




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__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					if hor == 1_28:
										__UpperCamelCase					:	Tuple             =      ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
										__UpperCamelCase					:	Tuple             =      (32, 1_28, 2_56)
										__UpperCamelCase					:	Union[str, Any]             =      ("""UpResnetBlock1D""", """UpResnetBlock1D""")

					elif hor == 32:
										__UpperCamelCase					:	Any             =      ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
										__UpperCamelCase					:	Tuple             =      (32, 64, 1_28, 2_56)
										__UpperCamelCase					:	Dict             =      ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
					__UpperCamelCase					:	str             =      torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''				)
					__UpperCamelCase					:	int             =      model.state_dict()
					__UpperCamelCase					:	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""": 6_55_36,
					    """mid_block_type""": """MidResTemporalBlock1D""",
					    """act_fn""": """mish""",
					}
					__UpperCamelCase					:	Union[str, Any]             =      UNetaDModel(**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()				)}'''				)
					__UpperCamelCase					:	List[str]             =      dict(zip(model.state_dict().keys()		,	hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										__UpperCamelCase					:	Dict             =      state_dict.pop(snake_case				)
					hf_value_function.load_state_dict(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(snake_case		,	snake_case				)





def 				A__ ()     ->						Union[str, Any]:
					__UpperCamelCase					:	List[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, 1_28, 2_56),
					    """layers_per_block""": 1,
					    """downsample_each_block""": True,
					    """sample_size""": 6_55_36,
					    """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""",
					}

					__UpperCamelCase					:	Optional[int]             =      torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""				)
					__UpperCamelCase					:	Optional[int]             =      model
					__UpperCamelCase					:	List[Any]             =      UNetaDModel(**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()				)}'''				)

					__UpperCamelCase					:	List[Any]             =      dict(zip(state_dict.keys()		,	hf_value_function.state_dict().keys()				)				)
					for k, v in mapping.items():
										__UpperCamelCase					:	Union[str, Any]             =      state_dict.pop(snake_case				)

					hf_value_function.load_state_dict(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(snake_case		,	snake_case				)


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












import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       int      	=		['input_ids', 'attention_mask']






	def __init__(					self		:						List[Any]					,     lowerCAmelCase		:						Union[str, Any]="</s>"					,     lowerCAmelCase		:						Tuple="<unk>"					,     lowerCAmelCase		:						Union[str, Any]="<pad>"					,     lowerCAmelCase		:						Tuple=125					,     lowerCAmelCase		:						str=None					,     **lowerCAmelCase		:						Optional[Any]					,     )	->       None:







						"""simple docstring"""







						if extra_ids > 0 and additional_special_tokens is None:
											__UpperCamelCase					:	str             =      [F'''<extra_id_{i}>''' for i in range(lowerCAmelCase	)]
						elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra_id special tokens
											__UpperCamelCase					:	Tuple             =      len(set(filter(lambda lowerCAmelCase	: bool("""extra_id""" in str(lowerCAmelCase	)	)					,     lowerCAmelCase	)	)	)
											if extra_tokens != extra_ids:
																raise ValueError(
																    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																    """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
																    """ extra_ids tokens"""	)

						__UpperCamelCase					:	Tuple             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else pad_token
						__UpperCamelCase					:	Tuple             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else eos_token
						__UpperCamelCase					:	int             =      AddedToken(lowerCAmelCase					,     lstrip=lowerCAmelCase					,     rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase					,     lowerCAmelCase	) else unk_token

						super().__init__(
						    eos_token=lowerCAmelCase					,     unk_token=lowerCAmelCase					,     pad_token=lowerCAmelCase					,     extra_ids=lowerCAmelCase					,     additional_special_tokens=lowerCAmelCase					,     **lowerCAmelCase					,     )

						__UpperCamelCase					:	Any             =      extra_ids

						__UpperCamelCase					:	List[Any]             =      2**8  # utf is 8 bits

						# define special tokens dict
						__UpperCamelCase					:	Dict[int, str]             =      {
						    self.pad_token: 0,
						    self.eos_token: 1,
						    self.unk_token: 2,
						}
						__UpperCamelCase					:	int             =      len(self.special_tokens_encoder	)
						__UpperCamelCase					:	Tuple             =      len(lowerCAmelCase	)
						for i, token in enumerate(lowerCAmelCase	):
											__UpperCamelCase					:	Optional[int]             =      self.vocab_size + i - n
						__UpperCamelCase					:	Dict[str, int]             =      {v: k for k, v in self.special_tokens_encoder.items()}






