



'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_pegasus import PegasusTokenizer
else:
						UpperCamelCase__ :			Any												=				None


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)


UpperCamelCase__ :			List[Any]												=				'''▁'''

UpperCamelCase__ :			Optional[int]												=				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :			List[str]												=				{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

UpperCamelCase__ :			Optional[Any]												=				{
    '''google/pegasus-xsum''': 5_12,
}






class 		_UpperCamelCase						(							snake_case__					):


			'''simple docstring'''

			_A   :	str    					=	VOCAB_FILES_NAMES
			_A   :	Tuple    					=	PRETRAINED_VOCAB_FILES_MAP
			_A   :	Dict    					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A   :	Optional[Any]    					=	PegasusTokenizer
			_A   :	Dict    					=	['''input_ids''', '''attention_mask''']

			def __init__(							self  :				Optional[int]							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				Optional[int]="<pad>"							,  lowerCAmelCase__  :				Any="</s>"							,  lowerCAmelCase__  :				Dict="<unk>"							,  lowerCAmelCase__  :				int="<mask_2>"							,  lowerCAmelCase__  :				List[Any]="<mask_1>"							,  lowerCAmelCase__  :				str=None							,  lowerCAmelCase__  :				int=1_0_3							,  **lowerCAmelCase__  :				Union[str, Any]							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	offset

					if additional_special_tokens is not None:
							if not isinstance(lowercase_							,  lowercase_		):
									raise TypeError(
									    F"additional_special_tokens should be of type {type(lowercase_		)}, but is"
									    F" {type(lowercase_		)}"		)

							__SCREAMING_SNAKE_CASE :	Tuple					 =	(
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    F"<unk_{i}>" for i in range(len(lowercase_		)							,  self.offset - 1		)
							]

							if len(set(lowercase_		)		) != len(lowercase_		):
									raise ValueError(
									    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
									    F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	additional_special_tokens_extended
					else:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [F"<unk_{i}>" for i in range(2							,  self.offset		)]

					super().__init__(
					    lowercase_							,  tokenizer_file=lowercase_							,  pad_token=lowercase_							,  eos_token=lowercase_							,  unk_token=lowercase_							,  mask_token=lowercase_							,  mask_token_sent=lowercase_							,  offset=lowercase_							,  additional_special_tokens=lowercase_							,  **lowercase_							,  )
					__SCREAMING_SNAKE_CASE :	int					 =	vocab_file
					__SCREAMING_SNAKE_CASE :	str					 =	False if not self.vocab_file else True

			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	set(self.all_special_ids		)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens		) + 3		)		):
							raise ValueError(
							    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
							    F" {len(self.additional_special_tokens		)} additional_special_tokens, but got {all_special_ids}"		)

					return [1 if x in all_special_ids else 0 for x in seq]

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Union[str, Any] = None							,  lowerCAmelCase__  :				Optional[int] = False		):





					"""simple docstring"""






					if already_has_special_tokens:
							return self._special_token_mask(lowercase_		)
					elif token_ids_a is None:
							return self._special_token_mask(lowercase_		) + [1]
					else:
							return self._special_token_mask(token_ids_a + token_ids_a		) + [1]

			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Any=None		):





					"""simple docstring"""






					if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]



			def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Optional[Any] = None		):





					"""simple docstring"""






					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer."""		)

					if not os.path.isdir(lowercase_		):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
							return
					__SCREAMING_SNAKE_CASE :	Dict					 =	os.path.join(
					    lowercase_							,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

					if os.path.abspath(self.vocab_file		) != os.path.abspath(lowercase_		):
							copyfile(self.vocab_file							,  lowercase_		)

					return (out_vocab_file,)




'''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        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if hor == 1_28:
				__SCREAMING_SNAKE_CASE :	Any					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(32, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	str					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

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

		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(_lowerCamelCase ,   _lowerCamelCase      )


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	{
		    """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""",
		}

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	UNetaDModel(**_lowerCamelCase      )
		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 :	Optional[Any]					 =	dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	str					 =	state_dict.pop(_lowerCamelCase      )

		hf_value_function.load_state_dict(_lowerCamelCase      )

		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(_lowerCamelCase ,   _lowerCamelCase      )


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