






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	# to overwrite at feature extractactor specific tests
	__snake_case						:Optional[int] =							None
	__snake_case						:Dict =							None







	@property
	def 			_a (	self   :				str      )		->			List[str]:
		"""simple docstring"""







		return self.feat_extract_tester.prepare_feat_extract_dict()







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







		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_dict      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""feature_size"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""sampling_rate"""      )      )
		self.assertTrue(hasattr(_lowerCAmelCase							,					"""padding_value"""      )      )







	def 			_a (	self   :				Tuple      )		->			str:
		"""simple docstring"""







		__lowercase   							=	self.feat_extract_tester.prepare_inputs_for_common()
		__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(_lowerCAmelCase      ) == len(_lowerCAmelCase      ) for x, y in zip(_lowerCAmelCase							,					processed_features[input_name]      )      )      )

		__lowercase   							=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase      )
		__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.feature_size)      )







	@require_torch
	def 			_a (	self   :				List[Any]      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase      )
		__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.feature_size)      )







	@require_tf
	def 			_a (	self   :				Optional[Any]      )		->			Optional[int]:
		"""simple docstring"""







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

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

		__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.feature_size)      )







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				List[Any]=False      )		->			int:
		"""simple docstring"""







		def _inputs_have_equal_length(_lowerCAmelCase   :				int      ):
			__lowercase   							=	len(input[0]      )
			for input_slice in input[1:]:
				if len(_lowerCAmelCase      ) != length:
					return False
			return True

		def _inputs_are_equal(_lowerCAmelCase   :				Dict							,					_lowerCAmelCase   :				Tuple      ):
			if len(_lowerCAmelCase      ) != len(_lowerCAmelCase      ):
				return False

			for input_slice_a, input_slice_a in zip(_lowerCAmelCase							,					_lowerCAmelCase      ):
				if not np.allclose(np.asarray(_lowerCAmelCase      )							,					np.asarray(_lowerCAmelCase      )							,					atol=1e-3      ):
					return False
			return True

		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_dict      )
		__lowercase   							=	self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase      )
		__lowercase   							=	feat_extract.model_input_names[0]

		__lowercase   							=	BatchFeature({input_name: speech_inputs}      )

		__lowercase   							=	self.feat_extract_tester.seq_length_diff
		__lowercase   							=	self.feat_extract_tester.max_seq_length + pad_diff
		__lowercase   							=	self.feat_extract_tester.min_seq_length
		__lowercase   							=	self.feat_extract_tester.batch_size
		__lowercase   							=	self.feat_extract_tester.feature_size

		# test padding for List[int] + numpy
		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding=_lowerCAmelCase      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[-1]      )      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""							,					return_tensors="""np"""      )
		__lowercase   							=	input_a[input_name]

		# max_length parameter has to be provided when setting `padding="max_length"`
		with self.assertRaises(_lowerCAmelCase      ):
			feat_extract.pad(_lowerCAmelCase							,					padding="""max_length"""      )[input_name]

		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=_lowerCAmelCase							,					return_tensors="""np"""      )
		__lowercase   							=	input_a[input_name]

		self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertTrue(_inputs_are_equal(_lowerCAmelCase							,					_lowerCAmelCase      )      )
		self.assertTrue(len(input_a[0]      ) == pad_min_length      )
		self.assertTrue(len(input_a[1]      ) == pad_min_length + pad_diff      )
		self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]      ))      )
		self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)      )

		if feature_size > 1:
			self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size      )

		# test padding for `pad_to_multiple_of` for List[int] + numpy
		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					pad_to_multiple_of=10      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""							,					pad_to_multiple_of=10      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					pad_to_multiple_of=10							,					max_length=_lowerCAmelCase      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					pad_to_multiple_of=10							,					max_length=_lowerCAmelCase							,					return_tensors="""np"""							,					)
		__lowercase   							=	input_a[input_name]

		self.assertTrue(all(len(_lowerCAmelCase      ) % 10 == 0 for x in input_a      )      )
		self.assertTrue(_inputs_are_equal(_lowerCAmelCase							,					_lowerCAmelCase      )      )

