



'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def        lowerCamelCase  (				_snake_case  :						Tuple       ):





    '''simple docstring'''


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


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









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return x if y == 0 else greatest_common_divisor(_snake_case     ,x % y       )




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return (x * y) // greatest_common_divisor(_snake_case     ,_snake_case       )




def        lowerCamelCase  (				_snake_case  :						int = 20       ):





    '''simple docstring'''


    lowercase__		       =						1
    for i in range(1     ,n + 1       ):
        lowercase__		       =						lcm(_snake_case     ,_snake_case       )
    return g


if __name__ == "__main__":
 print(f'''{solution() = }''')