	@property
	def 					lowerCamelCase__     (					self		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						return self._utf_vocab_size + self._num_special_tokens + self._extra_ids






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None					,     lowerCAmelCase		:						bool = False	)	->       List[int]:







						"""simple docstring"""







						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCAmelCase					,     token_ids_a=lowerCAmelCase					,     already_has_special_tokens=lowerCAmelCase	)

						# normal case: some special tokens
						if token_ids_a is None:
											return ([0] * len(lowerCAmelCase	)) + [1]
						return ([0] * len(lowerCAmelCase	)) + [1] + ([0] * len(lowerCAmelCase	)) + [1]






	def 					lowerCamelCase__     (					self		:						Tuple					,     lowerCAmelCase		:						List[int]	)	->       List[int]:







						"""simple docstring"""







						if len(lowerCAmelCase	) > 0 and token_ids[-1] == self.eos_token_id:
											warnings.warn(
											    F'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
											    """ eos tokens being added."""	)
											return token_ids
						else:
											return token_ids + [self.eos_token_id]






	def 					lowerCamelCase__     (					self		:						List[Any]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None	)	->       List[int]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      [self.eos_token_id]

						if token_ids_a is None:
											return len(token_ids_a + eos	) * [0]
						return len(token_ids_a + eos + token_ids_a + eos	) * [0]






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						List[int]					,     lowerCAmelCase		:						Optional[List[int]] = None	)	->       List[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      self._add_eos_if_not_present(lowerCAmelCase	)
						if token_ids_a is None:
											return token_ids_a
						else:
											__UpperCamelCase					:	List[Any]             =      self._add_eos_if_not_present(lowerCAmelCase	)
											return token_ids_a + token_ids_a






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						str	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      [chr(lowerCAmelCase	) for i in text.encode("""utf-8"""	)]
						return tokens






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Optional[int]	)	->       int:







						"""simple docstring"""







						if token in self.special_tokens_encoder:
											__UpperCamelCase					:	Dict             =      self.special_tokens_encoder[token]
						elif token in self.added_tokens_encoder:
											__UpperCamelCase					:	Tuple             =      self.added_tokens_encoder[token]
						elif len(lowerCAmelCase	) != 1:
											__UpperCamelCase					:	Dict             =      self.unk_token_id
						else:
											__UpperCamelCase					:	Union[str, Any]             =      ord(lowerCAmelCase	) + self._num_special_tokens
						return token_id






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Dict	)	->       Optional[Any]:







						"""simple docstring"""







						if index in self.special_tokens_decoder:
											__UpperCamelCase					:	str             =      self.special_tokens_decoder[index]
						else:
											__UpperCamelCase					:	Any             =      chr(index - self._num_special_tokens	)
						return token






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      b""""""
						for token in tokens:
											if token in self.special_tokens_decoder:
																__UpperCamelCase					:	Optional[Any]             =      self.special_tokens_decoder[token].encode("""utf-8"""	)
											elif token in self.added_tokens_decoder:
																__UpperCamelCase					:	Optional[int]             =      self.special_tokens_decoder[token].encode("""utf-8"""	)
											elif token in self.special_tokens_encoder:
																__UpperCamelCase					:	Tuple             =      token.encode("""utf-8"""	)
											elif token in self.added_tokens_encoder:
																__UpperCamelCase					:	Tuple             =      token.encode("""utf-8"""	)
											else:
																__UpperCamelCase					:	Union[str, Any]             =      bytes([ord(lowerCAmelCase	)]	)
											bstring += tok_string
						__UpperCamelCase					:	Optional[int]             =      bstring.decode("""utf-8"""					,     errors="""ignore"""	)
						return string






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Optional[str] = None	)	->       Tuple[str]:







						"""simple docstring"""







						return ()