		__lowercase   							=	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
		self.assertTrue(all(len(_lowerCAmelCase      ) == expected_mult_pad_length for x in input_a      )      )
		self.assertEqual(input_a.shape[:2]							,					(batch_size, expected_mult_pad_length)      )

		if feature_size > 1:
			self.assertTrue(input_a.shape[2] == feature_size      )

		# Check padding value is correct
		__lowercase   							=	(np.ones(self.feat_extract_tester.feature_size      ) * feat_extract.padding_value).sum()
		self.assertTrue(
		    abs(np.asarray(input_a[0]      )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      )
		    < 1e-3      )
		self.assertTrue(
		    abs(
		        np.asarray(input_a[1]      )[pad_min_length + pad_diff :].sum()
		        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)      )
		    < 1e-3      )
		self.assertTrue(
		    abs(
		        np.asarray(input_a[2]      )[pad_min_length + 2 * pad_diff :].sum()
		        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)      )
		    < 1e-3      )
		self.assertTrue(
		    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      ) < 1e-3      )
		self.assertTrue(
		    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)      )
		    < 1e-3      )







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				str=False      )		->			Union[str, Any]:
		"""simple docstring"""







		def _inputs_have_equal_length(_lowerCAmelCase   :				Tuple      ):
			__lowercase   							=	len(input[0]      )
			for input_slice in input[1:]:
				if len(_lowerCAmelCase      ) != length:
					return False
			return True

		def _inputs_are_equal(_lowerCAmelCase   :				Any							,					_lowerCAmelCase   :				str      ):
			if len(_lowerCAmelCase      ) != len(_lowerCAmelCase      ):
				return False

			for input_slice_a, input_slice_a in zip(_lowerCAmelCase							,					_lowerCAmelCase      ):
				if not np.allclose(np.asarray(_lowerCAmelCase      )							,					np.asarray(_lowerCAmelCase      )							,					atol=1e-3      ):
					return False
			return True

		__lowercase   							=	self.feature_extraction_class(**self.feat_extract_dict      )
		__lowercase   							=	self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase      )
		__lowercase   							=	feat_extract.model_input_names[0]

		__lowercase   							=	BatchFeature({input_name: speech_inputs}      )

		# truncate to smallest
		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[0]      )							,					truncation=_lowerCAmelCase      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[0]      )      )
		__lowercase   							=	input_a[input_name]

		self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase      )      )

		# truncate to smallest with np
		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[0]      )							,					return_tensors="""np"""							,					truncation=_lowerCAmelCase							,					)
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[0]      )							,					return_tensors="""np"""      )
		__lowercase   							=	input_a[input_name]

		self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertTrue(input_a.shape[1] == len(speech_inputs[0]      )      )

		# since truncation forces padding to be smaller than longest input
		# function can't return `np.ndarray`, but has to return list
		self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase      )      )

		# truncate to middle
		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[1]      )							,					truncation=_lowerCAmelCase							,					return_tensors="""np"""							,					)
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[1]      )							,					truncation=_lowerCAmelCase      )
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[1]      )							,					return_tensors="""np"""      )
		__lowercase   							=	input_a[input_name]

		self.assertTrue(input_a.shape[1] == len(speech_inputs[1]      )      )
		self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertTrue(_inputs_are_equal(_lowerCAmelCase							,					_lowerCAmelCase      )      )

		# since truncation forces padding to be smaller than longest input
		# function can't return `np.ndarray`, but has to return list
		self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertTrue(len(input_a[-1]      ) == len(speech_inputs[-1]      )      )

		# padding has to be max_length when setting `truncation=True`
		with self.assertRaises(_lowerCAmelCase      ):
			feat_extract.pad(_lowerCAmelCase							,					truncation=_lowerCAmelCase      )[input_name]

		# padding has to be max_length when setting `truncation=True`
		with self.assertRaises(_lowerCAmelCase      ):
			feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""							,					truncation=_lowerCAmelCase      )[input_name]

