





'''simple docstring'''






import warnings

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




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      str											=       ['''image_processor''', '''tokenizer''']
      _lowercase:      int											=       '''CLIPImageProcessor'''
      _lowercase:      Union[str, Any]											=       ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




      def __init__(      self      :			Dict      ,      __snake_case      :			str=None      ,      __snake_case      :			Optional[Any]=None      ,      **__snake_case      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							None
            if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""      ,      __UpperCAmelCase      ,      )
                  _lowerCAmelCase												=							kwargs.pop("""feature_extractor"""  )

            _lowerCAmelCase												=							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__(__UpperCAmelCase      ,      __UpperCAmelCase  )




      def __call__(      self      :			List[str]      ,      __snake_case      :			Any=None      ,      __snake_case      :			Dict=None      ,      __snake_case      :			Any=None      ,      **__snake_case      :			str  )    ->				Optional[Any]:


            if text is None and images is None:
                  raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

            if text is not None:
                  _lowerCAmelCase												=							self.tokenizer(__UpperCAmelCase      ,      return_tensors=__UpperCAmelCase      ,      **__UpperCAmelCase  )

            if images is not None:
                  _lowerCAmelCase												=							self.image_processor(__UpperCAmelCase      ,      return_tensors=__UpperCAmelCase      ,      **__UpperCAmelCase  )

            if text is not None and images is not None:
                  _lowerCAmelCase												=							image_features.pixel_values
                  return encoding
            elif text is not None:
                  return encoding
            else:
                  return BatchEncoding(data=dict(**__UpperCAmelCase  )      ,      tensor_type=__UpperCAmelCase  )




      def     lowercase__   (      self      :			Tuple      ,      *__snake_case      :			List[str]      ,      **__snake_case      :			Union[str, Any]  )    ->				Union[str, Any]:
            return self.tokenizer.batch_decode(*__UpperCAmelCase      ,      **__UpperCAmelCase  )




      def     lowercase__   (      self      :			Tuple      ,      *__snake_case      :			str      ,      **__snake_case      :			Tuple  )    ->				Any:
            return self.tokenizer.decode(*__UpperCAmelCase      ,      **__UpperCAmelCase  )




      @property
      def     lowercase__   (      self      :			int  )    ->				List[str]:
            _lowerCAmelCase												=							self.tokenizer.model_input_names
            _lowerCAmelCase												=							self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




      @property
      def     lowercase__   (      self      :			Optional[int]  )    ->				List[str]:
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,      __UpperCAmelCase      ,      )
            return self.image_processor_class




      @property
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Optional[int]:
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,      __UpperCAmelCase      ,      )
            return self.image_processor













'''simple docstring'''






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
A__    :       Any              =logging.getLogger()

A__    :       int              =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 			UpperCAmelCase					(      snake_case_       ):




      def     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			Any  )    ->				int:
            os.makedirs(__snake_case      ,      exist_ok=__snake_case  )
            _lowerCAmelCase												=							{"""source""": """What is love ?""", """target""": """life"""}
            _lowerCAmelCase												=							{"""train""": 12, """val""": 2, """test""": 2}
            for split in ["train", "test", "val"]:
                  for field in ["source", "target"]:
                        _lowerCAmelCase												=							"""\n""".join([contents[field]] * n_lines[split]  )
                        with open(os.path.join(__snake_case      ,      f"{split}.{field}"  )      ,      """w"""  ) as f:
                              f.write(__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			int      ,      __snake_case      :			str = "pytorch"  )    ->				int:
            _lowerCAmelCase												=							self.get_auto_remove_tmp_dir()
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """output"""  )
            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """data"""  )
            self._create_dummy_data(data_dir=__snake_case  )

            _lowerCAmelCase												=							f"\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            ".split()

            if gpus > 0:
                  testargs.append(f"--gpus={gpus}"  )
                  if is_apex_available():
                        testargs.append("""--fp16"""  )
            else:
                  testargs.append("""--gpus=0"""  )
                  testargs.append("""--distributed_backend=ddp_cpu"""  )
                  testargs.append("""--num_processes=2"""  )

            _lowerCAmelCase												=							[sys.executable, str(Path(finetune_rag.__file__  ).resolve()  )] + testargs
            execute_subprocess_async(__snake_case      ,      env=self.get_env()  )

            _lowerCAmelCase												=							os.path.join(__snake_case      ,      """metrics.json"""  )
            with open(__snake_case  ) as f:
                  _lowerCAmelCase												=							json.load(__snake_case  )
            return result




      @require_torch_gpu
      def     lowercase__   (      self      :			Dict  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_multi_gpu
      def     lowercase__   (      self      :			int  )    ->				Dict:
            _lowerCAmelCase												=							self._run_finetune(gpus=2  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_gpu
      @require_ray
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1      ,      distributed_retriever="""ray"""  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )




      @require_torch_multi_gpu
      @require_ray
      def     lowercase__   (      self      :			int  )    ->				List[str]:
            _lowerCAmelCase												=							self._run_finetune(gpus=1      ,      distributed_retriever="""ray"""  )
            self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]      ,      0.2  )






