





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  ['''image_processor''', '''tokenizer''']
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  '''BlipImageProcessor'''
       SCREAMING_SNAKE_CASE_      : Any      =  '''AutoTokenizer'''







       def __init__(    self	:	Tuple						,__A	:	Tuple						,__A	:	Union[str, Any]						,__A	:	Optional[Any]  )						->					Tuple:
         super().__init__(__A						,__A  )

         # add QFormer tokenizer
         _lowercase							= qformer_tokenizer







       def __call__(    self	:	List[Any]						,__A	:	ImageInput = None						,__A	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,__A	:	bool = True						,__A	:	Union[bool, str, PaddingStrategy] = False						,__A	:	Union[bool, str, TruncationStrategy] = None						,__A	:	Optional[int] = None						,__A	:	int = 0						,__A	:	Optional[int] = None						,__A	:	Optional[bool] = None						,__A	:	bool = False						,__A	:	bool = False						,__A	:	bool = False						,__A	:	bool = False						,__A	:	bool = False						,__A	:	bool = True						,__A	:	Optional[Union[str, TensorType]] = None						,**__A	:	Union[str, Any]						,)						->					BatchFeature:
         if images is None and text is None:
           raise ValueError('You have to specify at least images or text.'  )

         _lowercase							= BatchFeature()

         if text is not None:
           _lowercase							= self.tokenizer(
               text=__A						,add_special_tokens=__A						,padding=__A						,truncation=__A						,max_length=__A						,stride=__A						,pad_to_multiple_of=__A						,return_attention_mask=__A						,return_overflowing_tokens=__A						,return_special_tokens_mask=__A						,return_offsets_mapping=__A						,return_token_type_ids=__A						,return_length=__A						,verbose=__A						,return_tensors=__A						,**__A						,)
           encoding.update(__A  )
           _lowercase							= self.qformer_tokenizer(
               text=__A						,add_special_tokens=__A						,padding=__A						,truncation=__A						,max_length=__A						,stride=__A						,pad_to_multiple_of=__A						,return_attention_mask=__A						,return_overflowing_tokens=__A						,return_special_tokens_mask=__A						,return_offsets_mapping=__A						,return_token_type_ids=__A						,return_length=__A						,verbose=__A						,return_tensors=__A						,**__A						,)
           _lowercase							= qformer_text_encoding.pop('input_ids'  )
           _lowercase							= qformer_text_encoding.pop('attention_mask'  )

         if images is not None:
           _lowercase							= self.image_processor(__A						,return_tensors=__A  )
           encoding.update(__A  )

         return encoding







       def    __UpperCAmelCase (    self	:	Optional[Any]						,*__A	:	str						,**__A	:	int  )						->					List[Any]:
         return self.tokenizer.batch_decode(*__A						,**__A  )







       def    __UpperCAmelCase (    self	:	Any						,*__A	:	Tuple						,**__A	:	Union[str, Any]  )						->					Dict:
         return self.tokenizer.decode(*__A						,**__A  )







       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def    __UpperCAmelCase (    self	:	Dict  )						->					str:
         _lowercase							= self.tokenizer.model_input_names
         _lowercase							= self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







       def    __UpperCAmelCase (    self	:	int						,__A	:	Optional[int]						,**__A	:	Any  )						->					List[Any]:
         if os.path.isfile(__A  ):
           raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""  )
         os.makedirs(__A						,exist_ok=__A  )
         _lowercase							= os.path.join(__A						,'qformer_tokenizer'  )
         self.qformer_tokenizer.save_pretrained(__A  )
         return super().save_pretrained(__A						,**__A  )







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[str]						,__A	:	List[Any]						,**__A	:	Optional[int]  )						->					Union[str, Any]:
         _lowercase							= AutoTokenizer.from_pretrained(__A						,subfolder='qformer_tokenizer'  )
         _lowercase							= cls._get_arguments_from_pretrained(__A						,**__A  )
         args.append(__A  )
         return cls(*__A  )






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 				=       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 				A_				(      UpperCAmelCase	):







             """simple docstring"""





             def __init__(    self	:	str						,__A	:	int = 101  )						->					Optional[int]:
               _lowercase							= length







             def __len__(    self	:	List[Any]  )						->					Any:
               return self.length







             def __getitem__(    self	:	List[Any]						,__A	:	Optional[int]  )						->					int:
               return i





      class 				A_				:







             """simple docstring"""





             def __call__(    self	:	str						,__A	:	Union[str, Any]  )						->					Any:
               return {"input_ids": torch.tensor(__A  ), "labels": torch.tensor(__A  )}





      class 				A_				(      nn.Module	):







             """simple docstring"""





             def __init__(    self	:	Dict  )						->					Any:
               super().__init__()
               # Add some (unused) params otherwise DDP will complain.
               _lowercase							= nn.Linear(120						,80  )







             def    __UpperCAmelCase (    self	:	Dict						,__A	:	Dict						,__A	:	Any=None  )						->					Tuple:
               if labels is not None:
                 return torch.tensor(0.0						,device=input_ids.device  ), input_ids
               else:
                 return input_ids





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_neuroncore
       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['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 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_multi_gpu
       def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
         _lowercase							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['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

      snake_case 				=       HfArgumentParser((TrainingArguments,))
      snake_case 				=       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]:
            snake_case 				=       DummyDataset(dataset_length)

            def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :EvalPrediction     )      ->						Dict:
              _lowercase							= list(range(len(snake_case__     )     )     )
              _lowercase							= 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 				=       Trainer(
                model=DummyModel(),
                args=training_args,
                data_collator=DummyDataCollator(),
                eval_dataset=dataset,
                compute_metrics=compute_metrics,
            )
            snake_case 				=       trainer.evaluate()
            logger.info(metrics)
            if metrics["eval_success"] is not True:
                  logger.error(metrics)
                  exit(1)

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

            snake_case 				=       2

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

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







            snake_case 				=       None