
import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
	import jax







@require_flax
class 			A_  (     lowercase_ ,      unittest.TestCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Optional[int]									=			FlaxAutoencoderKL





	@property
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       4
		lowercase	    =       3
		lowercase	    =       (32, 32)

		lowercase	    =       jax.random.PRNGKey(0			)
		lowercase	    =       jax.random.uniform(snake_case						,       ((batch_size, num_channels) + sizes)			)

		return {"sample": image, "prng_key": prng_key}





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       {
		    'block_out_channels': [32, 64],
		    'in_channels': 3,
		    'out_channels': 3,
		    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
		    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
		    'latent_channels': 4,
		}
		lowercase	    =       self.dummy_input
		return init_dict, inputs_dict




import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       BertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case_     							=       BertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

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


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, 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(
					    """--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."""
					)
					_UpperCAmelCase						:  Any									=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




