


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









from ..utils import DummyObject, requires_backends






class    UpperCAmelCase__	( metaclass=A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  ["speech"]






    def __init__(      self ,       *A_ ,       **A_   )  ->	Any:
         requires_backends(self ,       ['speech']   )






class    UpperCAmelCase__	( metaclass=A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[str, Any]              =  ["speech"]






    def __init__(      self ,       *A_ ,       **A_   )  ->	Union[str, Any]:
         requires_backends(self ,       ['speech']   )





