





'''simple docstring'''





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


__A             =       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      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







      '''simple docstring'''

      def __init__(self       :     List[Any] ,				UpperCAmelCase_       :     int = 101) ->List[Any]:



        '''simple docstring'''



        lowerCamelCase__:   Dict    						=length

      def __len__(self       :     Optional[Any]) ->List[str]:



        '''simple docstring'''



        return self.length







      def __getitem__(self       :     Optional[int] ,				UpperCAmelCase_       :     Tuple) ->int:



        '''simple docstring'''



        return i



     class      _SCREAMING_SNAKE_CASE		:







      '''simple docstring'''

      def __call__(self       :     Any ,				UpperCAmelCase_       :     Optional[Any]) ->Optional[int]:



        '''simple docstring'''



        return {"input_ids": torch.tensor(UpperCAmelCase_), "labels": torch.tensor(UpperCAmelCase_)}



     class      _SCREAMING_SNAKE_CASE		(			nn.Module					):







      '''simple docstring'''

      def __init__(self       :     Any) ->Any:



        '''simple docstring'''



        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        lowerCamelCase__:   Optional[Any]    						=nn.Linear(120 ,				80)







      def 							SCREAMING_SNAKE_CASE_      (self       :     int ,				UpperCAmelCase_       :     Dict ,				UpperCAmelCase_       :     Tuple=None) ->List[str]:



        '''simple docstring'''



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



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''

 @require_torch_neuroncore
 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
   lowerCamelCase__:   Optional[int]    						=self.get_auto_remove_tmp_dir()
   lowerCamelCase__:   str    						=F"""--output_dir {output_dir}""".split()
   lowerCamelCase__:   Optional[Any]    						=["torchrun"] + distributed_args + args
   execute_subprocess_async(UpperCAmelCase_ ,				env=self.get_env())
   # successful return here == success - any errors would have caused an error in the sub-call



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''

 @require_torch_multi_gpu
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
   lowerCamelCase__:   str    						=self.get_auto_remove_tmp_dir()
   lowerCamelCase__:   Any    						=F"""--output_dir {output_dir}""".split()
   lowerCamelCase__:   Any    						=["torchrun"] + distributed_args + args
   execute_subprocess_async(UpperCAmelCase_ ,				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

     __A             =       HfArgumentParser((TrainingArguments,))
     __A             =       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 [101, 40, 7]:
          __A             =       DummyDataset(dataset_length)





          def   lowerCAmelCase_				( __a	)  ->	Dict:







            """simple docstring"""





            lowerCamelCase__:   List[str]    						=list(range(len(__a	)	)	)
            lowerCamelCase__:   Tuple    						=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}

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

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

          __A             =       2

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

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






          __A             =       None








from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

__A             =       logging.get_logger(__name__)





def   lowerCAmelCase_				( __a	)  ->	List[List[ImageInput]]:







  """simple docstring"""





  if isinstance(__a			,					(list, tuple)	) and isinstance(videos[0]			,					(list, tuple)	) and is_valid_image(videos[0][0]	):
    return videos

  elif isinstance(__a			,					(list, tuple)	) and is_valid_image(videos[0]	):
    return [videos]

  elif is_valid_image(__a	):
    return [[videos]]

  raise ValueError(F"""Could not make batched video from {videos}"""	)






class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    ["pixel_values"]

 def __init__(self       :     Tuple ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BILINEAR ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Union[int, float] = 1 / 255 ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:   Optional[int]    						=size if size is not None else {"shortest_edge": 256}
   lowerCamelCase__:   Any    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   lowerCamelCase__:   Tuple    						=crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase__:   Any    						=get_size_dict(UpperCAmelCase_ ,				param_name="crop_size")

   lowerCamelCase__:   str    						=do_resize
   lowerCamelCase__:   Any    						=size
   lowerCamelCase__:   Any    						=do_center_crop
   lowerCamelCase__:   int    						=crop_size
   lowerCamelCase__:   int    						=resample
   lowerCamelCase__:   Optional[int]    						=do_rescale
   lowerCamelCase__:   int    						=rescale_factor
   lowerCamelCase__:   Dict    						=offset
   lowerCamelCase__:   List[Any]    						=do_normalize
   lowerCamelCase__:   Optional[int]    						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCamelCase__:   List[str]    						=image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     PILImageResampling = PILImageResampling.BILINEAR ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   Dict    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   if "shortest_edge" in size:
     lowerCamelCase__:   Union[str, Any]    						=get_resize_output_image_size(UpperCAmelCase_ ,				size["shortest_edge"] ,				default_to_square=UpperCAmelCase_)
   elif "height" in size and "width" in size:
     lowerCamelCase__:   int    						=(size["height"], size["width"])
   else:
     raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""")
   return resize(UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     str ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Dict[str, int] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     List[Any] ,				) ->np.ndarray:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=get_size_dict(UpperCAmelCase_)
   if "height" not in size or "width" not in size:
     raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""")
   return center_crop(UpperCAmelCase_ ,				size=(size["height"], size["width"]) ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any] ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[int, float] ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     Union[str, Any] ,				) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   str    						=image.astype(np.floataa)
   if offset:
     lowerCamelCase__:   List[Any]    						=image - (scale / 2)
   return rescale(UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple ,				UpperCAmelCase_       :     np.ndarray ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Union[float, List[float]] ,				UpperCAmelCase_       :     Optional[Union[str, ChannelDimension]] = None ,				**UpperCAmelCase_       :     Union[str, Any] ,				) ->np.ndarray:



