




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor


class 				__UpperCamelCase      (			unittest.TestCase	):





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=7							,			UpperCAmelCase_=3							,			UpperCAmelCase_=18							,			UpperCAmelCase_=30							,			UpperCAmelCase_=4_00							,			UpperCAmelCase_=True							,			UpperCAmelCase_=None							,			UpperCAmelCase_=True							,			UpperCAmelCase_=None							,			):
											lowerCAmelCase =		size if size is not None else {'''shortest_edge''': 20}
											lowerCAmelCase =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
											lowerCAmelCase =		parent
											lowerCAmelCase =		batch_size
											lowerCAmelCase =		num_channels
											lowerCAmelCase =		image_size
											lowerCAmelCase =		min_resolution
											lowerCAmelCase =		max_resolution
											lowerCAmelCase =		do_resize
											lowerCAmelCase =		size
											lowerCAmelCase =		do_center_crop
											lowerCAmelCase =		crop_size






				def    __snake_case		(			self    ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}




@require_torch
@require_vision
class 				__UpperCamelCase      (			a__  ,       unittest.TestCase	):





				'''simple docstring'''



				__a				:			Tuple					    =MobileNetVaImageProcessor if is_vision_available() else None



				def    __snake_case		(			self    ):
											lowerCAmelCase =		MobileNetVaImageProcessingTester(self    )



				@property
				def    __snake_case		(			self    ):
											return self.image_processor_tester.prepare_image_processor_dict()



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(__UpperCAmelCase							,			'''do_resize'''    )    )
											self.assertTrue(hasattr(__UpperCAmelCase							,			'''size'''    )    )
											self.assertTrue(hasattr(__UpperCAmelCase							,			'''do_center_crop'''    )    )
											self.assertTrue(hasattr(__UpperCAmelCase							,			'''center_crop'''    )    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.image_processing_class.from_dict(self.image_processor_dict    )
											self.assertEqual(image_processor.size							,			{'''shortest_edge''': 20}    )
											self.assertEqual(image_processor.crop_size							,			{'''height''': 18, '''width''': 18}    )

											lowerCAmelCase =		self.image_processing_class.from_dict(self.image_processor_dict							,			size=42							,			crop_size=84    )
											self.assertEqual(image_processor.size							,			{'''shortest_edge''': 42}    )
											self.assertEqual(image_processor.crop_size							,			{'''height''': 84, '''width''': 84}    )



				def    __snake_case		(			self    ):
											pass



				def    __snake_case		(			self    ):
											# Initialize image_processing
											lowerCAmelCase =		self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											lowerCAmelCase =		prepare_image_inputs(self.image_processor_tester							,			equal_resolution=__UpperCAmelCase    )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCAmelCase							,			Image.Image    )

											# Test not batched input
											lowerCAmelCase =		image_processing(image_inputs[0]							,			return_tensors='''pt'''    ).pixel_values
											self.assertEqual(
											    encoded_images.shape							,			(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,			)

											# Test batched
											lowerCAmelCase =		image_processing(__UpperCAmelCase							,			return_tensors='''pt'''    ).pixel_values
											self.assertEqual(
											    encoded_images.shape							,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,			)



				def    __snake_case		(			self    ):
											# Initialize image_processing
											lowerCAmelCase =		self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											lowerCAmelCase =		prepare_image_inputs(self.image_processor_tester							,			equal_resolution=__UpperCAmelCase							,			numpify=__UpperCAmelCase    )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCAmelCase							,			np.ndarray    )

											# Test not batched input
											lowerCAmelCase =		image_processing(image_inputs[0]							,			return_tensors='''pt'''    ).pixel_values
											self.assertEqual(
											    encoded_images.shape							,			(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,			)

											# Test batched
											lowerCAmelCase =		image_processing(__UpperCAmelCase							,			return_tensors='''pt'''    ).pixel_values
											self.assertEqual(
											    encoded_images.shape							,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,			)






				def    __snake_case		(			self    ):
											# Initialize image_processing
											lowerCAmelCase =		self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											lowerCAmelCase =		prepare_image_inputs(self.image_processor_tester							,			equal_resolution=__UpperCAmelCase							,			torchify=__UpperCAmelCase    )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCAmelCase							,			torch.Tensor    )

											# Test not batched input
											lowerCAmelCase =		image_processing(image_inputs[0]							,			return_tensors='''pt'''    ).pixel_values
											self.assertEqual(
											    encoded_images.shape							,			(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,			)

											# Test batched
											lowerCAmelCase =		image_processing(__UpperCAmelCase							,			return_tensors='''pt'''    ).pixel_values
											self.assertEqual(
											    encoded_images.shape							,			(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,			)







"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

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


if __name__ == "__main__":
   __snake_case												=							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(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This 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												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)