





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


SCREAMING_SNAKE_CASE__							:      Optional[int]         =    random.Random()






def    _A	(   lowerCamelCase				,			lowerCamelCase=1.0				,			lowerCamelCase=None				,			lowerCamelCase=None ):
   if rng is None:
      a__						:    str   		=	global_rng

   a__						:    Union[str, Any]   		=	[]
   for batch_idx in range(shape[0] ):
      values.append([] )
      for _ in range(shape[1] ):
         values[-1].append(rng.random() * scale )

   return values







@require_torch
class 	__lowerCAmelCase      (       unittest.TestCase	):





   def __init__(			self       ,      snake_case       ,      snake_case=7       ,      snake_case=400       ,      snake_case=2_000       ,      snake_case=1       ,      snake_case=0.0       ,      snake_case=16_000       ,      snake_case=True       ,      snake_case=80       ,      snake_case=16       ,      snake_case=64       ,      snake_case="hann_window"       ,      snake_case=80       ,      snake_case=7_600       ,      snake_case=1E-10       ,      snake_case=True       ,      )					->	str:
      """simple docstring"""





      a__						:    Optional[Any]   		=	parent
      a__						:    str   		=	batch_size
      a__						:    Optional[int]   		=	min_seq_length
      a__						:    Dict   		=	max_seq_length
      a__						:    List[str]   		=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      a__						:    Union[str, Any]   		=	feature_size
      a__						:    List[str]   		=	padding_value
      a__						:    Dict   		=	sampling_rate
      a__						:    str   		=	do_normalize
      a__						:    List[Any]   		=	num_mel_bins
      a__						:    List[Any]   		=	hop_length
      a__						:    Any   		=	win_length
      a__						:    str   		=	win_function
      a__						:    int   		=	fmin
      a__						:    Tuple   		=	fmax
      a__						:    str   		=	mel_floor
      a__						:    Tuple   		=	return_attention_mask





   def 						_snake_case			(			self   )					->	List[Any]:
      """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 						_snake_case			(			self       ,      snake_case=False       ,      snake_case=False   )					->	Optional[Any]:
      """simple docstring"""





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

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

      return speech_inputs





   def 						_snake_case			(			self       ,      snake_case=False       ,      snake_case=False   )					->	str:
      """simple docstring"""





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

      return speech_inputs







@require_torch
class 	__lowerCAmelCase      (       _UpperCamelCase		,unittest.TestCase	):
   _UpperCamelCase			:    Any  		=					SpeechTaFeatureExtractor





   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    Any   		=	SpeechTaFeatureExtractionTester(self   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      self.assertTrue(np.all(np.mean(snake_case       ,      axis=0   ) < 1E-3   )   )
      self.assertTrue(np.all(np.abs(np.var(snake_case       ,      axis=0   ) - 1   ) < 1E-3   )   )





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





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

      # Test not batched input
      a__						:    str   		=	feat_extract(speech_inputs[0]       ,      return_tensors="np"   ).input_values
      a__						:    str   		=	feat_extract(np_speech_inputs[0]       ,      return_tensors="np"   ).input_values
      self.assertTrue(np.allclose(snake_case       ,      snake_case       ,      atol=1E-3   )   )

      # Test batched
      a__						:    Optional[int]   		=	feat_extract(snake_case       ,      return_tensors="np"   ).input_values
      a__						:    Optional[int]   		=	feat_extract(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 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      a__						:    Dict   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    List[Any]   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]

      a__						:    Optional[int]   		=	["longest", "max_length", "do_not_pad"]
      a__						:    int   		=	[None, 1_600, None]
      for max_length, padding in zip(snake_case       ,      snake_case   ):
         a__						:    Tuple   		=	feat_extract(snake_case       ,      padding=snake_case       ,      max_length=snake_case       ,      return_tensors="np"   )
         a__						:    Tuple   		=	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 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





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

      a__						:    Optional[Any]   		=	["longest", "max_length", "do_not_pad"]
      a__						:    Tuple   		=	[None, 1_600, None]

      for max_length, padding in zip(snake_case       ,      snake_case   ):
         a__						:    Union[str, Any]   		=	feat_extract(snake_case       ,      max_length=snake_case       ,      padding=snake_case   )
         a__						:    Any   		=	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 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      a__						:    List[Any]   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    Dict   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    int   		=	feat_extract(
          snake_case       ,      truncation=snake_case       ,      max_length=1_000       ,      padding="max_length"       ,      return_tensors="np"   )
      a__						:    Optional[int]   		=	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 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    int   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    str   		=	feat_extract(
          snake_case       ,      truncation=snake_case       ,      max_length=1_000       ,      padding="longest"       ,      return_tensors="np"   )
      a__						:    Dict   		=	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)   )

      a__						:    List[str]   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    Any   		=	feat_extract(
          snake_case       ,      truncation=snake_case       ,      max_length=2_000       ,      padding="longest"       ,      return_tensors="np"   )
      a__						:    int   		=	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 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    Tuple   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      a__						:    Any   		=	np.random.rand(100   ).astype(np.floataa   )
      a__						:    Optional[Any]   		=	np_speech_inputs.tolist()

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





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      a__						:    str   		=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
      # create three inputs of length 800, 1000, and 1200
      a__						:    Dict   		=	[floats_list((1, x)   )[0] for x in range(800       ,      1_400       ,      200   )]
      a__						:    Dict   		=	[np.asarray(snake_case   ) for speech_input in speech_inputs]

