
import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()






def        lowerCamelCase__    (    __lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  Any    ):




			"""simple docstring"""


			lowerCAmelCase_      =     ElectraConfig.from_json_file(__lowerCAmelCase    )
			print(F"""Building PyTorch model from configuration: {config}"""    )

			if discriminator_or_generator == "discriminator":
						lowerCAmelCase_      =     ElectraForPreTraining(__lowerCAmelCase    )
			elif discriminator_or_generator == "generator":
						lowerCAmelCase_      =     ElectraForMaskedLM(__lowerCAmelCase    )
			else:
						raise ValueError("The discriminator_or_generator argument should be either 'discriminator' or 'generator'"    )

			# Load weights from tf checkpoint
			load_tf_weights_in_electra(
			    __lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							discriminator_or_generator=__lowerCAmelCase    )

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


if __name__ == "__main__":
			_A       =					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."
			)
			parser.add_argument(
			    "--discriminator_or_generator",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or "
			        "'generator'."
			    ),
			)
			_A       =					parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
			)





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest





class      _lowerCAmelCase (     __a	):
				_lowercase      		=(VQDiffusionScheduler,)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     {
							    "num_vec_classes": 4_097,
							    "num_train_timesteps": 100,
							}

							config.update(**_UpperCamelCase					)
							return config




				def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     4
							lowerCAmelCase_      =     8
							lowerCAmelCase_      =     8

							lowerCAmelCase_      =     torch.randint(0    ,    _UpperCamelCase    ,    (batch_size, height * width)					)

							return sample




				@property
				def 					__a						( self					)    ->				List[str]:
							assert False




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							def model(_UpperCamelCase    ,    _UpperCamelCase    ,    *_UpperCamelCase					):
										lowerCAmelCase_     ,  lowerCAmelCase_      =     sample.shape
										lowerCAmelCase_      =     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
										lowerCAmelCase_      =     F.log_softmax(logits.double()    ,    dim=1					).float()
										return return_value

							return model




				def 					__a						( self					)    ->				str:
							for timesteps in [2, 5, 100, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




				def 					__a						( self					)    ->				Dict:
							for num_vec_classes in [5, 100, 1_000, 4_000]:
										self.check_over_configs(num_vec_classes=_UpperCamelCase					)




				def 					__a						( self					)    ->				Tuple:
							for t in [0, 50, 99]:
										self.check_over_forward(time_step=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[str]:
							pass



