


from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCamelCase__							:						Any							     =   logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer
   class 		__magic_name__						(snake_case_				):





        '''simple docstring'''



        def __init__(					self:Optional[int]     ,		_a:Dict = 1_01   ):
          snake_case__						= length

        def __len__(					self:Union[str, Any]   ):
          return self.length







        def __getitem__(					self:List[str]     ,		_a:List[Any]   ):
          return i




   class 		__magic_name__						:





        '''simple docstring'''



        def __call__(					self:Dict     ,		_a:Optional[int]   ):
          return {"input_ids": torch.tensor(_a   ), "labels": torch.tensor(_a   )}




   class 		__magic_name__						(nn.Module				):





        '''simple docstring'''



        def __init__(					self:int   ):
          super().__init__()
          # Add some (unused) params otherwise DDP will complain.
          snake_case__						= nn.Linear(1_20     ,		80   )







        def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Tuple     ,		_a:List[str]=None   ):







          if labels is not None:
            return torch.tensor(0.0     ,		device=input_ids.device   ), input_ids
          else:
            return input_ids
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     @require_torch_neuroncore
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= F"""--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        """.split()
       snake_case__						= self.get_auto_remove_tmp_dir()
       snake_case__						= F"""--output_dir {output_dir}""".split()
       snake_case__						= ['torchrun'] + distributed_args + args
       execute_subprocess_async(_a     ,		env=self.get_env()   )
       # successful return here == success - any errors would have caused an error in the sub-call




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     @require_torch_multi_gpu
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= F"""--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        """.split()
       snake_case__						= self.get_auto_remove_tmp_dir()
       snake_case__						= F"""--output_dir {output_dir}""".split()
       snake_case__						= ['torchrun'] + distributed_args + args
       execute_subprocess_async(_a     ,		env=self.get_env()   )
       # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
   # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
   #
   # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

   lowerCamelCase__							:						Any							     =   HfArgumentParser((TrainingArguments,))
   lowerCamelCase__							:						str							     =   parser.parse_args_into_dataclasses()[0]

   logger.warning(
       F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
       F"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
   )

   # Essentially, what we want to verify in the distributed case is that we get all samples back,
   # in the right order. (this is crucial for prediction for instance)
   for dataset_length in [1_0_1, 4_0, 7]:
      lowerCamelCase__							:						List[Any]							     =   DummyDataset(dataset_length)







      def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Tuple:
        snake_case__						= list(range(len(__UpperCamelCase				)				)				)
        snake_case__						= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
        if not success and training_args.local_rank == 0:
          logger.warning(
              '''Predictions and/or labels do not match expected results:\n  - predictions: '''
              F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""				)
        return {"success": success}

      lowerCamelCase__							:						Tuple							     =   Trainer(
          model=DummyModel(),
          args=training_args,
          data_collator=DummyDataCollator(),
          eval_dataset=dataset,
          compute_metrics=compute_metrics,
      )
      lowerCamelCase__							:						Optional[Any]							     =   trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      lowerCamelCase__							:						List[str]							     =   trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)

      lowerCamelCase__							:						Tuple							     =   2

      lowerCamelCase__							:						Optional[int]							     =   trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      lowerCamelCase__							:						Any							     =   trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)


      lowerCamelCase__							:						Any							     =   None







'''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


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      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  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             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        UpperCamelCase_							(      self,							A,							A = True,							A = None,							A = False,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]

             # 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`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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  )


