





'''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 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_					)
		print(f'''Building PyTorch model from configuration: {config}'''					)
		SCREAMING_SNAKE_CASE				:							str						=	FNetForPreTraining(SCREAMING_SNAKE_CASE_					)

		SCREAMING_SNAKE_CASE				:							Optional[int]						=	restore_checkpoint(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	checkpoint_dict["target"]

		# Embeddings
		# Position IDs
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	fnet_pretraining_model.state_dict()

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

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

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

				SCREAMING_SNAKE_CASE				:							str						=	torch.tensor(
				    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]					)
				SCREAMING_SNAKE_CASE				:							str						=	torch.tensor(
				    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]					)

		# Pooler Layers
		SCREAMING_SNAKE_CASE				:							Tuple						=	torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]					).T
		SCREAMING_SNAKE_CASE				:							int						=	torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]					)

		# Masked LM Layers
		SCREAMING_SNAKE_CASE				:							str						=	torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]					).T
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]					)
		SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]					)
		SCREAMING_SNAKE_CASE				:							Union[str, 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				:							List[Any]						=	torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]					)
		SCREAMING_SNAKE_CASE				:							int						=	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				:							str						=	torch.tensor(
		    pretrained_model_params["""classification"""]["""output_bias"""]					)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_					)

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


if __name__ == "__main__":
							__UpperCAmelCase         =   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         =   parser.parse_args()
							convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)










'''simple docstring'''




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE				:							Any						=	ImageGPTConfig(n_embd=lowerCamelCase_	,	n_layer=lowerCamelCase_	,	n_head=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							str						=	ImageGPTForCausalLM(lowerCamelCase_					)

		# Load weights from numpy
		load_tf_weights_in_imagegpt(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					)

		# Save pytorch-model
		SCREAMING_SNAKE_CASE				:							Any						=	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''					)
		torch.save(model.state_dict()	,	lowerCamelCase_					)
		print(f'''Save configuration file to {pytorch_config_dump_path}'''					)
		with open(lowerCamelCase_	,	"""w"""	,	encoding="""utf-8"""					) as f:
				f.write(config.to_json_string()					)


if __name__ == "__main__":
							__UpperCAmelCase         =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--imagegpt_checkpoint_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the TensorFlow checkpoint path.""",
							)
							parser.add_argument(
							    """--model_size""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__UpperCAmelCase         =   parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)



