


'''simple docstring'''



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__UpperCAmelCase    :Dict				       =     logging.get_logger(__name__)



class 			a       :


					"""simple docstring"""






					def __init__(       self  :	Optional[Any]							,				snake_case  :	Tuple							,				snake_case  :	str    )		->							Optional[int]:
										__UpperCAmelCase			:				str													=    question_encoder
										__UpperCAmelCase			:				Optional[Any]													=    generator
										__UpperCAmelCase			:				Any													=    self.question_encoder

					def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]    )		->							str:
										if os.path.isfile(snake_case    ):
															raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'    )
										os.makedirs(snake_case							,				exist_ok=snake_case    )
										__UpperCAmelCase			:				List[str]													=    os.path.join(snake_case							,				'''question_encoder_tokenizer'''    )
										__UpperCAmelCase			:				Tuple													=    os.path.join(snake_case							,				'''generator_tokenizer'''    )
										self.question_encoder.save_pretrained(snake_case    )
										self.generator.save_pretrained(snake_case    )

					@classmethod
					def 				lowerCamelCase__				(       cls  :	int							,				snake_case  :	List[str]							,				**snake_case  :	Any    )		->							Union[str, Any]:
										# dynamically import AutoTokenizer
										from ..auto.tokenization_auto import AutoTokenizer

										__UpperCAmelCase			:				Tuple													=    kwargs.pop('''config'''							,				snake_case    )

										if config is None:
															__UpperCAmelCase			:				Any													=    RagConfig.from_pretrained(snake_case    )

										__UpperCAmelCase			:				List[str]													=    AutoTokenizer.from_pretrained(
										    snake_case							,				config=config.question_encoder							,				subfolder='''question_encoder_tokenizer'''    )
										__UpperCAmelCase			:				Tuple													=    AutoTokenizer.from_pretrained(
										    snake_case							,				config=config.generator							,				subfolder='''generator_tokenizer'''    )
										return cls(question_encoder=snake_case							,				generator=snake_case    )

					def __call__(       self  :	Optional[int]							,				*snake_case  :	str							,				**snake_case  :	Any    )		->							str:
										return self.current_tokenizer(*snake_case							,				**snake_case    )

					def 				lowerCamelCase__				(       self  :	List[Any]							,				*snake_case  :	Optional[int]							,				**snake_case  :	Optional[Any]    )		->							int:
										return self.generator.batch_decode(*snake_case							,				**snake_case    )

					def 				lowerCamelCase__				(       self  :	str							,				*snake_case  :	Dict							,				**snake_case  :	Optional[int]    )		->							List[str]:
										return self.generator.decode(*snake_case							,				**snake_case    )

					def 				lowerCamelCase__				(       self  :	int    )		->							int:
										__UpperCAmelCase			:				Dict													=    self.question_encoder

					def 				lowerCamelCase__				(       self  :	Any    )		->							Union[str, Any]:
										__UpperCAmelCase			:				Any													=    self.generator






					def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	List[str]							,				snake_case  :	Optional[List[str]] = None							,				snake_case  :	Optional[int] = None							,				snake_case  :	Optional[int] = None							,				snake_case  :	str = "longest"							,				snake_case  :	str = None							,				snake_case  :	bool = True							,				**snake_case  :	Optional[Any]							,				)		->							BatchEncoding:
										warnings.warn(
										    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
										    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
										    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
										    '''details'''							,				snake_case							,				)
										if max_length is None:
															__UpperCAmelCase			:				str													=    self.current_tokenizer.model_max_length
										__UpperCAmelCase			:				List[Any]													=    self(
										    snake_case							,				add_special_tokens=snake_case							,				return_tensors=snake_case							,				max_length=snake_case							,				padding=snake_case							,				truncation=snake_case							,				**snake_case							,				)
										if tgt_texts is None:
															return model_inputs
										# Process tgt_texts
										if max_target_length is None:
															__UpperCAmelCase			:				List[Any]													=    self.current_tokenizer.model_max_length
										__UpperCAmelCase			:				Optional[int]													=    self(
										    text_target=snake_case							,				add_special_tokens=snake_case							,				return_tensors=snake_case							,				padding=snake_case							,				max_length=snake_case							,				truncation=snake_case							,				**snake_case							,				)
										__UpperCAmelCase			:				List[str]													=    labels['''input_ids''']
										return model_inputs



'''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 			_a			(		_lowercase      :			Any						):





     '''simple docstring'''






     if hor == 128:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Union[str, Any]													=    (32, 128, 256)
          __UpperCAmelCase			:				str													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

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

     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(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    {
         '''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''',
     }

     __UpperCAmelCase			:				str													=    torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''						)
     __UpperCAmelCase			:				Optional[Any]													=    model
     __UpperCAmelCase			:				Optional[int]													=    UNetaDModel(**_lowercase						)
     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			:				Union[str, Any]													=    dict(zip(state_dict.keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				Dict													=    state_dict.pop(_lowercase						)

     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)


if __name__ == "__main__":
      unet(3_2)
      # unet(128)
      value_function()