
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 				a__   (   a   :				str		):

		"""simple docstring"""







		_snake_case     :						Optional[Any]						=   os.path.join(args.tf_model_dir       ,     "parameters.json"		)
		_snake_case     :						Union[str, Any]						=   json.loads(open(a		).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"		):
				_snake_case     :						List[str]						=   args.output + ".pt"
		_snake_case     :						Tuple						=   OrderedDict()
		with tf.device("/CPU:0"		):
				_snake_case     :						int						=   tf.train.load_checkpoint(args.tf_model_dir		)
				_snake_case     :						int						=   reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						_snake_case     :						Tuple						=   reader.get_tensor(a		).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"		):
										_snake_case     :						Any						=   int(key_name[9]		)
								elif key_name.startswith("pasts/out"		):
										_snake_case     :						List[Any]						=   8
								_snake_case     :						Optional[Any]						=   "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								_snake_case     :						Union[str, Any]						=   vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
								_snake_case     :						List[str]						=   torch.tensor(a		)
						elif key_name.startswith("model/moe"		):
								_snake_case     :						List[str]						=   int(key_name[9:].split("/"		)[0]		)
								if key_name.endswith("/switch_gating/kernel"		):
										_snake_case     :						Optional[Any]						=   "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
										_snake_case     :						Optional[Any]						=   vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
										_snake_case     :						List[str]						=   torch.tensor(a		)
								elif key_name.endswith("/softmlp/kernel"		):
										_snake_case     :						Tuple						=   "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
										_snake_case     :						Optional[int]						=   vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
										_snake_case     :						List[str]						=   torch.tensor(a		)
								elif key_name.endswith("/wo/kernel"		) or key_name.endswith("/wi/kernel"		):
										_snake_case     :						Any						=   key_name[-9:-7]
										for i in range(16		):
												_snake_case     :						List[Any]						=   "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
												_snake_case     :						List[str]						=   (
												    vnp[i].transpose([1, 0]		).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												_snake_case     :						List[Any]						=   torch.tensor(a		)
						elif key_name.startswith("model/mlp"		):
								_snake_case     :						Tuple						=   int(key_name[9:].split("/"		)[0]		)
								if key_name.endswith("/p1/kernel"		):
										_snake_case     :						Tuple						=   "model.blocks.%d.feed_forward.mlp.wi.weight" % player
										_snake_case     :						List[str]						=   vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
										_snake_case     :						str						=   torch.tensor(a		)
								elif key_name.endswith("/p1/bias"		):
										_snake_case     :						Optional[Any]						=   "model.blocks.%d.feed_forward.mlp.wi.bias" % player
										_snake_case     :						Optional[Any]						=   vnp.copy()  # same because it is one dimensional
										_snake_case     :						Any						=   torch.tensor(a		)
								elif key_name.endswith("/p2/kernel"		):
										_snake_case     :						List[str]						=   "model.blocks.%d.feed_forward.mlp.wo.weight" % player
										_snake_case     :						int						=   vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
										_snake_case     :						Tuple						=   torch.tensor(a		)
								elif key_name.endswith("/p2/bias"		):
										_snake_case     :						Dict						=   "model.blocks.%d.feed_forward.mlp.wo.bias" % player
										_snake_case     :						List[str]						=   vnp.copy()  # same because it is one dimensional
										_snake_case     :						Any						=   torch.tensor(a		)
						elif key_name.startswith("model/ln"		):
								_snake_case     :						List[str]						=   int(key_name[8:].split("/"		)[0]		)
								if key_name.endswith("/b"		):
										_snake_case     :						Any						=   "model.blocks.%d.feed_forward.norm.bias" % player
										_snake_case     :						List[str]						=   vnp.copy()  # same because it is one dimensional
										_snake_case     :						Tuple						=   torch.tensor(a		)
								elif key_name.endswith("/g"		):
										_snake_case     :						int						=   "model.blocks.%d.feed_forward.norm.weight" % player
										_snake_case     :						Tuple						=   vnp.copy()  # same because it is one dimensional
										_snake_case     :						Dict						=   torch.tensor(a		)
						elif key_name.startswith("model/att"		):
								_snake_case     :						Optional[int]						=   int(key_name[9:].split("/"		)[0]		)
