




import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase__    			=			logging.get_logger(__name__)
class 			a__    (					snake_case__      ):






						def __init__(   self		,			_A			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       nn.ModuleList(_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = False		,			_A = True		,			):




											"""simple docstring"""



											for i, (image, scale, controlnet) in enumerate(zip(_A		,			_A		,			self.nets			)			):
																__lowerCAmelCase   ,							__lowerCAmelCase				   =       controlnet(
																    _A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			_A		,			)

																# merge samples
																if i == 0:
																					__lowerCAmelCase   ,							__lowerCAmelCase				   =       down_samples, mid_sample
																else:
																					__lowerCAmelCase				   =       [
																					    samples_prev + samples_curr
																					    for samples_prev, samples_curr in zip(_A		,			_A			)
																					]
																					mid_block_res_sample += mid_sample

											return down_block_res_samples, mid_block_res_sample






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = True		,			_A = None		,			_A = False		,			_A = None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       0
											__lowerCAmelCase				   =       save_directory
											for controlnet in self.nets:
																controlnet.save_pretrained(
																    _A		,			is_main_process=_A		,			save_function=_A		,			safe_serialization=_A		,			variant=_A		,			)

																idx += 1
																__lowerCAmelCase				   =       model_path_to_save + f"""_{idx}"""






						@classmethod
						def 				__SCREAMING_SNAKE_CASE(   cls		,			_A		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       0
											__lowerCAmelCase				   =       []

											# load controlnet and append to list until no controlnet directory exists anymore
											# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
											# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
											__lowerCAmelCase				   =       pretrained_model_path
											while os.path.isdir(_A			):
																__lowerCAmelCase				   =       ControlNetModel.from_pretrained(_A		,			**_A			)
																controlnets.append(_A			)

																idx += 1
																__lowerCAmelCase				   =       pretrained_model_path + f"""_{idx}"""

											logger.info(f"""{len(_A			)} controlnets loaded from {pretrained_model_path}."""			)

											if len(_A			) == 0:
																raise ValueError(
																    f"""No ControlNets found under {os.path.dirname(_A			)}. Expected at least {pretrained_model_path + '_0'}."""			)

											return cls(_A			)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    """decision_transformer"""
						_a							:    Optional[int]					=    ["""past_key_values"""]
						_a							:    Dict					=    {
						    """max_position_embeddings""": """n_positions""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(   self		,			_A=1_7		,			_A=4		,			_A=1_2_8		,			_A=4_0_9_6		,			_A=True		,			_A=1		,			_A=1_0_2_4		,			_A=3		,			_A=1		,			_A=None		,			_A="relu"		,			_A=0.1		,			_A=0.1		,			_A=0.1		,			_A=1E-5		,			_A=0.02		,			_A=True		,			_A=True		,			_A=5_0_2_5_6		,			_A=5_0_2_5_6		,			_A=False		,			_A=False		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       state_dim
											__lowerCAmelCase				   =       act_dim
											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       max_ep_len
											__lowerCAmelCase				   =       action_tanh
											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       n_positions
											__lowerCAmelCase				   =       n_layer
											__lowerCAmelCase				   =       n_head
											__lowerCAmelCase				   =       n_inner
											__lowerCAmelCase				   =       activation_function
											__lowerCAmelCase				   =       resid_pdrop
											__lowerCAmelCase				   =       embd_pdrop
											__lowerCAmelCase				   =       attn_pdrop
											__lowerCAmelCase				   =       layer_norm_epsilon
											__lowerCAmelCase				   =       initializer_range
											__lowerCAmelCase				   =       scale_attn_weights
											__lowerCAmelCase				   =       use_cache
											__lowerCAmelCase				   =       scale_attn_by_inverse_layer_idx
											__lowerCAmelCase				   =       reorder_and_upcast_attn

											__lowerCAmelCase				   =       bos_token_id
											__lowerCAmelCase				   =       eos_token_id

											super().__init__(bos_token_id=_A		,			eos_token_id=_A		,			**_A			)




