
"""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  lowercase						(	lowerCAmelCase__						):
			if hor == 128:
						lowerCamelCase_					=  ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						lowerCamelCase_					=  (32, 128, 256)
						lowerCamelCase_					=  ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						lowerCamelCase_					=  ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						lowerCamelCase_					=  (32, 64, 128, 256)
						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''': 65_536,
			    '''mid_block_type''': '''MidResTemporalBlock1D''',
			    '''act_fn''': '''mish''',
			}
			lowerCamelCase_					=  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()						)}"						)
			lowerCamelCase_					=  dict(zip(model.state_dict().keys()	,hf_value_function.state_dict().keys()						)						)
			for k, v in mapping.items():
						lowerCamelCase_					=  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  lowercase						(	):
			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, 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''',
			}

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

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





"""simple docstring"""


import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_    							=					namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			return (abs(source - target						) / target) < 0.01






@pytest.mark.integration
def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  _TestCommandArgs(dataset=lowerCAmelCase__	,all_configs=lowerCAmelCase__	,save_infos=lowerCAmelCase__						)
			lowerCamelCase_					=  TestCommand(*lowerCAmelCase__						)
			test_command.run()
			lowerCamelCase_					=  os.path.join(lowerCAmelCase__	,'''README.md'''						)
			assert os.path.exists(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict.from_directory(lowerCAmelCase__						)
			lowerCamelCase_					=  DatasetInfosDict(
			    {
			        '''default''': DatasetInfo(
			            features=Features(
			                {
			                    '''tokens''': Sequence(Value('''string'''						)						),
			                    '''ner_tags''': Sequence(
			                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
			                    '''langs''': Sequence(Value('''string'''						)						),
			                    '''spans''': Sequence(Value('''string'''						)						),
			                }						)	,splits=[
			                {
			                    '''name''': '''train''',
			                    '''num_bytes''': 2_351_563,
			                    '''num_examples''': 10_000,
			                },
			                {
			                    '''name''': '''validation''',
			                    '''num_bytes''': 238_418,
			                    '''num_examples''': 1_000,
			                },
			            ]	,download_size=3_940_680	,dataset_size=2_589_981	,)
			    }						)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase_		, lowerCamelCase_					=  getattr(dataset_infos['''default''']	,lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']	,lowerCAmelCase__						)
						if key == "num_bytes":
									assert is_apercent_close(lowerCAmelCase__	,lowerCAmelCase__						)
						elif key == "splits":
									assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes	,expected[split].num_bytes						)
						else:
									result == expected