								if key_name.endswith("/qkv/kernel"		):
										_snake_case     :						Optional[int]						=   vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										_snake_case     :						Union[str, Any]						=   state[:, 0, :, :]
										_snake_case     :						Dict						=   state[:, 1, :, :]
										_snake_case     :						List[Any]						=   state[:, 2, :, :]
										_snake_case     :						Tuple						=   (
										    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
										_snake_case     :						str						=   (
										    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
										_snake_case     :						Tuple						=   (
										    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
										_snake_case     :						str						=   "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
										_snake_case     :						List[str]						=   torch.tensor(a		)
										_snake_case     :						List[Any]						=   "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
										_snake_case     :						Tuple						=   torch.tensor(a		)
										_snake_case     :						List[str]						=   "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
										_snake_case     :						Any						=   torch.tensor(a		)
								elif key_name.endswith("/o/kernel"		):
										_snake_case     :						Optional[Any]						=   "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
										_snake_case     :						Dict						=   (
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]		).transpose([1, 0]		).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										_snake_case     :						Tuple						=   torch.tensor(a		)
						elif key_name.startswith("model/an"		):
								_snake_case     :						Tuple						=   int(key_name[8:].split("/"		)[0]		)
								if key_name.endswith("/b"		):
										_snake_case     :						Tuple						=   "model.blocks.%d.self_attn.norm.bias" % player
										_snake_case     :						List[str]						=   vnp.copy()  # same because it is one dimensional
										_snake_case     :						str						=   torch.tensor(a		)
								elif key_name.endswith("/g"		):
										_snake_case     :						Union[str, Any]						=   "model.blocks.%d.self_attn.norm.weight" % player
										_snake_case     :						Union[str, Any]						=   vnp.copy()  # same because it is one dimensional
										_snake_case     :						Optional[Any]						=   torch.tensor(a		)
						elif (
						    key_name.startswith("model/wte"		)
						    or key_name.startswith("model/wpe"		)
						    or key_name.startswith("model/ete"		)
						):
								_snake_case     :						Optional[Any]						=   {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
								    key_name[-3:]
								]
								_snake_case     :						List[str]						=   "model.%s.weight" % nlayer
								_snake_case     :						List[str]						=   vnp.copy()  # same in embedded
								_snake_case     :						Any						=   torch.tensor(a		)
								if key_name.startswith("model/wte"		):
										_snake_case     :						Union[str, Any]						=   "lm_head.weight"
										_snake_case     :						Optional[Any]						=   vnp.copy()  # same in embedded
										_snake_case     :						Dict						=   torch.tensor(a		)
						elif key_name.startswith("model/wob"		):
								_snake_case     :						List[Any]						=   "final_logits_bias"
								_snake_case     :						Dict						=   vnp.copy()  # same in embedded
								_snake_case     :						Union[str, Any]						=   state.reshape((1, -1)		)
								_snake_case     :						int						=   torch.tensor(a		)
						elif key_name == "model/dense/kernel":
								_snake_case     :						Optional[Any]						=   "model.last_project.weight"
								_snake_case     :						List[Any]						=   vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
								_snake_case     :						List[Any]						=   torch.tensor(a		)
						elif key_name == "model/dense_1/bias":
								_snake_case     :						int						=   "model.last_project.bias"
								_snake_case     :						Any						=   vnp.copy()  # same because it is one dimensional
								_snake_case     :						Optional[int]						=   torch.tensor(a		)
		torch.save(a       ,     args.output		)


if __name__ == "__main__":
				_a      :			Tuple					=							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      :			str					=							parser.parse_args()
				convert_tf_gptsan_to_pt(args)



"""simple docstring"""
def 				a__   (   a   :				list       ,     a   :				int       ,     a   :				int = 0       ,     a   :				int = 0		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   right or len(a		) - 1
  if left > right:
    return -1
  elif list_data[left] == key:
    return left
  elif list_data[right] == key:
    return right
  else:
    return search(a       ,     a       ,     left + 1       ,     right - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