      # Test feature size
      a__						:    int   		=	feature_extractor(audio_target=snake_case       ,      padding=snake_case       ,      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
      a__						:    List[Any]   		=	feature_extractor(speech_inputs[0]       ,      return_tensors="np"   ).input_values
      a__						:    Optional[Any]   		=	feature_extractor(np_speech_inputs[0]       ,      return_tensors="np"   ).input_values
      self.assertTrue(np.allclose(snake_case       ,      snake_case       ,      atol=1E-3   )   )

      # Test batched
      a__						:    Dict   		=	feature_extractor(snake_case       ,      return_tensors="np"   ).input_values
      a__						:    str   		=	feature_extractor(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   )   )

      # Test 2-D numpy arrays are batched.
      a__						:    Optional[Any]   		=	[floats_list((1, x)   )[0] for x in (800, 800, 800)]
      a__						:    int   		=	np.asarray(snake_case   )
      a__						:    Any   		=	feature_extractor(snake_case       ,      return_tensors="np"   ).input_values
      a__						:    Dict   		=	feature_extractor(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 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Optional[Any]   		=	self.feat_extract_tester.prepare_inputs_for_target()
      a__						:    Optional[Any]   		=	self.feature_extraction_class(**self.feat_extract_dict   )
      a__						:    Optional[Any]   		=	feat_extract.model_input_names[0]

      a__						:    str   		=	BatchFeature({input_name: speech_inputs}   )

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

      a__						:    Optional[int]   		=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=snake_case   )
      a__						:    Tuple   		=	BatchFeature({input_name: speech_inputs}       ,      tensor_type="np"   )

      a__						:    Tuple   		=	processed_features[input_name]

      if len(batch_features_input.shape   ) < 3:
         a__						:    str   		=	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   )					->	Any:
      """simple docstring"""





      a__						:    Optional[int]   		=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=snake_case   )
      a__						:    Optional[Any]   		=	self.feature_extraction_class(**self.feat_extract_dict   )
      a__						:    Dict   		=	feat_extract.model_input_names[0]

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

      a__						:    int   		=	processed_features[input_name]

      if len(batch_features_input.shape   ) < 3:
         a__						:    Any   		=	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   )					->	Dict:
      """simple docstring"""





      a__						:    List[Any]   		=	self.feature_extraction_class(**self.feat_extract_dict   )
      a__						:    Optional[int]   		=	self.feat_extract_tester.prepare_inputs_for_target()
      a__						:    Dict   		=	feat_extract.model_input_names[0]

      a__						:    Any   		=	BatchFeature({input_name: speech_inputs}   )

      a__						:    str   		=	feat_extract.num_mel_bins  # hack!

      a__						:    Dict   		=	feat_extract.pad(snake_case       ,      padding="longest"       ,      return_tensors="np"   )[input_name]
      a__						:    Optional[Any]   		=	feat_extract.pad(snake_case       ,      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   )					->	Dict:
      """simple docstring"""





      a__						:    int   		=	self.feat_extract_dict
      a__						:    List[str]   		=	True
      a__						:    Union[str, Any]   		=	self.feature_extraction_class(**snake_case   )
      a__						:    Any   		=	self.feat_extract_tester.prepare_inputs_for_target()
      a__						:    List[str]   		=	[len(snake_case   ) for x in speech_inputs]
      a__						:    int   		=	feat_extract.model_input_names[0]

      a__						:    List[str]   		=	BatchFeature({input_name: speech_inputs}   )

      a__						:    Union[str, Any]   		=	feat_extract.num_mel_bins  # hack!

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





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      a__						:    Tuple   		=	self.feat_extract_dict
      a__						:    str   		=	True
      a__						:    int   		=	self.feature_extraction_class(**snake_case   )
      a__						:    Any   		=	self.feat_extract_tester.prepare_inputs_for_target()
      a__						:    Dict   		=	[len(snake_case   ) for x in speech_inputs]
      a__						:    List[Any]   		=	feat_extract.model_input_names[0]

      a__						:    Optional[Any]   		=	BatchFeature({input_name: speech_inputs}   )
      a__						:    Union[str, Any]   		=	min(snake_case   )

      a__						:    Union[str, Any]   		=	feat_extract.num_mel_bins  # hack!

      a__						:    Any   		=	feat_extract.pad(
          snake_case       ,      padding="max_length"       ,      max_length=snake_case       ,      truncation=snake_case       ,      return_tensors="np"   )
      self.assertIn("attention_mask"       ,      snake_case   )
      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       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      from datasets import load_dataset

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

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





   def 						_snake_case			(			self   )					->	List[Any]:
      """simple docstring"""





      a__						:    Dict   		=	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

      a__						:    Dict   		=	self._load_datasamples(1   )
      a__						:    Union[str, Any]   		=	SpeechTaFeatureExtractor()
      a__						:    Any   		=	feature_extractor(snake_case       ,      return_tensors="pt"   ).input_values
      self.assertEquals(input_values.shape       ,      (1, 93_680)   )
      self.assertTrue(torch.allclose(input_values[0, :30]       ,      snake_case       ,      atol=1E-6   )   )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      a__						:    Tuple   		=	torch.tensor(
          [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
           -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
           -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
           -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]   )
      # fmt: on

      a__						:    Optional[Any]   		=	self._load_datasamples(1   )
      a__						:    Optional[int]   		=	SpeechTaFeatureExtractor()
      a__						:    int   		=	feature_extractor(audio_target=snake_case       ,      return_tensors="pt"   ).input_values
      self.assertEquals(input_values.shape       ,      (1, 366, 80)   )
      self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,      snake_case       ,      atol=1E-4   )   )








import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

