





'''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__(__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	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''': 65536,
	    '''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 lowerCAmelCase__()       ->   Optional[int]:







	'''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, 128, 256),
	    '''layers_per_block''': 1,
	    '''downsample_each_block''': True,
	    '''sample_size''': 65536,
	    '''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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a						=   {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   ["FunnelTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FunnelBaseModel",
			    "FunnelForMaskedLM",
			    "FunnelForMultipleChoice",
			    "FunnelForPreTraining",
			    "FunnelForQuestionAnswering",
			    "FunnelForSequenceClassification",
			    "FunnelForTokenClassification",
			    "FunnelModel",
			    "FunnelPreTrainedModel",
			    "load_tf_weights_in_funnel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFFunnelBaseModel",
			    "TFFunnelForMaskedLM",
			    "TFFunnelForMultipleChoice",
			    "TFFunnelForPreTraining",
			    "TFFunnelForQuestionAnswering",
			    "TFFunnelForSequenceClassification",
			    "TFFunnelForTokenClassification",
			    "TFFunnelModel",
			    "TFFunnelPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



