





"""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


A			      =	random.Random()

def      __A   (      a_    :List[str] ,							a_    :int=1.0 ,							a_    :Optional[Any]=None ,							a_    :int=None)		->		List[Any]:
				if rng is None:
								__a			:    int      							=	global_rng

				__a			:    Optional[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 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=7						,      _UpperCAmelCase=400						,      _UpperCAmelCase=2000						,      _UpperCAmelCase=1						,      _UpperCAmelCase=0.0						,      _UpperCAmelCase=16000						,      _UpperCAmelCase=True						,      _UpperCAmelCase=80						,      _UpperCAmelCase=16						,      _UpperCAmelCase=64						,      _UpperCAmelCase="hann_window"						,      _UpperCAmelCase=80						,      _UpperCAmelCase=7600						,      _UpperCAmelCase=1e-1_0						,      _UpperCAmelCase=True						,      ):
									__a			:    Optional[Any]      							=	parent
									__a			:    int      							=	batch_size
									__a			:    Optional[int]      							=	min_seq_length
									__a			:    Any      							=	max_seq_length
									__a			:    Optional[int]      							=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									__a			:    Union[str, Any]      							=	feature_size
									__a			:    Optional[int]      							=	padding_value
									__a			:    int      							=	sampling_rate
									__a			:    str      							=	do_normalize
									__a			:    int      							=	num_mel_bins
									__a			:    Dict      							=	hop_length
									__a			:    Dict      							=	win_length
									__a			:    Dict      							=	win_function
									__a			:    Optional[Any]      							=	fmin
									__a			:    Union[str, Any]      							=	fmax
									__a			:    Tuple      							=	mel_floor
									__a			:    Optional[Any]      							=	return_attention_mask







					def 			_lowerCamelCase		(    self  ):
									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 			_lowerCamelCase		(    self						,      _UpperCAmelCase=False						,      _UpperCAmelCase=False  ):
									def _flatten(_UpperCAmelCase  ):
													return list(itertools.chain(*_UpperCAmelCase  )  )

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

									return speech_inputs







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase=False						,      _UpperCAmelCase=False  ):
									if equal_length:
													__a			:    int      							=	[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			:    Optional[int]      							=	[
													    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			:    List[Any]      							=	[np.asarray(_UpperCAmelCase  ) for x in speech_inputs]

									return speech_inputs






@require_torch
class 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	SpeechTaFeatureExtractor







					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	SpeechTaFeatureExtractionTester(self  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									self.assertTrue(np.all(np.mean(_UpperCAmelCase						,      axis=0  ) < 1e-3  )  )
									self.assertTrue(np.all(np.abs(np.var(_UpperCAmelCase						,      axis=0  ) - 1  ) < 1e-3  )  )







					def 			_lowerCamelCase		(    self  ):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									__a			:    int      							=	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						,      1400						,      200  )]
									__a			:    Union[str, Any]      							=	[np.asarray(_UpperCAmelCase  ) for speech_input in speech_inputs]

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

									# Test batched
									__a			:    Any      							=	feat_extract(_UpperCAmelCase						,      return_tensors='''np'''  ).input_values
									__a			:    List[Any]      							=	feat_extract(_UpperCAmelCase						,      return_tensors='''np'''  ).input_values
									for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase						,      _UpperCAmelCase  ):
													self.assertTrue(np.allclose(_UpperCAmelCase						,      _UpperCAmelCase						,      atol=1e-3  )  )







					def 			_lowerCamelCase		(    self  ):
									__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						,      1400						,      200  )]

									__a			:    Optional[Any]      							=	['''longest''', '''max_length''', '''do_not_pad''']
									__a			:    Tuple      							=	[None, 1600, None]
									for max_length, padding in zip(_UpperCAmelCase						,      _UpperCAmelCase  ):
													__a			:    Union[str, Any]      							=	feat_extract(_UpperCAmelCase						,      padding=_UpperCAmelCase						,      max_length=_UpperCAmelCase						,      return_tensors='''np'''  )
													__a			:    str      							=	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][:1000]  )
													self.assertTrue(input_values[0][1000:].sum() < 1e-6  )
													self._check_zero_mean_unit_variance(input_values[2][:1200]  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    int      							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									__a			:    Any      							=	range(800						,      1400						,      200  )
									__a			:    Dict      							=	[floats_list((1, x)  )[0] for x in lengths]

