAMING_SNAKE_CASE_      =     SpeechTaFeatureExtractor()
        SCREAMING_SNAKE_CASE_      =     feature_extractor(audio_target=__magic_name__     ,   return_tensors="pt"      ).input_values
        self.assertEquals(input_values.shape     ,   (1, 366, 80)      )
        self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,   __magic_name__     ,   atol=1e-4      )      )









import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Tuple          =      re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''							)
           UpperCamelCase__							:				str          =      input_file.read()
           UpperCamelCase__							:				List[Any]          =      regexp.search(__magic_name__							)

       return match




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Dict          =      re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''',		re.DOTALL							)
           UpperCamelCase__							:				Any          =      input_file.read()
           # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
           UpperCamelCase__							:				Tuple          =      regexp.finditer(__magic_name__							)

       UpperCamelCase__							:				Dict          =      [match for match in matches if match is not None and match.group(1							) is not None]
       return matches[0] if matches else None




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Any          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_encoding_on_file_open(str(__magic_name__							)							):
               raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Optional[Any]          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_print_statements(str(__magic_name__							)							):
               raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."							)

