


"""simple docstring"""


import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Tuple:
				# Construct model
				if openai_config_file == "":
								_A					  =			OpenAIGPTConfig()
				else:
								_A					  =			OpenAIGPTConfig.from_json_file(UpperCamelCase__   )
				_A					  =			OpenAIGPTModel(UpperCamelCase__   )

				# Load weights from numpy
				load_tf_weights_in_openai_gpt(UpperCamelCase__     ,			UpperCamelCase__     ,			UpperCamelCase__   )

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


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--openai_checkpoint_folder_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path to the TensorFlow checkpoint path.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--openai_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
		        "This specifies the model architecture."
		    ),
		)
		a_       							=    parser.parse_args()
		convert_openai_checkpoint_to_pytorch(
		    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
		)



"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def      a__ (				__lowercase   )	->	List[Any]:
				_A					  =			os.path.join(args.tf_model_dir     ,			"parameters.json"   )
				_A					  =			json.loads(open(__lowercase   ).read()   )
				if not params:
								raise ValueError(
								    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""   )
				if not args.output.endswith(".pt"   ):
								_A					  =			args.output + ".pt"
				_A					  =			OrderedDict()
				with tf.device("/CPU:0"   ):
								_A					  =			tf.train.load_checkpoint(args.tf_model_dir   )
								_A					  =			reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												_A					  =			reader.get_tensor(__lowercase   ).astype(np.floataa   )
												if key_name.endswith("/adam_m"   ) or key_name.endswith("/adam_v"   ):
																continue
												if key_name.startswith("pasts/"   ):
																if key_name.startswith("pasts/mlp"   ):
																				_A					  =			int(key_name[9]   )
																elif key_name.startswith("pasts/out"   ):
																				_A					  =			8
																_A					  =			"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/moe"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/switch_gating/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/softmlp/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/wo/kernel"   ) or key_name.endswith("/wi/kernel"   ):
																				_A					  =			key_name[-9:-7]
																				for i in range(16   ):
																								_A					  =			"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																								_A					  =			(
																								    vnp[i].transpose([1, 0]   ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/mlp"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/p1/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wi.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p1/bias"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wi.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p2/kernel"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wo.weight" % player
																				_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/p2/bias"   ):
																				_A					  =			"model.blocks.%d.feed_forward.mlp.wo.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/ln"   ):
																_A					  =			int(key_name[8:].split("/"   )[0]   )
																if key_name.endswith("/b"   ):
																				_A					  =			"model.blocks.%d.feed_forward.norm.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/g"   ):
																				_A					  =			"model.blocks.%d.feed_forward.norm.weight" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/att"   ):
																_A					  =			int(key_name[9:].split("/"   )[0]   )
																if key_name.endswith("/qkv/kernel"   ):
																				_A					  =			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				_A					  =			state[:, 0, :, :]
																				_A					  =			state[:, 1, :, :]
																				_A					  =			state[:, 2, :, :]
																				_A					  =			(
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]   )
																				    .transpose([1, 0]   )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			(
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]   )
																				    .transpose([1, 0]   )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			(
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]   )
																				    .transpose([1, 0]   )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																				_A					  =			"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																				_A					  =			"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/o/kernel"   ):
																				_A					  =			"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
																				_A					  =			(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]   ).transpose([1, 0]   ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/an"   ):
																_A					  =			int(key_name[8:].split("/"   )[0]   )
																if key_name.endswith("/b"   ):
																				_A					  =			"model.blocks.%d.self_attn.norm.bias" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
																elif key_name.endswith("/g"   ):
																				_A					  =			"model.blocks.%d.self_attn.norm.weight" % player
																				_A					  =			vnp.copy()  # same because it is one dimensional
																				_A					  =			torch.tensor(__lowercase   )
												elif (
												    key_name.startswith("model/wte"   )
												    or key_name.startswith("model/wpe"   )
												    or key_name.startswith("model/ete"   )
												):
																_A					  =			{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
																    key_name[-3:]
																]
																_A					  =			"model.%s.weight" % nlayer
																_A					  =			vnp.copy()  # same in embedded
																_A					  =			torch.tensor(__lowercase   )
																if key_name.startswith("model/wte"   ):
																				_A					  =			"lm_head.weight"
																				_A					  =			vnp.copy()  # same in embedded
																				_A					  =			torch.tensor(__lowercase   )
												elif key_name.startswith("model/wob"   ):
																_A					  =			"final_logits_bias"
																_A					  =			vnp.copy()  # same in embedded
																_A					  =			state.reshape((1, -1)   )
																_A					  =			torch.tensor(__lowercase   )
												elif key_name == "model/dense/kernel":
																_A					  =			"model.last_project.weight"
																_A					  =			vnp.transpose([1, 0]   ).copy()  # Mesh-Tensorflow is a diagonal matrix
																_A					  =			torch.tensor(__lowercase   )
												elif key_name == "model/dense_1/bias":
																_A					  =			"model.last_project.bias"
																_A					  =			vnp.copy()  # same because it is one dimensional
																_A					  =			torch.tensor(__lowercase   )
				torch.save(__lowercase     ,			args.output   )


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser(
		    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
		parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
		a_       							=    parser.parse_args()
		convert_tf_gptsan_to_pt(args)