

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

import numpy as np
import tensorflow as tf
import torch




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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=  {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SEWForCTC""",
        """SEWForSequenceClassification""",
        """SEWModel""",
        """SEWPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

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

