



import argparse
import json
import os
from collections import OrderedDict

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


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




# Copyright 2022 The HuggingFace Team and The OpenBMB 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_									=							{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CpmAntForCausalLM",
        "CpmAntModel",
        "CpmAntPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

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