
from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  def __init__( self			: List[Any]			,							UpperCamelCase__			: NestedDataStructureLike[PathLike]			,							UpperCamelCase__			: Optional[NamedSplit] = None			,							UpperCamelCase__			: Optional[Features] = None			,							UpperCamelCase__			: str = None			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: Optional[int] = None			,							**UpperCamelCase__			: List[str]			,							)			->	List[Any]:







    """simple docstring"""






    super().__init__(
        UpperCamelCase__			,							split=UpperCamelCase__			,							features=UpperCamelCase__			,							cache_dir=UpperCamelCase__			,							keep_in_memory=UpperCamelCase__			,							streaming=UpperCamelCase__			,							num_proc=UpperCamelCase__			,							**UpperCamelCase__			,							)
    __magic_name__				     =       path_or_paths if isinstance(UpperCamelCase__			,							UpperCamelCase__    ) else {self.split: path_or_paths}
    __magic_name__				     =       Text(
        cache_dir=UpperCamelCase__			,							data_files=UpperCamelCase__			,							features=UpperCamelCase__			,							**UpperCamelCase__			,							)





  def 							_lowercase							( self			: List[Any]    )			->	Tuple:







    """simple docstring"""






    if self.streaming:
      __magic_name__				     =       self.builder.as_streaming_dataset(split=self.split    )
    # Build regular (map-style) dataset
    else:
      __magic_name__				     =       None
      __magic_name__				     =       None
      __magic_name__				     =       None
      __magic_name__				     =       None

      self.builder.download_and_prepare(
          download_config=UpperCamelCase__			,							download_mode=UpperCamelCase__			,							verification_mode=UpperCamelCase__			,							base_path=UpperCamelCase__			,							num_proc=self.num_proc			,							)
      __magic_name__				     =       self.builder.as_dataset(
          split=self.split			,							verification_mode=UpperCamelCase__			,							in_memory=self.keep_in_memory    )
    return dataset






"""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	):







						'''simple docstring'''


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