






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)

"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
