



'''simple docstring'''
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 							lowercase__     (							lowercase       ):
	# to overwrite at feature extractactor specific tests
	lowercase__      =      None
	lowercase__      =      None





	@property
	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							return self.feat_extract_tester.prepare_feat_extract_dict()





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       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      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase :		List[Any]			    =       self.feature_extraction_class(**self.feat_extract_dict			)
							_UpperCamelCase :		Optional[Any]			    =       feat_extract.model_input_names[0]

							_UpperCamelCase :		Tuple			    =       BatchFeature({input_name: speech_inputs}			)

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

							_UpperCamelCase :		Any			    =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       BatchFeature({input_name: speech_inputs}			,tensor_type='np'			)

							_UpperCamelCase :		str			    =       processed_features[input_name]

							if len(batch_features_input.shape			) < 3:
													_UpperCamelCase :		int			    =       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      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       self.feature_extraction_class(**self.feat_extract_dict			)
							_UpperCamelCase :		Optional[int]			    =       feat_extract.model_input_names[0]

							_UpperCamelCase :		List[Any]			    =       BatchFeature({input_name: speech_inputs}			,tensor_type='pt'			)

							_UpperCamelCase :		List[Any]			    =       processed_features[input_name]

							if len(batch_features_input.shape			) < 3:
													_UpperCamelCase :		Tuple			    =       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      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase__			)
							_UpperCamelCase :		str			    =       self.feature_extraction_class(**self.feat_extract_dict			)
							_UpperCamelCase :		List[Any]			    =       feat_extract.model_input_names[0]

							_UpperCamelCase :		List[Any]			    =       BatchFeature({input_name: speech_inputs}			,tensor_type='tf'			)

							_UpperCamelCase :		Tuple			    =       processed_features[input_name]

							if len(batch_features_input.shape			) < 3:
													_UpperCamelCase :		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.feature_size)			)





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    Union[str, Any]=False			):







							'''simple docstring'''
							def _inputs_have_equal_length(lowerCamelCase__ :    Any			):
													_UpperCamelCase :		str			    =       len(input[0]			)
													for input_slice in input[1:]:
																			if len(lowerCamelCase__			) != length:
																									return False
													return True

							def _inputs_are_equal(lowerCamelCase__ :    Union[str, Any]			,lowerCamelCase__ :    List[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

							_UpperCamelCase :		Optional[int]			    =       self.feature_extraction_class(**self.feat_extract_dict			)
							_UpperCamelCase :		Any			    =       self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       feat_extract.model_input_names[0]

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

							_UpperCamelCase :		Any			    =       self.feat_extract_tester.seq_length_diff
							_UpperCamelCase :		Optional[int]			    =       self.feat_extract_tester.max_seq_length + pad_diff
							_UpperCamelCase :		Tuple			    =       self.feat_extract_tester.min_seq_length
							_UpperCamelCase :		List[str]			    =       self.feat_extract_tester.batch_size
							_UpperCamelCase :		str			    =       self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							_UpperCamelCase :		Dict			    =       feat_extract.pad(lowerCamelCase__			,padding=lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       input_a[input_name]

							_UpperCamelCase :		List[str]			    =       feat_extract.pad(lowerCamelCase__			,padding='longest'			)
							_UpperCamelCase :		List[Any]			    =       input_a[input_name]

							_UpperCamelCase :		int			    =       feat_extract.pad(lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[-1]			)			)
							_UpperCamelCase :		Dict			    =       input_a[input_name]

							_UpperCamelCase :		List[str]			    =       feat_extract.pad(lowerCamelCase__			,padding='longest'			,return_tensors='np'			)
							_UpperCamelCase :		Dict			    =       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]

							_UpperCamelCase :		Dict			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=lowerCamelCase__			,return_tensors='np'			)
							_UpperCamelCase :		Any			    =       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
							_UpperCamelCase :		str			    =       feat_extract.pad(lowerCamelCase__			,pad_to_multiple_of=10			)
							_UpperCamelCase :		Dict			    =       input_a[input_name]

							_UpperCamelCase :		int			    =       feat_extract.pad(lowerCamelCase__			,padding='longest'			,pad_to_multiple_of=10			)
							_UpperCamelCase :		Dict			    =       input_a[input_name]

							_UpperCamelCase :		Union[str, Any]			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,pad_to_multiple_of=10			,max_length=lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       input_a[input_name]

							_UpperCamelCase :		Optional[int]			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,pad_to_multiple_of=10			,max_length=lowerCamelCase__			,return_tensors='np'			,)
							_UpperCamelCase :		Optional[Any]			    =       input_a[input_name]

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

							_UpperCamelCase :		Dict			    =       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
							_UpperCamelCase :		int			    =       (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      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    Union[str, Any]=False			):







							'''simple docstring'''
							def _inputs_have_equal_length(lowerCamelCase__ :    Optional[int]			):
													_UpperCamelCase :		str			    =       len(input[0]			)
													for input_slice in input[1:]:
																			if len(lowerCamelCase__			) != length:
																									return False
													return True

							def _inputs_are_equal(lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    int			):
													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