   '''simple docstring'''



   return normalize(UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     ImageInput ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     PILImageResampling = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[ChannelDimension] = ChannelDimension.FIRST ,				) ->np.ndarray:



   '''simple docstring'''



   if do_resize and size is None or resample is None:
     raise ValueError("Size and resample must be specified if do_resize is True.")

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True.")

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True.")

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True.")

   if offset and not do_rescale:
     raise ValueError("For offset, do_rescale must also be set to True.")

   # All transformations expect numpy arrays.
   lowerCamelCase__:   List[str]    						=to_numpy_array(UpperCAmelCase_)

   if do_resize:
     lowerCamelCase__:   Optional[Any]    						=self.resize(image=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_)

   if do_center_crop:
     lowerCamelCase__:   str    						=self.center_crop(UpperCAmelCase_ ,				size=UpperCAmelCase_)

   if do_rescale:
     lowerCamelCase__:   List[str]    						=self.rescale(image=UpperCAmelCase_ ,				scale=UpperCAmelCase_ ,				offset=UpperCAmelCase_)

   if do_normalize:
     lowerCamelCase__:   Dict    						=self.normalize(image=UpperCAmelCase_ ,				mean=UpperCAmelCase_ ,				std=UpperCAmelCase_)

   lowerCamelCase__:   Any    						=to_channel_dimension_format(UpperCAmelCase_ ,				UpperCAmelCase_)
   return image







 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any] ,				UpperCAmelCase_       :     ImageInput ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     PILImageResampling = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Dict[str, int] = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     float = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     bool = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[float, List[float]]] = None ,				UpperCAmelCase_       :     Optional[Union[str, TensorType]] = None ,				UpperCAmelCase_       :     ChannelDimension = ChannelDimension.FIRST ,				**UpperCAmelCase_       :     List[str] ,				) ->PIL.Image.Image:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=do_resize if do_resize is not None else self.do_resize
   lowerCamelCase__:   Optional[int]    						=resample if resample is not None else self.resample
   lowerCamelCase__:   Optional[int]    						=do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase__:   List[str]    						=do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:   Dict    						=rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase__:   Union[str, Any]    						=offset if offset is not None else self.offset
   lowerCamelCase__:   Dict    						=do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase__:   Optional[int]    						=image_mean if image_mean is not None else self.image_mean
   lowerCamelCase__:   List[Any]    						=image_std if image_std is not None else self.image_std

   lowerCamelCase__:   List[Any]    						=size if size is not None else self.size
   lowerCamelCase__:   Optional[int]    						=get_size_dict(UpperCAmelCase_ ,				default_to_square=UpperCAmelCase_)
   lowerCamelCase__:   str    						=crop_size if crop_size is not None else self.crop_size
   lowerCamelCase__:   Optional[Any]    						=get_size_dict(UpperCAmelCase_ ,				param_name="crop_size")

   if not valid_images(UpperCAmelCase_):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray.")

   lowerCamelCase__:   str    						=make_batched(UpperCAmelCase_)

   lowerCamelCase__:   Any    						=[
       [
           self._preprocess_image(
               image=UpperCAmelCase_ ,				do_resize=UpperCAmelCase_ ,				size=UpperCAmelCase_ ,				resample=UpperCAmelCase_ ,				do_center_crop=UpperCAmelCase_ ,				crop_size=UpperCAmelCase_ ,				do_rescale=UpperCAmelCase_ ,				rescale_factor=UpperCAmelCase_ ,				offset=UpperCAmelCase_ ,				do_normalize=UpperCAmelCase_ ,				image_mean=UpperCAmelCase_ ,				image_std=UpperCAmelCase_ ,				data_format=UpperCAmelCase_ ,				)
           for img in video
       ]
       for video in videos
   ]

   lowerCamelCase__:   int    						={"pixel_values": videos}
   return BatchFeature(data=UpperCAmelCase_ ,				tensor_type=UpperCAmelCase_)

