





import argparse

import torch

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


logging.set_verbosity_info()






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  LxmertConfig.from_json_file(_UpperCamelCase			)
				print(f'''Building PyTorch model from configuration: {config}'''			)
				snake_case_						:   Optional[Any]           =  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__":
	lowerCAmelCase_    =    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.'''
	)
	lowerCAmelCase_    =    parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class  __lowerCAmelCase	(      unittest.TestCase,					_a					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  load_tool('''text-to-speech'''   )
							self.tool.setup()






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Optional[int]           =  self.tool('''hey'''   )
							snake_case_						:   Union[str, Any]           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   Any           =  self.tool('''hey'''   )
							snake_case_						:   str           =  result.to_raw()
							self.assertTrue(
							    torch.allclose(
							        resulting_tensor[:3] ,							torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]   ) ,							)   )
