





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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Any:
    if hor == 128:
        lowerCamelCase__					:   Tuple      				=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
        lowerCamelCase__					:   Optional[Any]      				=	(32, 128, 256)
        lowerCamelCase__					:   Optional[Any]      				=	('UpResnetBlock1D', 'UpResnetBlock1D')

    elif hor == 32:
        lowerCamelCase__					:   Optional[int]      				=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
        lowerCamelCase__					:   Optional[Any]      				=	(32, 64, 128, 256)
        lowerCamelCase__					:   Optional[Any]      				=	('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
    lowerCamelCase__					:   Any      				=	torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
    lowerCamelCase__					:   List[str]      				=	model.state_dict()
    lowerCamelCase__					:   Tuple      				=	{
        '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_5536,
        'mid_block_type': 'MidResTemporalBlock1D',
        'act_fn': 'mish',
    }
    lowerCamelCase__					:   Optional[int]      				=	UNetaDModel(**_UpperCAmelCase				)
    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__					:   Tuple      				=	dict(zip(model.state_dict().keys()		,			hf_value_function.state_dict().keys()				)				)
    for k, v in mapping.items():
        lowerCamelCase__					:   str      				=	state_dict.pop(_UpperCAmelCase				)
    hf_value_function.load_state_dict(_UpperCAmelCase				)

    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(_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    lowerCamelCase__					:   Any      				=	{
        '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': 6_5536,
        '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__					:   Any      				=	torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'				)
    lowerCamelCase__					:   Any      				=	model
    lowerCamelCase__					:   Union[str, Any]      				=	UNetaDModel(**_UpperCAmelCase				)
    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__					:   Tuple      				=	dict(zip(state_dict.keys()		,			hf_value_function.state_dict().keys()				)				)
    for k, v in mapping.items():
        lowerCamelCase__					:   Tuple      				=	state_dict.pop(_UpperCAmelCase				)

    hf_value_function.load_state_dict(_UpperCAmelCase				)

    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(_UpperCAmelCase		,			_UpperCAmelCase				)


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












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch

    from transformers.activations import gelu_new, gelu_python, get_activation

@require_torch
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Tuple			)    ->			int:
     lowerCamelCase__					:   Optional[int]      				=	torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]			)
     lowerCamelCase__					:   Dict      				=	get_activation('gelu'			)
     self.assertTrue(torch.allclose(gelu_python(UpperCAmelCase			)    ,  torch_builtin(UpperCAmelCase			)			)			)
     self.assertFalse(torch.allclose(gelu_python(UpperCAmelCase			)    ,  gelu_new(UpperCAmelCase			)			)			)
 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]			)
     lowerCamelCase__					:   str      				=	get_activation('gelu'			)
     lowerCamelCase__					:   Tuple      				=	get_activation('gelu_10'			)

     lowerCamelCase__					:   Tuple      				=	torch_builtin(UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	geluaa(UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	torch.where(y_gelu_aa < 1_0.0    ,  1    ,  0			)

     self.assertTrue(torch.max(UpperCAmelCase			).item() == 1_0.0			)
     self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,  y_gelu_aa * clipped_mask			)			)
 def   A_			(   self						:   str			)    ->			List[str]:
     get_activation('gelu'			)
     get_activation('gelu_10'			)
     get_activation('gelu_fast'			)
     get_activation('gelu_new'			)
     get_activation('gelu_python'			)
     get_activation('gelu_pytorch_tanh'			)
     get_activation('linear'			)
     get_activation('mish'			)
     get_activation('quick_gelu'			)
     get_activation('relu'			)
     get_activation('sigmoid'			)
     get_activation('silu'			)
     get_activation('swish'			)
     get_activation('tanh'			)
     with self.assertRaises(UpperCAmelCase			):
         get_activation('bogus'			)
     with self.assertRaises(UpperCAmelCase			):
         get_activation(UpperCAmelCase			)







 def   A_			(   self						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	get_activation('gelu'			)
     lowerCamelCase__					:   Union[str, Any]      				=	1
     lowerCamelCase__					:   Optional[Any]      				=	get_activation('gelu'			)
     self.assertEqual(acta.a    ,  1			)
     with self.assertRaises(UpperCAmelCase			):
         lowerCamelCase__					:   str      				=	acta.a





