






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def     snake_case   (UpperCAmelCase__    )			->							Dict:
  UpperCamelCase_:   Optional[Any]        =							os.path.join(args.tf_model_dir      ,       'parameters.json'    )
  UpperCamelCase_:   int        =							json.loads(open(UpperCAmelCase__    ).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_:   Union[str, Any]        =							args.output + '.pt'
  UpperCamelCase_:   Optional[Any]        =							OrderedDict()
  with tf.device('/CPU:0'    ):
    UpperCamelCase_:   Optional[int]        =							tf.train.load_checkpoint(args.tf_model_dir    )
    UpperCamelCase_:   str        =							reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      UpperCamelCase_:   Tuple        =							reader.get_tensor(UpperCAmelCase__    ).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_:   Tuple        =							int(key_name[9]    )
        elif key_name.startswith('pasts/out'    ):
          UpperCamelCase_:   Any        =							8
        UpperCamelCase_:   int        =							'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        UpperCamelCase_:   Optional[int]        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
        UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/moe'    ):
        UpperCamelCase_:   Dict        =							int(key_name[9:].split('/'    )[0]    )
        if key_name.endswith('/switch_gating/kernel'    ):
          UpperCamelCase_:   str        =							'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
          UpperCamelCase_:   List[str]        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Tuple        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/softmlp/kernel'    ):
          UpperCamelCase_:   Optional[int]        =							'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
          UpperCamelCase_:   int        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/wo/kernel'    ) or key_name.endswith('/wi/kernel'    ):
          UpperCamelCase_:   Tuple        =							key_name[-9:-7]
          for i in range(1_6    ):
            UpperCamelCase_:   str        =							'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
            UpperCamelCase_:   Dict        =							(
                vnp[i].transpose([1, 0]    ).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            UpperCamelCase_:   Any        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/mlp'    ):
        UpperCamelCase_:   Tuple        =							int(key_name[9:].split('/'    )[0]    )
        if key_name.endswith('/p1/kernel'    ):
          UpperCamelCase_:   List[Any]        =							'model.blocks.%d.feed_forward.mlp.wi.weight' % player
          UpperCamelCase_:   Tuple        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Tuple        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/p1/bias'    ):
          UpperCamelCase_:   List[str]        =							'model.blocks.%d.feed_forward.mlp.wi.bias' % player
          UpperCamelCase_:   List[str]        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/p2/kernel'    ):
          UpperCamelCase_:   Optional[Any]        =							'model.blocks.%d.feed_forward.mlp.wo.weight' % player
          UpperCamelCase_:   List[str]        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/p2/bias'    ):
          UpperCamelCase_:   List[Any]        =							'model.blocks.%d.feed_forward.mlp.wo.bias' % player
          UpperCamelCase_:   Dict        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/ln'    ):
        UpperCamelCase_:   Optional[Any]        =							int(key_name[8:].split('/'    )[0]    )
        if key_name.endswith('/b'    ):
          UpperCamelCase_:   Optional[Any]        =							'model.blocks.%d.feed_forward.norm.bias' % player
          UpperCamelCase_:   Optional[Any]        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/g'    ):
          UpperCamelCase_:   Optional[Any]        =							'model.blocks.%d.feed_forward.norm.weight' % player
          UpperCamelCase_:   Tuple        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/att'    ):
        UpperCamelCase_:   Dict        =							int(key_name[9:].split('/'    )[0]    )
        if key_name.endswith('/qkv/kernel'    ):
          UpperCamelCase_:   Any        =							vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          UpperCamelCase_:   List[Any]        =							state[:, 0, :, :]
          UpperCamelCase_:   Any        =							state[:, 1, :, :]
          UpperCamelCase_:   Optional[int]        =							state[:, 2, :, :]
          UpperCamelCase_:   Optional[Any]        =							(
              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_:   Dict        =							(
              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_:   Optional[int]        =							'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
          UpperCamelCase_:   List[Any]        =							torch.tensor(UpperCAmelCase__    )
          UpperCamelCase_:   str        =							'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
          UpperCamelCase_:   Any        =							torch.tensor(UpperCAmelCase__    )
          UpperCamelCase_:   Union[str, Any]        =							'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
          UpperCamelCase_:   List[Any]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/o/kernel'    ):
          UpperCamelCase_:   Optional[int]        =							'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
          UpperCamelCase_:   Optional[Any]        =							(
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]    ).transpose([1, 0]    ).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          UpperCamelCase_:   Union[str, Any]        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/an'    ):
        UpperCamelCase_:   Union[str, Any]        =							int(key_name[8:].split('/'    )[0]    )
        if key_name.endswith('/b'    ):
          UpperCamelCase_:   Optional[int]        =							'model.blocks.%d.self_attn.norm.bias' % player
          UpperCamelCase_:   int        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
        elif key_name.endswith('/g'    ):
          UpperCamelCase_:   str        =							'model.blocks.%d.self_attn.norm.weight' % player
          UpperCamelCase_:   Optional[Any]        =							vnp.copy()  # same because it is one dimensional
          UpperCamelCase_:   List[str]        =							torch.tensor(UpperCAmelCase__    )
      elif (
          key_name.startswith('model/wte'    )
          or key_name.startswith('model/wpe'    )
          or key_name.startswith('model/ete'    )
      ):
        UpperCamelCase_:   int        =							{'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
            key_name[-3:]
        ]
        UpperCamelCase_:   int        =							'model.%s.weight' % nlayer
        UpperCamelCase_:   Dict        =							vnp.copy()  # same in embedded
        UpperCamelCase_:   Dict        =							torch.tensor(UpperCAmelCase__    )
        if key_name.startswith('model/wte'    ):
          UpperCamelCase_:   Dict        =							'lm_head.weight'
          UpperCamelCase_:   List[str]        =							vnp.copy()  # same in embedded
          UpperCamelCase_:   int        =							torch.tensor(UpperCAmelCase__    )
      elif key_name.startswith('model/wob'    ):
        UpperCamelCase_:   List[Any]        =							'final_logits_bias'
        UpperCamelCase_:   str        =							vnp.copy()  # same in embedded
        UpperCamelCase_:   Union[str, Any]        =							state.reshape((1, -1)    )
        UpperCamelCase_:   Optional[Any]        =							torch.tensor(UpperCAmelCase__    )
      elif key_name == "model/dense/kernel":
        UpperCamelCase_:   Any        =							'model.last_project.weight'
        UpperCamelCase_:   Dict        =							vnp.transpose([1, 0]    ).copy()  # Mesh-Tensorflow is a diagonal matrix
        UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
      elif key_name == "model/dense_1/bias":
        UpperCamelCase_:   List[Any]        =							'model.last_project.bias'
        UpperCamelCase_:   Any        =							vnp.copy()  # same because it is one dimensional
        UpperCamelCase_:   str        =							torch.tensor(UpperCAmelCase__    )
  torch.save(UpperCAmelCase__      ,       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_						:						int						      =					parser.parse_args()
   convert_tf_gptsan_to_pt(args)