									__a			:    int      							=	['''longest''', '''max_length''', '''do_not_pad''']
									__a			:    Any      							=	[None, 1600, None]

									for max_length, padding in zip(_UpperCAmelCase						,      _UpperCAmelCase  ):
													__a			:    int      							=	feat_extract(_UpperCAmelCase						,      max_length=_UpperCAmelCase						,      padding=_UpperCAmelCase  )
													__a			:    int      							=	processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]  )
													self._check_zero_mean_unit_variance(input_values[1][:1000]  )
													self._check_zero_mean_unit_variance(input_values[2][:1200]  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	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						,      1400						,      200  )]
									__a			:    int      							=	feat_extract(
									    _UpperCAmelCase						,      truncation=_UpperCAmelCase						,      max_length=1000						,      padding='''max_length'''						,      return_tensors='''np'''  )
									__a			:    List[Any]      							=	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 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									__a			:    Optional[Any]      							=	[floats_list((1, x)  )[0] for x in range(800						,      1400						,      200  )]
									__a			:    List[str]      							=	feat_extract(
									    _UpperCAmelCase						,      truncation=_UpperCAmelCase						,      max_length=1000						,      padding='''longest'''						,      return_tensors='''np'''  )
									__a			:    List[Any]      							=	processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]  )
									self._check_zero_mean_unit_variance(input_values[1, :1000]  )
									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, 1000)  )

									__a			:    List[str]      							=	[floats_list((1, x)  )[0] for x in range(800						,      1400						,      200  )]
									__a			:    Optional[int]      							=	feat_extract(
									    _UpperCAmelCase						,      truncation=_UpperCAmelCase						,      max_length=2000						,      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, :1000]  )
									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, 1200)  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[str]      							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									__a			:    Optional[int]      							=	np.random.rand(100  ).astype(np.floataa  )
									__a			:    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			:    str      							=	feature_extractor.pad([{'''input_values''': inputs}]						,      return_tensors='''pt'''  )
													self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )







					def 			_lowerCamelCase		(    self  ):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									__a			:    Union[str, Any]      							=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									# create three inputs of length 800, 1000, and 1200
									__a			:    Tuple      							=	[floats_list((1, x)  )[0] for x in range(800						,      1400						,      200  )]
									__a			:    Tuple      							=	[np.asarray(_UpperCAmelCase  ) for speech_input in speech_inputs]

									# Test feature size
									__a			:    Union[str, Any]      							=	feature_extractor(audio_target=_UpperCAmelCase						,      padding=_UpperCAmelCase						,      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			:    Tuple      							=	feature_extractor(speech_inputs[0]						,      return_tensors='''np'''  ).input_values
									__a			:    int      							=	feature_extractor(np_speech_inputs[0]						,      return_tensors='''np'''  ).input_values
									self.assertTrue(np.allclose(_UpperCAmelCase						,      _UpperCAmelCase						,      atol=1e-3  )  )

									# Test batched
									__a			:    Union[str, Any]      							=	feature_extractor(_UpperCAmelCase						,      return_tensors='''np'''  ).input_values
									__a			:    Union[str, Any]      							=	feature_extractor(_UpperCAmelCase						,      return_tensors='''np'''  ).input_values
									for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase						,      _UpperCAmelCase  ):
													self.assertTrue(np.allclose(_UpperCAmelCase						,      _UpperCAmelCase						,      atol=1e-3  )  )

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







					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	self.feat_extract_tester.prepare_inputs_for_target()
									__a			:    int      							=	self.feature_extraction_class(**self.feat_extract_dict  )
									__a			:    Tuple      							=	feat_extract.model_input_names[0]

									__a			:    int      							=	BatchFeature({input_name: speech_inputs}  )

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

									__a			:    Union[str, Any]      							=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_UpperCAmelCase  )
									__a			:    List[str]      							=	BatchFeature({input_name: speech_inputs}						,      tensor_type='''np'''  )

									__a			:    Any      							=	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 			_lowerCamelCase		(    self  ):
									__a			:    str      							=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=_UpperCAmelCase  )
									__a			:    Dict      							=	self.feature_extraction_class(**self.feat_extract_dict  )
									__a			:    List[str]      							=	feat_extract.model_input_names[0]

