'''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      UpperCamelCase_						(					snake_case_       :       int      )					->     Optional[Any]:






 '''simple docstring'''

 if hor == 1_28:
  __lowerCAmelCase											=						('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
  __lowerCAmelCase											=						(32, 1_28, 2_56)
  __lowerCAmelCase											=						('UpResnetBlock1D', 'UpResnetBlock1D')

 elif hor == 32:
  __lowerCAmelCase											=						('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
  __lowerCAmelCase											=						(32, 64, 1_28, 2_56)
  __lowerCAmelCase											=						('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
 __lowerCAmelCase											=						torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""      )
 __lowerCAmelCase											=						model.state_dict()
 __lowerCAmelCase											=						{
     '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',
 }
 __lowerCAmelCase											=						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()      )}"""      )
 __lowerCAmelCase											=						dict(zip(model.state_dict().keys()						,		hf_value_function.state_dict().keys()      )      )
 for k, v in mapping.items():
  __lowerCAmelCase											=						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      UpperCamelCase_						(					)					->     Tuple:






 '''simple docstring'''

 __lowerCAmelCase											=						{
     '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',
 }

 __lowerCAmelCase											=						torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
 __lowerCAmelCase											=						model
 __lowerCAmelCase											=						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()      )}"""      )

 __lowerCAmelCase											=						dict(zip(state_dict.keys()						,		hf_value_function.state_dict().keys()      )      )
 for k, v in mapping.items():
  __lowerCAmelCase											=						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()











from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

            snake_case__							:	Dict   	=	hidden_size
            snake_case__							:	Optional[int]   	=	num_hidden_layers
            snake_case__							:	Any   	=	num_attention_heads
            snake_case__							:	List[Any]   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Dict   	=	attention_probs_dropout_prob
            snake_case__							:	List[str]   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	Optional[int]   	=	relative_attention
            snake_case__							:	Tuple   	=	max_relative_positions
            snake_case__							:	Union[str, Any]   	=	pad_token_id
            snake_case__							:	Optional[int]   	=	position_biased_input

            # Backwards compatibility
            if type(_snake_case					) == str:
                   snake_case__							:	int   	=	[x.strip() for x in pos_att_type.lower().split('|'					)]

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	List[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]					)
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional["TensorType"] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






