
"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class      lowerCAmelCase_						(_lowerCamelCase  ):


				"""simple docstring"""



				__UpperCamelCase				:						Union[str, Any] 	=       42
				__UpperCamelCase				:						int 	=       42



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		List[str]:



											"""simple docstring"""




											super().__init__()

											SCREAMING_SNAKE_CASE__						:	List[str]									=       PNDMScheduler.from_config(scheduler.config   )

											self.register_modules(unet=__UpperCamelCase       ,	scheduler=__UpperCamelCase   )



				@torch.no_grad()
				def __call__(self       ,	SCREAMING_SNAKE_CASE__ = 1       ,	SCREAMING_SNAKE_CASE__ = 50       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = "pil"       ,	SCREAMING_SNAKE_CASE__ = True       ,	**SCREAMING_SNAKE_CASE__       ,	) ->		Union[ImagePipelineOutput, Tuple]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,	generator=__UpperCamelCase       ,	device=self.device       ,	)

											self.scheduler.set_timesteps(__UpperCamelCase   )
											for t in self.progress_bar(self.scheduler.timesteps   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       self.unet(__UpperCamelCase       ,	__UpperCamelCase   ).sample

																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       self.scheduler.step(__UpperCamelCase       ,	__UpperCamelCase       ,	__UpperCamelCase   ).prev_sample

											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       (image / 2 + 0.5).clamp(0       ,	1   )
											SCREAMING_SNAKE_CASE__						:	int									=       image.cpu().permute(0       ,	2       ,	3       ,	1   ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE__						:	str									=       self.numpy_to_pil(__UpperCamelCase   )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=__UpperCamelCase   )






"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       FNetConfig.from_json_file(_snake_case						)
							print(f'''Building PyTorch model from configuration: {config}'''						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       FNetForPreTraining(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	Tuple									=       restore_checkpoint(_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       checkpoint_dict["""target"""]

							# Embeddings
							# Position IDs
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fnet_pretraining_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Any									=       state_dict["""fnet.embeddings.position_ids"""]
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""fnet.embeddings.position_ids""": position_ids}
							# Embedding Layers
							SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Any									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]						)

							# Encoder Layers
							for layer in range(config.num_hidden_layers						):
														SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]						)

							# Pooler Layers
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]						)

							# Masked LM Layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_output"""]["""output_bias"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]						)

							# Seq Relationship Layers
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_kernel"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_bias"""]						)

							# Load State Dict
							fnet_pretraining_model.load_state_dict(_snake_case						)

							# Save PreTrained
							print(f'''Saving pretrained model to {save_path}'''						)
							fnet_pretraining_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__				:   Optional[int]   							=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--fnet_config_file',
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        'The config json file corresponding to the pre-trained FNet model. \n'
					        'This specifies the model architecture.'
					    ),
					)
					parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




