



"""simple docstring"""






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class     UpperCAmelCase    (	unittest.TestCase  ):



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     # For consistency across different places the DisjunctiveConstraint is called,
     # dc.token_ids is a list of integers. It is also initialized only by integers.

     _snake_case   	=      [[1, 2, 4], [1, 2, 3, 4]]
     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)
     self.assertTrue(isinstance(dc.token_ids							,       __lowerCamelCase				)				)

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]				)				)

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint([torch.LongTensor([1, 2, 4]				), torch.LongTensor([1, 2, 3, 4, 5]				)]				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # We can't have constraints that are complete subsets of another. This leads to a preverse
     # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
     # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
     # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
     # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
     _snake_case   	=      [[1, 2], [1, 2, 3, 4]]

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint(__lowerCamelCase				)  # fails here



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      [[1, 2, 3], [1, 2, 4]]

     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     _snake_case   	=      stepped is True and completed is False and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     _snake_case   	=      stepped is True and completed is False and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(3				)
     _snake_case   	=      stepped is True and completed is True and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.current_seq == [1, 2, 3]				)





   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(4				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2, 4]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(5				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.current_seq == [1, 2, 4, 5]				)

     dc.reset()

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.remaining() == 3				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.remaining() == 2				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(5				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.remaining() == 0				)
     self.assertTrue(dc.current_seq == [1, 2, 5]				)







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	)     ->List[Any]:




			'''simple docstring'''


			if hor == 128:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 64, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			SCREAMING_SNAKE_CASE     						= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
			SCREAMING_SNAKE_CASE     						= model.state_dict()
			SCREAMING_SNAKE_CASE     						= {
			    '''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''',
			}
			SCREAMING_SNAKE_CASE     						= 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     						= dict(zip(model.state_dict().keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= 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      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= {
			    '''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''',
			}

			SCREAMING_SNAKE_CASE     						= torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''	)
			SCREAMING_SNAKE_CASE     						= model
			SCREAMING_SNAKE_CASE     						= 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     						= dict(zip(state_dict.keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= 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(3_2)
							# unet(128)
							value_function()