		# padding has to be max_length when setting `truncation=True`
		with self.assertRaises(_lowerCAmelCase      ):
			feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""							,					truncation=_lowerCAmelCase      )[input_name]

		# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
		with self.assertRaises(_lowerCAmelCase      ):
			feat_extract.pad(_lowerCAmelCase							,					padding="""max_length"""							,					truncation=_lowerCAmelCase      )[input_name]

		# test truncation for `pad_to_multiple_of` for List[int] + numpy
		__lowercase   							=	12
		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[0]      )							,					pad_to_multiple_of=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					)
		__lowercase   							=	input_a[input_name]

		__lowercase   							=	feat_extract.pad(
		    _lowerCAmelCase							,					padding="""max_length"""							,					max_length=len(speech_inputs[0]      )							,					pad_to_multiple_of=_lowerCAmelCase							,					)
		__lowercase   							=	input_a[input_name]

		# retrieve expected_length as multiple of pad_to_multiple_of
		__lowercase   							=	len(speech_inputs[0]      )
		if expected_length % pad_to_multiple_of != 0:
			__lowercase   							=	((len(speech_inputs[0]      ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

		self.assertTrue(len(input_a[0]      ) == expected_length      )
		self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase      )      )
		self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase      )      )







	def 			_a (	self   :				List[str]      )		->			Optional[int]:
		"""simple docstring"""







		self._check_padding(numpify=_lowerCAmelCase      )







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







		self._check_padding(numpify=_lowerCAmelCase      )







	def 			_a (	self   :				int      )		->			Tuple:
		"""simple docstring"""







		self._check_truncation(numpify=_lowerCAmelCase      )







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







		self._check_truncation(numpify=_lowerCAmelCase      )







	@require_torch
	def 			_a (	self   :				Optional[Any]      )		->			Tuple:
		"""simple docstring"""







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

		__lowercase   							=	BatchFeature({input_name: speech_inputs}      )

		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""							,					return_tensors="""np"""      )[input_name]
		__lowercase   							=	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      )







	@require_tf
	def 			_a (	self   :				Any      )		->			Any:
		"""simple docstring"""







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

		__lowercase   							=	BatchFeature({input_name: speech_inputs}      )

		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""							,					return_tensors="""np"""      )[input_name]
		__lowercase   							=	feat_extract.pad(_lowerCAmelCase							,					padding="""longest"""							,					return_tensors="""tf"""      )[input_name]

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







	def 			_a (	self   :				Union[str, Any]      )		->			Optional[Any]:
		"""simple docstring"""







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

		__lowercase   							=	BatchFeature({input_name: speech_inputs}      )

		__lowercase   							=	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 			_a (	self   :				List[Any]      )		->			Union[str, Any]:
		"""simple docstring"""







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

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

		__lowercase   							=	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]      )


import argparse
import os
import re


_lowercase:	Optional[Any]       			=		'''src/transformers/models/auto'''


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






def _lowerCamelCase  (		snake_case     , snake_case = False  ):
							with open(snake_case     , 'r'     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							f.read()

							_lowerCAmelCase							=							content.split('\n'  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							0
							while line_idx < len(snake_case  ):
														if _re_intro_mapping.search(lines[line_idx]  ) is not None:
																					_lowerCAmelCase							=							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

																					_lowerCAmelCase							=							[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			_lowerCAmelCase							=							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
																					_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : _re_identifier.search(snake_case  ).groups()[0]  )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]  )
																					line_idx += 1

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






def _lowerCamelCase  (		snake_case = False  ):
							_lowerCAmelCase							=							[os.path.join(snake_case     , snake_case  ) for f in os.listdir(snake_case  ) if f.endswith('.py'  )]
							_lowerCAmelCase							=							[sort_auto_mapping(snake_case     , overwrite=snake_case  ) for fname in fnames]

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


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
				_lowercase:	Tuple       			=		parser.parse_args()

				sort_all_auto_mappings(not args.check_only)