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

									__a			:    List[Any]      							=	processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
													__a			:    List[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 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	self.feature_extraction_class(**self.feat_extract_dict  )
									__a			:    str      							=	self.feat_extract_tester.prepare_inputs_for_target()
									__a			:    Tuple      							=	feat_extract.model_input_names[0]

									__a			:    Optional[int]      							=	BatchFeature({input_name: speech_inputs}  )

									__a			:    int      							=	feat_extract.num_mel_bins  # hack!

									__a			:    Any      							=	feat_extract.pad(_UpperCAmelCase						,      padding='''longest'''						,      return_tensors='''np'''  )[input_name]
									__a			:    Optional[int]      							=	feat_extract.pad(_UpperCAmelCase						,      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 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	self.feat_extract_dict
									__a			:    str      							=	True
									__a			:    Dict      							=	self.feature_extraction_class(**_UpperCAmelCase  )
									__a			:    int      							=	self.feat_extract_tester.prepare_inputs_for_target()
									__a			:    Optional[Any]      							=	[len(_UpperCAmelCase  ) for x in speech_inputs]
									__a			:    Any      							=	feat_extract.model_input_names[0]

									__a			:    Dict      							=	BatchFeature({input_name: speech_inputs}  )

									__a			:    Any      							=	feat_extract.num_mel_bins  # hack!

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







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

									__a			:    Optional[Any]      							=	BatchFeature({input_name: speech_inputs}  )
									__a			:    Tuple      							=	min(_UpperCAmelCase  )

									__a			:    str      							=	feat_extract.num_mel_bins  # hack!

									__a			:    Dict      							=	feat_extract.pad(
									    _UpperCAmelCase						,      padding='''max_length'''						,      max_length=_UpperCAmelCase						,      truncation=_UpperCAmelCase						,      return_tensors='''np'''  )
									self.assertIn('''attention_mask'''						,      _UpperCAmelCase  )
									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 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									from datasets import load_dataset

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

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







					def 			_lowerCamelCase		(    self  ):
									# fmt: off
									__a			:    List[Any]      							=	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

									__a			:    Union[str, Any]      							=	self._load_datasamples(1  )
									__a			:    str      							=	SpeechTaFeatureExtractor()
									__a			:    List[str]      							=	feature_extractor(_UpperCAmelCase						,      return_tensors='''pt'''  ).input_values
									self.assertEquals(input_values.shape						,      (1, 93680)  )
									self.assertTrue(torch.allclose(input_values[0, :30]						,      _UpperCAmelCase						,      atol=1e-6  )  )







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

									__a			:    Dict      							=	self._load_datasamples(1  )
									__a			:    Any      							=	SpeechTaFeatureExtractor()
									__a			:    List[Any]      							=	feature_extractor(audio_target=_UpperCAmelCase						,      return_tensors='''pt'''  ).input_values
									self.assertEquals(input_values.shape						,      (1, 366, 80)  )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]						,      _UpperCAmelCase						,      atol=1e-4  )  )






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						List[str]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class       a							(   a__  ):
 snake_case__             =       '''glpn'''



 def __init__(     self				,       _snake_case=3				,       _snake_case=4				,       _snake_case=[2, 2, 2, 2]				,       _snake_case=[8, 4, 2, 1]				,       _snake_case=[32, 64, 1_60, 2_56]				,       _snake_case=[7, 3, 3, 3]				,       _snake_case=[4, 2, 2, 2]				,       _snake_case=[1, 2, 5, 8]				,       _snake_case=[4, 4, 4, 4]				,       _snake_case="gelu"				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=0.1				,       _snake_case=1E-6				,       _snake_case=64				,       _snake_case=10				,       _snake_case=-1				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )

  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= num_encoder_blocks
  lowerCAmelCase    				= depths
  lowerCAmelCase    				= sr_ratios
  lowerCAmelCase    				= hidden_sizes
  lowerCAmelCase    				= patch_sizes
  lowerCAmelCase    				= strides
  lowerCAmelCase    				= mlp_ratios
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= drop_path_rate
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= decoder_hidden_size
  lowerCAmelCase    				= max_depth
  lowerCAmelCase    				= head_in_index





