




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







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


if __name__ == "__main__":
  _snake_case            = 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")
  _snake_case            = parser.parse_args()
  convert_tf_gptsan_to_pt(args)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case            = {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  _snake_case            = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

