

'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_UpperCAmelCase  :		Optional[Any]  =		logging.get_logger(__name__)




def 	UpperCamelCase   (       lowercase_						:    Optional[int]=None     ,					lowercase_						:    Union[str, Any]=None	)		->	Dict:




  '''simple docstring'''



  return field(default_factory=lambda: default     ,					metadata=lowercase_	)




@dataclass
class 						__magic_name__   :
 UpperCamelCase__     =       list_field(
     default=[] ,				metadata={
         'help': (
             'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
             ' of all available models'
         )
     } ,				)

 UpperCamelCase__     =       list_field(
     default=[8] ,				metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'} )

 UpperCamelCase__     =       list_field(
     default=[8, 32, 1_28, 5_12] ,				metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'} ,				)

 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'} ,				)
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'} ,				)
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Use FP16 to accelerate inference.'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Benchmark training of model'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Verbose memory tracing'} )
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'} ,				)
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={
         'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
     } ,				)
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Trace memory line by line'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Save result to a CSV file'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Save all print statements in a log file'} )
 UpperCamelCase__     =       field(default=__SCREAMING_SNAKE_CASE ,				metadata={'help': 'Whether to print environment information'} )
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={
         'help': (
             'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
             ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
             ' for debugging / testing and on TPU.'
         )
     } ,				)
 UpperCamelCase__     =       field(
     default=f"""inference_time_{round(time() )}.csv""" ,				metadata={'help': 'CSV filename used if saving time results to csv.'} ,				)
 UpperCamelCase__     =       field(
     default=f"""inference_memory_{round(time() )}.csv""" ,				metadata={'help': 'CSV filename used if saving memory results to csv.'} ,				)
 UpperCamelCase__     =       field(
     default=f"""train_time_{round(time() )}.csv""" ,				metadata={'help': 'CSV filename used if saving time results to csv for training.'} ,				)
 UpperCamelCase__     =       field(
     default=f"""train_memory_{round(time() )}.csv""" ,				metadata={'help': 'CSV filename used if saving memory results to csv for training.'} ,				)
 UpperCamelCase__     =       field(
     default=f"""env_info_{round(time() )}.csv""" ,				metadata={'help': 'CSV filename used if saving environment information.'} ,				)
 UpperCamelCase__     =       field(
     default=f"""log_{round(time() )}.csv""" ,				metadata={'help': 'Log filename used if print statements are saved in log.'} ,				)
 UpperCamelCase__     =       field(default=3 ,				metadata={'help': 'Times an experiment will be run.'} )
 UpperCamelCase__     =       field(
     default=__SCREAMING_SNAKE_CASE ,				metadata={
         'help': (
             'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
             ' model weights.'
         )
     } ,				)







 def 						_A(       self   ):
   warnings.warn(
       f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
       ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
       ''' to benchmark Transformer models.'''  , snake_case_  , )







 def 						_A(       self   ):
   return json.dumps(dataclasses.asdict(self   )  , indent=2   )







 @property
 def 						_A(       self   ):
   if len(self.models   ) <= 0:
     raise ValueError(
         '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
         ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''   )
   return self.models







 @property
 def 						_A(       self   ):
   if not self.multi_process:
     return False
   elif self.is_tpu:
     logger.info('''Multiprocessing is currently not possible on TPU.'''   )
     return False
   else:
     return True




"""simple docstring"""


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


A	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.ModuleList(__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = False       ,  __a = True       ,  ):
										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      snake_case							(	self       ,  __a       ,  __a = True       ,  __a = None       ,  __a = False       ,  __a = None       ,  ):
										__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      snake_case							(	cls       ,  __a       ,  **__a      ):
										__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      )




