






import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


a						:			Tuple     	=					[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]						):





  for pegasus_name, hf_name in PATTERNS:
    __UpperCAmelCase				:      Optional[int]	 =    k.replace(__lowerCamelCase    ,						__lowerCamelCase						)
  return k






def 							lowerCamelCase__ (       __lowerCamelCase     :	str    ,						__lowerCamelCase     :	Tuple						):
  __UpperCAmelCase				:      Any	 =    DEFAULTS.copy()
  cfg_kwargs.update(__lowerCamelCase						)
  __UpperCAmelCase				:      Tuple	 =    PegasusConfig(**__lowerCamelCase						)
  __UpperCAmelCase				:      Dict	 =    PegasusForConditionalGeneration(__lowerCamelCase						)
  __UpperCAmelCase				:      Any	 =    torch_model.model.state_dict()
  __UpperCAmelCase				:      Union[str, Any]	 =    {}
  for k, v in tf_weights.items():
    __UpperCAmelCase				:      Optional[Any]	 =    rename_state_dict_key(__lowerCamelCase						)
    if new_k not in sd:
      raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""						)

    if "dense" in k or "proj" in new_k:
      __UpperCAmelCase				:      Optional[Any]	 =    v.T
    __UpperCAmelCase				:      int	 =    torch.tensor(__lowerCamelCase    ,						dtype=sd[new_k].dtype						)
    assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
  # make sure embedding.padding_idx is respected
  __UpperCAmelCase				:      int	 =    torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]						)
  __UpperCAmelCase				:      List[Any]	 =    mapping["shared.weight"]
  __UpperCAmelCase				:      Tuple	 =    mapping["shared.weight"]
  __UpperCAmelCase				:      Optional[int]	 =    {k: torch.zeros_like(__lowerCamelCase						) for k, v in sd.items() if k.endswith("""bias"""						) and k not in mapping}
  mapping.update(**__lowerCamelCase						)
  __UpperCAmelCase				:      Union[str, Any]	 =    torch_model.model.load_state_dict(__lowerCamelCase    ,						strict=__lowerCamelCase						)
  __UpperCAmelCase				:      Dict	 =    [
      k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
  ]
  assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
  assert extra == [], f"""no matches found for the following tf keys {extra}"""
  return torch_model






def 							lowerCamelCase__ (       __lowerCamelCase     :	Any="./ckpt/aeslc/model.ckpt-32000"						):
  __UpperCAmelCase				:      Tuple	 =    tf.train.list_variables(__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[Any]	 =    {}
  __UpperCAmelCase				:      List[str]	 =    ["Adafactor", "global_step"]
  for name, shape in tqdm(__lowerCamelCase    ,						desc="""converting tf checkpoint to dict"""						):
    __UpperCAmelCase				:      str	 =    any(pat in name for pat in ignore_name						)
    if skip_key:
      continue
    __UpperCAmelCase				:      Union[str, Any]	 =    tf.train.load_variable(__lowerCamelCase    ,						__lowerCamelCase						)
    __UpperCAmelCase				:      Any	 =    array
  return tf_weights






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	Dict						):
  __UpperCAmelCase				:      Any	 =    Path(__lowerCamelCase						).parent.name
  __UpperCAmelCase				:      Optional[int]	 =    task_specific_params[f"""summarization_{dataset}"""]["max_position_embeddings"]
  __UpperCAmelCase				:      Tuple	 =    PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""    ,						model_max_length=__lowerCamelCase						)
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(__lowerCamelCase						)

  # convert model
  __UpperCAmelCase				:      Union[str, Any]	 =    get_tf_weights_as_numpy(__lowerCamelCase						)
  __UpperCAmelCase				:      int	 =    task_specific_params[f"""summarization_{dataset}"""]
  if dataset == "large":
    __UpperCAmelCase				:      Any	 =    task_specific_params
  __UpperCAmelCase				:      Any	 =    convert_pegasus(__lowerCamelCase    ,						__lowerCamelCase						)
  torch_model.save_pretrained(__lowerCamelCase						)
  __UpperCAmelCase				:      Union[str, Any]	 =    torch_model.state_dict()
  sd.pop("""model.decoder.embed_positions.weight"""						)
  sd.pop("""model.encoder.embed_positions.weight"""						)
  torch.save(__lowerCamelCase    ,						Path(__lowerCamelCase						) / """pytorch_model.bin"""						)


if __name__ == "__main__":
       a						:			Union[str, Any]     	=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
       parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
       a						:			Optional[int]     	=					parser.parse_args()
       if args.save_dir is None:
              a						:			int     	=					Path(args.tf_ckpt_path).parent.name
              a						:			List[str]     	=					os.path.join("pegasus", dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






'''simple docstring'''


import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if hor == 128:
          __A	:       int      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Union[str, Any]      =							(32, 128, 256)
          __A	:       str      =							("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          __A	:       List[Any]      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Optional[Any]      =							(32, 64, 128, 256)
          __A	:       Dict      =							("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     __A	:       Union[str, Any]      =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
     __A	:       List[str]      =							model.state_dict()
     __A	:       Any      =							{
         "down_block_types": down_block_types,
         "block_out_channels": block_out_channels,
         "up_block_types": up_block_types,
         "layers_per_block": 1,
         "use_timestep_embedding": True,
         "out_block_type": "OutConv1DBlock",
         "norm_num_groups": 8,
         "downsample_each_block": False,
         "in_channels": 14,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "sample_size": 6_5536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)
     __A	:       Dict      =							dict(zip(model.state_dict().keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       Optional[int]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"							)
     with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							{
         "in_channels": 14,
         "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
         "up_block_types": (),
         "out_block_type": "ValueFunction",
         "mid_block_type": "ValueFunctionMidBlock1D",
         "block_out_channels": (32, 64, 128, 256),
         "layers_per_block": 1,
         "downsample_each_block": True,
         "sample_size": 6_5536,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "use_timestep_embedding": True,
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "norm_num_groups": 8,
         "act_fn": "mish",
     }

     __A	:       Optional[int]      =							torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
     __A	:       Dict      =							model
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)

     __A	:       int      =							dict(zip(state_dict.keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       str      =							state_dict.pop(SCREAMING_SNAKE_CASE							)

     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     torch.save(hf_value_function.state_dict()       ,					"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"							)
     with open("hub/hopper-medium-v2/value_function/config.json"       ,					"w"							) as f:
          json.dump(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      unet(32)
      # unet(128)
      value_function()