							_UpperCamelCase :		str			    =       self.feature_extraction_class(**self.feat_extract_dict			)
							_UpperCamelCase :		Tuple			    =       self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase__			)
							_UpperCamelCase :		Any			    =       feat_extract.model_input_names[0]

							_UpperCamelCase :		Union[str, Any]			    =       BatchFeature({input_name: speech_inputs}			)

							# truncate to smallest
							_UpperCamelCase :		Any			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[0]			)			,truncation=lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       input_a[input_name]

							_UpperCamelCase :		str			    =       feat_extract.pad(lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[0]			)			)
							_UpperCamelCase :		Optional[Any]			    =       input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(lowerCamelCase__			)			)
							self.assertFalse(_inputs_have_equal_length(lowerCamelCase__			)			)

							# truncate to smallest with np
							_UpperCamelCase :		List[str]			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[0]			)			,return_tensors='np'			,truncation=lowerCamelCase__			,)
							_UpperCamelCase :		int			    =       input_a[input_name]

							_UpperCamelCase :		int			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[0]			)			,return_tensors='np'			)
							_UpperCamelCase :		List[Any]			    =       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
							_UpperCamelCase :		Optional[Any]			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[1]			)			,truncation=lowerCamelCase__			,return_tensors='np'			,)
							_UpperCamelCase :		int			    =       input_a[input_name]

							_UpperCamelCase :		List[Any]			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[1]			)			,truncation=lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       input_a[input_name]

							_UpperCamelCase :		Optional[int]			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[1]			)			,return_tensors='np'			)
							_UpperCamelCase :		Tuple			    =       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
							_UpperCamelCase :		Optional[Any]			    =       12
							_UpperCamelCase :		str			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[0]			)			,pad_to_multiple_of=lowerCamelCase__			,truncation=lowerCamelCase__			,)
							_UpperCamelCase :		Tuple			    =       input_a[input_name]

							_UpperCamelCase :		Dict			    =       feat_extract.pad(
							    lowerCamelCase__			,padding='max_length'			,max_length=len(speech_inputs[0]			)			,pad_to_multiple_of=lowerCamelCase__			,)
							_UpperCamelCase :		str			    =       input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							_UpperCamelCase :		Optional[Any]			    =       len(speech_inputs[0]			)
							if expected_length % pad_to_multiple_of != 0:
													_UpperCamelCase :		Any			    =       ((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      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							self._check_padding(numpify=lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							self._check_padding(numpify=lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							self._check_truncation(numpify=lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							self._check_truncation(numpify=lowerCamelCase__			)





	@require_torch
	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self.feature_extraction_class(**self.feat_extract_dict			)
							_UpperCamelCase :		Optional[int]			    =       self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase :		List[str]			    =       feat_extract.model_input_names[0]

							_UpperCamelCase :		Optional[Any]			    =       BatchFeature({input_name: speech_inputs}			)

							_UpperCamelCase :		Tuple			    =       feat_extract.pad(lowerCamelCase__			,padding='longest'			,return_tensors='np'			)[input_name]
							_UpperCamelCase :		Union[str, Any]			    =       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      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       self.feature_extraction_class(**self.feat_extract_dict			)
							_UpperCamelCase :		Optional[Any]			    =       self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase :		Optional[Any]			    =       feat_extract.model_input_names[0]

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

							_UpperCamelCase :		Optional[int]			    =       feat_extract.pad(lowerCamelCase__			,padding='longest'			,return_tensors='np'			)[input_name]
							_UpperCamelCase :		Union[str, Any]			    =       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      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       self.feat_extract_dict
							_UpperCamelCase :		Dict			    =       True
							_UpperCamelCase :		List[Any]			    =       self.feature_extraction_class(**lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase :		Tuple			    =       [len(lowerCamelCase__			) for x in speech_inputs]
							_UpperCamelCase :		Dict			    =       feat_extract.model_input_names[0]

							_UpperCamelCase :		List[Any]			    =       BatchFeature({input_name: speech_inputs}			)

							_UpperCamelCase :		List[Any]			    =       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      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       self.feat_extract_dict
							_UpperCamelCase :		Union[str, Any]			    =       True
							_UpperCamelCase :		str			    =       self.feature_extraction_class(**lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase :		Any			    =       [len(lowerCamelCase__			) for x in speech_inputs]
							_UpperCamelCase :		Union[str, Any]			    =       feat_extract.model_input_names[0]

							_UpperCamelCase :		int			    =       BatchFeature({input_name: speech_inputs}			)
							_UpperCamelCase :		Any			    =       min(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       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]			)







'''simple docstring'''
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Initialise PyTorch model
						_UpperCamelCase :		Any			    =       LxmertConfig.from_json_file(UpperCAmelCase_							)
						print(f'Building PyTorch model from configuration: {config}'							)
						_UpperCamelCase :		int			    =       LxmertForPreTraining(UpperCAmelCase_							)

						# Load weights from tf checkpoint
						load_tf_weights_in_lxmert(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)

						# Save pytorch-model
						print(f'Save PyTorch model to {pytorch_dump_path}'							)
						torch.save(model.state_dict()  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					snake_case_			:     int  	=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_			:     List[str]  	=  parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


