


'''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)
SCREAMING_SNAKE_CASE_      =			logging.getLogger()

SCREAMING_SNAKE_CASE_      =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       os.makedirs(SCREAMING_SNAKE_CASE__				,							exist_ok=SCREAMING_SNAKE_CASE__						)
       __a       =   {"""source""": """What is love ?""", """target""": """life"""}
       __a       =   {"""train""": 1_2, """val""": 2, """test""": 2}
       for split in ["train", "test", "val"]:
            for field in ["source", "target"]:
                 __a       =   """\n""".join([contents[field]] * n_lines[split]						)
                 with open(os.path.join(SCREAMING_SNAKE_CASE__				,							f'''{split}.{field}'''						)				,							"""w"""						) as f:
                      f.write(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							str = "pytorch"						):





       '''simple docstring'''

       __a       =   self.get_auto_remove_tmp_dir()
       __a       =   os.path.join(SCREAMING_SNAKE_CASE__				,							"""output"""						)
       __a       =   os.path.join(SCREAMING_SNAKE_CASE__				,							"""data"""						)
       self._create_dummy_data(data_dir=SCREAMING_SNAKE_CASE__						)

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

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

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

  @require_torch_gpu
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self._run_finetune(gpus=1						)
       self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]				,							0.2						)

  @require_torch_multi_gpu
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self._run_finetune(gpus=2						)
       self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]				,							0.2						)

  @require_torch_gpu
  @require_ray
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   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 __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self._run_finetune(gpus=1				,							distributed_retriever="""ray"""						)
       self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]				,							0.2						)










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_      =			{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure)






