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


snake_case__					:      Dict				      =  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						(	lowerCamelCase__	):







   '''simple docstring'''






   def __init__(			self				,		snake_case_ = 1_0_1						):




        '''simple docstring'''

        UpperCAmelCase_							:       Optional[Any]       =				length


   def __len__(			self						):




        '''simple docstring'''

        return self.length






   def __getitem__(			self				,		snake_case_						):




        '''simple docstring'''

        return i







 class 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   def __call__(			self				,		snake_case_						):




        '''simple docstring'''

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







 class 					__SCREAMING_SNAKE_CASE						(	nn.Module	):







   '''simple docstring'''






   def __init__(			self						):




        '''simple docstring'''

        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        UpperCAmelCase_							:       Tuple       =				nn.Linear(1_2_0				,		8_0						)






   def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_=None						):




        '''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						(	lowerCamelCase__	):







  '''simple docstring'''






  @require_torch_neuroncore
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase__	):







  '''simple docstring'''






  @require_torch_multi_gpu
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				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()
       UpperCAmelCase_							:       Dict       =				self.get_auto_remove_tmp_dir()
       UpperCAmelCase_							:       List[Any]       =				F'''--output_dir {output_dir}'''.split()
       UpperCAmelCase_							:       Optional[Any]       =				['torchrun'] + distributed_args + args
       execute_subprocess_async(lowercase__				,		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

 snake_case__					:      List[Any]				      =  HfArgumentParser((TrainingArguments,))
 snake_case__					:      Optional[int]				      =  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]:
  snake_case__					:      List[str]				      =  DummyDataset(dataset_length)


  def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]       ):



       """simple docstring"""





       UpperCAmelCase_							:       Tuple       =				list(range(len(A__       )       )       )
       UpperCAmelCase_							:       Dict       =				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}

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

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

  snake_case__					:      str				      =  2

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

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





  snake_case__					:      str				      =  None







'''simple docstring'''


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'				,		snake_case_				,		)
            UpperCAmelCase_							:       int       =				kwargs.pop('feature_extractor'						)

       UpperCAmelCase_							:       str       =				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(snake_case_				,		snake_case_						)


  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if text is None and visual_prompt is None and images is None:
            raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
            raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
            UpperCAmelCase_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                'pixel_values': image_features.pixel_values,
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       elif text is not None and images is not None:
            UpperCAmelCase_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.batch_decode(*snake_case_				,		**snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       return self.tokenizer.decode(*snake_case_				,		**snake_case_						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,		snake_case_				,		)
       return self.image_processor_class






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		snake_case_				,		)
       return self.image_processor






