



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 PoolFormerImageProcessor

class     _A       (	unittest.TestCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple=7     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3     ,		__SCREAMING_SNAKE_CASE							:     str=30     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=400     ,		__SCREAMING_SNAKE_CASE							:     str=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=None     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.9     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     List[str]=[0.5, 0.5, 0.5]     ,		__SCREAMING_SNAKE_CASE							:     Dict=[0.5, 0.5, 0.5]     ,		):


           '''simple docstring'''

           __a						       =				size if size is not None else {'''shortest_edge''': 30}
           __a						       =				crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
           __a						       =				parent
           __a						       =				batch_size
           __a						       =				num_channels
           __a						       =				min_resolution
           __a						       =				max_resolution
           __a						       =				do_resize_and_center_crop
           __a						       =				size
           __a						       =				crop_pct
           __a						       =				crop_size
           __a						       =				do_normalize
           __a						       =				image_mean
           __a						       =				image_std





       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           return {
               "size": self.size,
               "do_resize_and_center_crop": self.do_resize_and_center_crop,
               "crop_pct": self.crop_pct,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
           }


@require_torch
@require_vision
class     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Union[str, Any]											=						PoolFormerImageProcessor if is_vision_available() else None



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				PoolFormerImageProcessingTester(self)



       @property
       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           __a						       =				self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,		'''do_resize_and_center_crop'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,		'''size'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,		'''crop_pct'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,		'''do_normalize'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,		'''image_mean'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,		'''image_std'''))



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size     ,		{'''shortest_edge''': 30})
           self.assertEqual(image_processor.crop_size     ,		{'''height''': 30, '''width''': 30})

           __a						       =				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 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           __a						       =				prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		Image.Image)

           # Test not batched input
           __a						       =				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
           __a						       =				image_processing(__SCREAMING_SNAKE_CASE     ,		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 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           __a						       =				prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=__SCREAMING_SNAKE_CASE     ,		numpify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		np.ndarray)

           # Test not batched input
           __a						       =				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
           __a						       =				image_processing(__SCREAMING_SNAKE_CASE     ,		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 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a						       =				prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=__SCREAMING_SNAKE_CASE     ,		torchify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,		torch.Tensor)

           # Test not batched input
           __a						       =				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
           __a						       =				image_processing(__SCREAMING_SNAKE_CASE     ,		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 RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = RemBertConfig.from_json_file(lowerCAmelCase_		)
       print("""Building PyTorch model from configuration: {}""".format(str(lowerCAmelCase_		)		)		)
       _UpperCAmelCase  :						Any			     = RemBertModel(lowerCAmelCase_		)

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

       # Save pytorch-model
       print("""Save PyTorch model to {}""".format(lowerCAmelCase_		)		)
       torch.save(model.state_dict()       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[Any]						 =	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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   A_							: Any						 =	parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



