





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=os.path.join(args.tf_model_dir				,							"parameters.json"     )
   UpperCAmelCase__						=json.loads(open(A     ).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(A     ).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(A     )
         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(A     )
            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(A     )
            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(A     )
         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(A     )
            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(A     )
            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(A     )
            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(A     )
         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(A     )
            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(A     )
         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(A     )
               UpperCAmelCase__						="model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
               UpperCAmelCase__						=torch.tensor(A     )
               UpperCAmelCase__						="model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
               UpperCAmelCase__						=torch.tensor(A     )
            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(A     )
         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(A     )
            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(A     )
         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(A     )
            if key_name.startswith("model/wte"     ):
               UpperCAmelCase__						="lm_head.weight"
               UpperCAmelCase__						=vnp.copy()  # same in embedded
               UpperCAmelCase__						=torch.tensor(A     )
         elif key_name.startswith("model/wob"     ):
            UpperCAmelCase__						="final_logits_bias"
            UpperCAmelCase__						=vnp.copy()  # same in embedded
            UpperCAmelCase__						=state.reshape((1, -1)     )
            UpperCAmelCase__						=torch.tensor(A     )
         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(A     )
         elif key_name == "model/dense_1/bias":
            UpperCAmelCase__						="model.last_project.bias"
            UpperCAmelCase__						=vnp.copy()  # same because it is one dimensional
            UpperCAmelCase__						=torch.tensor(A     )
   torch.save(A				,							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)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'roformer'

 def __init__(  self,		A_=5_0000,		A_=None,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=1536,		A_=2,		A_=0.02,		A_=1E-12,		A_=0,		A_=False,		A_=True,		**A_,		)	->       Dict:
    super().__init__(pad_token_id=A_,		**A_		)

    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size if embedding_size is None else embedding_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=type_vocab_size
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=rotary_value
    UpperCAmelCase__						=use_cache

class     snake_case_		(   a							):






 '''simple docstring'''





 @property
 def  __UpperCAmelCase						(  self		)	->       Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
       UpperCAmelCase__						={0: "batch", 1: "choice", 2: "sequence"}
    else:
       UpperCAmelCase__						={0: "batch", 1: "sequence"}
    UpperCAmelCase__						={0: "batch", 1: "sequence"}
    return OrderedDict(
        [
            ("input_ids", dynamic_axis),
            ("attention_mask", dynamic_axis),
            ("token_type_ids", dynamic_axis),
        ]		)