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
						import torch


_snake_case      :							Optional[Any]     	=    logging.get_logger(__name__)





@dataclass
class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:str		,    lowerCAmelCase__							:Any=False		,    lowerCAmelCase__							:Tuple=False		,    lowerCAmelCase__							:List[str]=6.0		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Optional[Any]=False		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Tuple="fp4"		,    lowerCAmelCase__							:Union[str, Any]=False		,    **lowerCAmelCase__							:Optional[Any]		,    )  ->Any:
										lowercase         =      load_in_abit
										lowercase         =      load_in_abit
										lowercase         =      llm_inta_threshold
										lowercase         =      llm_inta_skip_modules
										lowercase         =      llm_inta_enable_fpaa_cpu_offload
										lowercase         =      llm_inta_has_fpaa_weight
										lowercase         =      bnb_abit_quant_type
										lowercase         =      bnb_abit_use_double_quant

										if bnb_abit_compute_dtype is None:
														lowercase         =      torch.floataa
										elif isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      getattr(lowerCAmelCase__		,    lowerCAmelCase__     )
										elif isinstance(lowerCAmelCase__		,    torch.dtype     ):
														lowercase         =      bnb_abit_compute_dtype
										else:
														raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype"     )

										self.post_init()





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										if not isinstance(self.llm_inta_threshold		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_threshold must be a float"     )

										if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_skip_modules must be a list of strings"     )
										if not isinstance(self.llm_inta_enable_fpaa_cpu_offload		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean"     )

										if not isinstance(self.llm_inta_has_fpaa_weight		,    lowerCAmelCase__     ):
														raise ValueError("llm_int8_has_fp16_weight must be a boolean"     )

										if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype		,    torch.dtype     ):
														raise ValueError("bnb_4bit_compute_dtype must be torch.dtype"     )

										if not isinstance(self.bnb_abit_quant_type		,    lowerCAmelCase__     ):
														raise ValueError("bnb_4bit_quant_type must be a string"     )

										if not isinstance(self.bnb_abit_use_double_quant		,    lowerCAmelCase__     ):
														raise ValueError("bnb_4bit_use_double_quant must be a boolean"     )

										if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes"     )     ) >= version.parse(
										    "0.39.0"     ):
														raise ValueError(
														    "4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Tuple:
										return self.load_in_abit or self.load_in_abit





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										if self.load_in_abit:
														return "llm_int8"
										elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
														return "fp4"
										elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
														return "nf4"
										else:
														return None





						@classmethod
						def        SCREAMING_SNAKE_CASE(			cls							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Union[str, Any]		,    **lowerCAmelCase__							:Any     )  ->Optional[Any]:
										lowercase         =      cls(**lowerCAmelCase__     )

										lowercase         =      []
										for key, value in kwargs.items():
														if hasattr(lowerCAmelCase__		,    lowerCAmelCase__     ):
																		setattr(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
																		to_remove.append(lowerCAmelCase__     )
										for key in to_remove:
														kwargs.pop(lowerCAmelCase__		,    lowerCAmelCase__     )

										if return_unused_kwargs:
														return config, kwargs
										else:
														return config





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Union[str, os.PathLike]     )  ->Optional[int]:
										with open(lowerCAmelCase__		,    "w"		,    encoding="utf-8"     ) as writer:
														lowercase         =      self.to_dict()
														lowercase         =      json.dumps(lowerCAmelCase__		,    indent=2		,    sort_keys=lowerCAmelCase__     ) + "\n"

														writer.write(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Dict[str, Any]:
										lowercase         =      copy.deepcopy(self.__dict__     )
										lowercase         =      str(output["bnb_4bit_compute_dtype"]     ).split("."     )[1]

										return output





						def __repr__(			self							:int     )  ->Optional[Any]:
										return F'''{self.__class__.__name__} {self.to_json_string()}'''





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:bool = True     )  ->str:
										if use_diff is True:
														lowercase         =      self.to_diff_dict()
										else:
														lowercase         =      self.to_dict()
										return json.dumps(lowerCAmelCase__		,    indent=2		,    sort_keys=lowerCAmelCase__     ) + "\n"





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Dict[str, Any]:
										lowercase         =      self.to_dict()

										# get the default config dict
										lowercase         =      BitsAndBytesConfig().to_dict()

										lowercase         =      {}

										# only serialize values that differ from the default config
										for key, value in config_dict.items():
														if value != default_config_dict[key]:
																		lowercase         =      value

										return serializable_config_dict
