

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_	(				a	):

      """simple docstring"""



      if hor == 1_2_8:
            SCREAMING_SNAKE_CASE_					:	Tuple     							=  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
            SCREAMING_SNAKE_CASE_					:	Tuple     							=  (3_2, 1_2_8, 2_5_6)
            SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  ("""UpResnetBlock1D""", """UpResnetBlock1D""")

      elif hor == 3_2:
            SCREAMING_SNAKE_CASE_					:	Any     							=  ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
            SCREAMING_SNAKE_CASE_					:	Tuple     							=  (3_2, 6_4, 1_2_8, 2_5_6)
            SCREAMING_SNAKE_CASE_					:	Dict     							=  ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
      SCREAMING_SNAKE_CASE_					:	str     							=  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""": 1_4,
          """out_channels""": 1_4,
          """extra_in_channels""": 0,
          """time_embedding_type""": """positional""",
          """flip_sin_to_cos""": False,
          """freq_shift""": 1,
          """sample_size""": 6_5_5_3_6,
          """mid_block_type""": """MidResTemporalBlock1D""",
          """act_fn""": """mish""",
      }
      SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  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_					:	Dict     							=  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  A_	(				):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_					:	List[Any]     							=  {
          """in_channels""": 1_4,
          """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
          """up_block_types""": (),
          """out_block_type""": """ValueFunction""",
          """mid_block_type""": """ValueFunctionMidBlock1D""",
          """block_out_channels""": (3_2, 6_4, 1_2_8, 2_5_6),
          """layers_per_block""": 1,
          """downsample_each_block""": True,
          """sample_size""": 6_5_5_3_6,
          """out_channels""": 1_4,
          """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_					:	Optional[int]     							=  model
      SCREAMING_SNAKE_CASE_					:	List[Any]     							=  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[Any]     							=  dict(zip(state_dict.keys()       , hf_value_function.state_dict().keys()	)	)
      for k, v in mapping.items():
            SCREAMING_SNAKE_CASE_					:	Union[str, Any]     							=  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()












import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


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







						"""simple docstring"""







						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as input_file:
											__UpperCamelCase					:	Union[str, Any]             =      re.compile(R"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"""	)
											__UpperCamelCase					:	str             =      input_file.read()
											__UpperCamelCase					:	Optional[int]             =      regexp.search(lowerCAmelCase	)

						return match






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







						"""simple docstring"""







						with open(lowerCAmelCase					,     encoding="""utf-8"""	) as input_file:
											__UpperCamelCase					:	Dict             =      re.compile(R"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"""					,     re.DOTALL	)
											__UpperCamelCase					:	str             =      input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											__UpperCamelCase					:	List[Any]             =      regexp.finditer(lowerCAmelCase	)

						__UpperCamelCase					:	int             =      [match for match in matches if match is not None and match.group(1	) is not None]
						return matches[0] if matches else None






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







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      Path("""./datasets"""	)
						__UpperCamelCase					:	Optional[int]             =      list(dataset_paths.absolute().glob("""**/*.py"""	)	)

						for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(lowerCAmelCase	)	):
																raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''	)






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







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      Path("""./datasets"""	)
						__UpperCamelCase					:	Optional[int]             =      list(dataset_paths.absolute().glob("""**/*.py"""	)	)

						for dataset in dataset_files:
											if self._no_print_statements(str(lowerCAmelCase	)	):
																raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''	)






