





'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





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


if __name__ == "__main__":
  __lowerCAmelCase							:     Dict		     =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")
  __lowerCAmelCase							:     int		     =parser.parse_args()
  convert_tf_gptsan_to_pt(args)










'''simple docstring'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Optional[int]		     =[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__    =      k.replace(_lowerCamelCase						,	_lowerCamelCase	)

    if k.startswith("encoder"	):
        A__    =      k.replace(".attn"						,	".self_attn"	)
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"final_layer_norm"	)
    elif k.startswith("decoder"	):
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"encoder_attn_layer_norm"	)
        A__    =      k.replace("norm3"						,	"final_layer_norm"	)
    return k





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      [
        "model.encoder.layernorm_embedding.weight",
        "model.encoder.layernorm_embedding.bias",
        "model.decoder.layernorm_embedding.weight",
        "model.decoder.layernorm_embedding.bias",
    ]
    for k in keys:
        A__    =      sd.pop(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    A__    =      model["model"]
    A__    =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    A__    =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    A__    =      m.model.state_dict().keys()
    A__    =      []
    A__    =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        A__    =      rename_state_dict_key(_lowerCamelCase	)
        if new_k not in valid_keys:
            failures.append([k, new_k]	)
        else:
            A__    =      v
    if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
        rename_layernorm_keys(_lowerCamelCase	)
    m.model.load_state_dict(_lowerCamelCase						,	strict=_lowerCamelCase	)
    m.half()
    m.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  __lowerCAmelCase							:     Union[str, Any]		     =parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



