

import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


a_						     =    logging.getLogger(__name__)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Tuple      ,      a							: str      ,      a							: Dict      ,      a							: Optional[Any]      ,      a							: List[Any]=None	)							->       Dict:

				"""simple docstring"""

				super().__init__(
				    a      ,      question_encoder_tokenizer=a      ,      generator_tokenizer=a      ,      index=a      ,      init_retrieval=a      ,      )
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       None
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: int	)							->       str:

				"""simple docstring"""

				logger.info("initializing retrieval"	)

				# initializing a separate process group for retrieval as the default
				# nccl backend doesn't support gather/scatter operations while gloo
				# is too slow to replace nccl for the core gpu communication
				if dist.is_initialized():
					logger.info("dist initialized"	)
					# needs to be set manually
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self._infer_socket_ifname()
					# avoid clash with the NCCL port
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       str(distributed_port + 1	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       dist.new_group(ranks=a      ,      backend="gloo"	)

				# initialize retriever only on the main worker
				if not dist.is_initialized() or self._is_main():
					logger.info("dist not initialized / main"	)
					self.index.init_index()

				# all processes wait untill the retriever is initialized by the main process
				if dist.is_initialized():
					torch.distributed.barrier(group=self.process_group	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       str:

				"""simple docstring"""

				return dist.get_rank(group=self.process_group	) == 0
			def 							__UpperCamelCase (			self							: Dict      ,      a							: int      ,      a							: List[str]      ,      a							: str=torch.floataa	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.empty(a      ,      dtype=a	)
				dist.scatter(a      ,      src=0      ,      scatter_list=a      ,      group=self.process_group	)
				return target_tensor
			def 							__UpperCamelCase (			self							: Tuple	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       psutil.net_if_addrs()
				# a hacky way to deal with varying network interface names
				SCREAMING_SNAKE_CASE   :      Dict				     =       next((addr for addr in addrs if addr.startswith("e"	))      ,      a	)
				return ifname






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: np.ndarray      ,      a							: int	)							->       Tuple[np.ndarray, List[dict]]:

				"""simple docstring"""

				if not dist.is_initialized():
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       self._main_retrieve(a      ,      a	)
					return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a	)

				# distributed training
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dist.get_world_size(group=self.process_group	)

				# gather logic
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				if self._is_main():
					SCREAMING_SNAKE_CASE   :      List[str]				     =       [torch.empty(question_hidden_states.shape      ,      dtype=torch.floataa	) for _ in range(a	)]
				dist.gather(torch.tensor(a	)      ,      dst=0      ,      gather_list=a      ,      group=self.process_group	)

				# scatter logic
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       question_hidden_states.shape[0]
				SCREAMING_SNAKE_CASE   :      Any				     =       []
				SCREAMING_SNAKE_CASE   :      Tuple				     =       []
				if self._is_main():
					assert len(a	) == world_size
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       self._main_retrieve(torch.cat(a	).numpy()      ,      a	)
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.tensor(a	), torch.tensor(a	)
					SCREAMING_SNAKE_CASE   :      int				     =       self._chunk_tensor(a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       self._chunk_tensor(a      ,      a	)
				SCREAMING_SNAKE_CASE   :      str				     =       self._scattered(a      ,      [n_queries, n_docs]      ,      target_type=torch.intaa	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self._scattered(a      ,      [n_queries, n_docs, question_hidden_states.shape[1]]	)

				return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(a	)


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  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "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": 65536,
	    "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",
	}

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

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)


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