





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



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


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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase	:					str			  =					{"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""EncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""TFEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Union[str, Any]			  =					["""FlaxEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __lowerCamelCase	:					Dict			  =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
