import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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


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






def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if not grid or not grid[0]:
        raise TypeError('''The grid does not contain the appropriate information'''     )

    for cell_n in range(1  ,      len(grid[0]     )     ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    __UpperCamelCase					:Union[str, Any]        =		grid[0]

    for row_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        __UpperCamelCase					:Optional[int]        =		grid[row_n]
        __UpperCamelCase					:Dict        =		fill_row(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Any        =		grid[row_n]

    return grid[-1][-1]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    current_row[0] += row_above[0]
    for cell_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        current_row[cell_n] += min(current_row[cell_n - 1]  ,      row_above[cell_n]     )

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()





