




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
							import torch






class        _a(    a__    ):
		lowerCamelCase__     :Tuple   				=		'char'
		lowerCamelCase__     :List[Any]   				=		'bpe'
		lowerCamelCase__     :List[Any]   				=		'wp'






__lowerCAmelCase :Optional[Any]     =    (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class        _a(    a__    ):
		lowerCamelCase__     :Union[str, Any]   				=		['image_processor', 'char_tokenizer']
		lowerCamelCase__     :Optional[Any]   				=		'ViTImageProcessor'
		lowerCamelCase__     :Tuple   				=		'MgpstrTokenizer'







		def __init__(    self ,						__snake_case=None ,						__snake_case=None ,						**__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead." ,						_A ,						)
												_snake_case       :					Union[str, Any]				       =   kwargs.pop("feature_extractor"				)

							_snake_case       :					int				       =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError("You need to specify an `image_processor`."				)
							if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`."				)

							_snake_case       :					List[Any]				       =   tokenizer
							_snake_case       :					Tuple				       =   AutoTokenizer.from_pretrained("gpt2"				)
							_snake_case       :					Union[str, Any]				       =   AutoTokenizer.from_pretrained("bert-base-uncased"				)

							super().__init__(_A ,						_A				)







		def __call__(    self ,						__snake_case=None ,						__snake_case=None ,						__snake_case=None ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							if images is None and text is None:
												raise ValueError("You need to specify either an `images` or `text` input to process."				)

							if images is not None:
												_snake_case       :					Union[str, Any]				       =   self.image_processor(_A ,						return_tensors=_A ,						**_A				)
							if text is not None:
												_snake_case       :					Tuple				       =   self.char_tokenizer(_A ,						return_tensors=_A ,						**_A				)

							if text is None:
												return inputs
							elif images is None:
												return encodings
							else:
												_snake_case       :					Any				       =   encodings['input_ids']
												return inputs







		def        lowercase       (    self ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   sequences
							_snake_case       :					List[Any]				       =   char_preds.size(0				)

							_snake_case       :					Union[str, Any]				       =   self._decode_helper(_A ,						"char"				)
							_snake_case       :					Tuple				       =   self._decode_helper(_A ,						"bpe"				)
							_snake_case       :					int				       =   self._decode_helper(_A ,						"wp"				)

							_snake_case       :					Dict				       =   []
							_snake_case       :					int				       =   []
							for i in range(_A				):
												_snake_case       :					str				       =   [char_scores[i], bpe_scores[i], wp_scores[i]]
												_snake_case       :					Optional[int]				       =   [char_strs[i], bpe_strs[i], wp_strs[i]]
												_snake_case       :					Optional[Any]				       =   scores.index(max(_A				)				)
												final_strs.append(strs[max_score_index]				)
												final_scores.append(scores[max_score_index]				)

							_snake_case       :					List[Any]				       =   {}
							_snake_case       :					Any				       =   final_strs
							_snake_case       :					Optional[Any]				       =   final_scores
							_snake_case       :					Optional[int]				       =   char_strs
							_snake_case       :					int				       =   bpe_strs
							_snake_case       :					Any				       =   wp_strs
							return out







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							if format == DecodeType.CHARACTER:
												_snake_case       :					int				       =   self.char_decode
												_snake_case       :					Any				       =   1
												_snake_case       :					int				       =   '[s]'
							elif format == DecodeType.BPE:
												_snake_case       :					Optional[Any]				       =   self.bpe_decode
												_snake_case       :					Optional[int]				       =   2
												_snake_case       :					Optional[Any]				       =   '#'
							elif format == DecodeType.WORDPIECE:
												_snake_case       :					str				       =   self.wp_decode
												_snake_case       :					Any				       =   1_0_2
												_snake_case       :					Any				       =   '[SEP]'
							else:
												raise ValueError(f"""Format {format} is not supported."""				)

							_snake_case       :					str				       =   [], []
							_snake_case       :					Union[str, Any]				       =   pred_logits.size(0				)
							_snake_case       :					List[str]				       =   pred_logits.size(1				)
							_snake_case       :					int				       =   pred_logits.topk(1 ,						dim=-1 ,						largest=_A ,						sorted=_A				)
							_snake_case       :					Tuple				       =   preds_index.view(-1 ,						_A				)[:, 1:]
							_snake_case       :					Union[str, Any]				       =   decoder(_A				)
							_snake_case       :					Any				       =   torch.nn.functional.softmax(_A ,						dim=2				).max(dim=2				)
							_snake_case       :					Tuple				       =   preds_max_prob[:, 1:]

							for index in range(_A				):
												_snake_case       :					Any				       =   preds_str[index].find(_A				)
												_snake_case       :					Dict				       =   preds_str[index][:pred_eos]
												_snake_case       :					List[Any]				       =   preds_index[index].cpu().tolist()
												_snake_case       :					Dict				       =   pred_index.index(_A				) if eos_token in pred_index else -1
												_snake_case       :					int				       =   preds_max_prob[index][: pred_eos_index + 1]
												_snake_case       :					Union[str, Any]				       =   pred_max_prob.cumprod(dim=0				)[-1] if pred_max_prob.nelement() != 0 else 0.0
												dec_strs.append(_A				)
												conf_scores.append(_A				)

							return dec_strs, conf_scores







		def        lowercase       (    self ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Any				       =   [seq.replace(" " ,						""				) for seq in self.char_tokenizer.batch_decode(_A				)]
							return decode_strs







		def        lowercase       (    self ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							return self.bpe_tokenizer.batch_decode(_A				)







		def        lowercase       (    self ,						__snake_case				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   [seq.replace(" " ,						""				) for seq in self.wp_tokenizer.batch_decode(_A				)]
							return decode_strs






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    A				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										_snake_case       :					List[Any]				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Optional[int]				       =   (32, 64, 128, 256)
										_snake_case       :					int				       =   ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					_snake_case       :					Dict				       =   torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
					_snake_case       :					Dict				       =   model.state_dict()
					_snake_case       :					Tuple				       =   {
					    "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": 65_536,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					_snake_case       :					Any				       =   UNetaDModel(**UpperCAmelCase				)
					print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)
					_snake_case       :					List[Any]				       =   dict(zip(model.state_dict().keys()  ,  hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										_snake_case       :					Optional[int]				       =   state_dict.pop(UpperCAmelCase				)
					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					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": 65_536,
					    "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",
					}

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

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

					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)


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