






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    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



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

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





						"""simple docstring"""



						_UpperCAmelCase									=			{
						    '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',
						}

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

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








import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      TransfoXLTokenizer
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase									=			[
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['vocab_file']    )
							with open(self.vocab_file       ,    'w'       ,    encoding='utf-8'    ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<unk> UNwanted , running'
							_UpperCAmelCase									=			'<unk> unwanted, running'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(vocab_file=self.vocab_file       ,    lower_case=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('<unk> UNwanted , running'    )
							self.assertListEqual(UpperCAmelCase       ,    ['<unk>', 'unwanted', ',', 'running']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [0, 4, 8, 7]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['hello', '!', 'how', 'are', 'you', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )
							_UpperCAmelCase									=			'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							_UpperCAmelCase									=			[
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(UpperCAmelCase    )       ,    UpperCAmelCase    )

							self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			len(UpperCAmelCase    )

							tokenizer.add_tokens(['new1', 'new2']    )
							tokenizer.move_added_token('new1'       ,    1    )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(UpperCAmelCase    )       ,    original_len + 2    )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'    )       ,    [1]    )
							self.assertEqual(tokenizer.decode([1]    )       ,    'new1'    )
