






'''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_					=	logging.getLogger()

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






class        __SCREAMING_SNAKE_CASE (     snake_case_			):




 def        __magic_name__		( self		:							str   , __lowercase		:							Any )		->  Union[str, Any]:
       os.makedirs(__lowercase   , exist_ok=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													={'''source''': '''What is love ?''', '''target''': '''life'''}
       SCREAMING_SNAKE_CASE__       :			Tuple													={'''train''': 12, '''val''': 2, '''test''': 2}
       for split in ["train", "test", "val"]:
             for field in ["source", "target"]:
                   SCREAMING_SNAKE_CASE__       :			List[Any]													='''\n'''.join([contents[field]] * n_lines[split] )
                   with open(os.path.join(__lowercase   , F"{split}.{field}" )   , '''w''' ) as f:
                         f.write(__lowercase )




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							Dict   , __lowercase		:							Tuple = "pytorch" )		->  Optional[int]:
       SCREAMING_SNAKE_CASE__       :			Dict													=self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE__       :			Tuple													=os.path.join(__lowercase   , '''output''' )
       SCREAMING_SNAKE_CASE__       :			str													=os.path.join(__lowercase   , '''data''' )
       self._create_dummy_data(data_dir=__lowercase )

       SCREAMING_SNAKE_CASE__       :			int													=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''' )

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

       SCREAMING_SNAKE_CASE__       :			List[Any]													=os.path.join(__lowercase   , '''metrics.json''' )
       with open(__lowercase ) as f:
             SCREAMING_SNAKE_CASE__       :			int													=json.load(__lowercase )
       return result




 @require_torch_gpu
 def        __magic_name__		( self		:							Dict )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self._run_finetune(gpus=1 )
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']   , 0.2 )




 @require_torch_multi_gpu
 def        __magic_name__		( self		:							Any )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=self._run_finetune(gpus=2 )
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']   , 0.2 )




 @require_torch_gpu
 @require_ray
 def        __magic_name__		( self		:							Union[str, Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=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        __magic_name__		( self		:							Union[str, Any] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self._run_finetune(gpus=1   , distributed_retriever='''ray''' )
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']   , 0.2 )







'''simple docstring'''
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a_					=	'src/diffusers'

# Matches is_xxx_available()
a_					=	re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
a_					=	re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


a_					=	'\n{0} = None\n'

a_					=	'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


a_					=	'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'





def 	_a(					UpperCamelCase__			:  Any							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=_re_backend.findall(UpperCamelCase__							)
      if len(UpperCamelCase__							) == 0:
            return None

      return "_and_".join(UpperCamelCase__							)





def 	_a(					):




      '''simple docstring'''






      with open(os.path.join(UpperCamelCase__,		'''__init__.py'''							),		'''r''',		encoding='''utf-8''',		newline='''\n'''							) as f:
            SCREAMING_SNAKE_CASE__       :			List[str]													=f.readlines()

      # Get to the point we do the actual imports for type checking
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=0
      SCREAMING_SNAKE_CASE__       :			List[str]													={}
      # Go through the end of the file
      while line_index < len(UpperCamelCase__							):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            SCREAMING_SNAKE_CASE__       :			List[Any]													=find_backend(lines[line_index]							)
            if backend is not None:
                  while not lines[line_index].startswith('''else:'''							):
                        line_index += 1
                  line_index += 1
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=[]
                  # Until we unindent, add backend objects to the list
                  while line_index < len(UpperCamelCase__							) and len(lines[line_index]							) > 1:
                        SCREAMING_SNAKE_CASE__       :			Optional[Any]													=lines[line_index]
                        SCREAMING_SNAKE_CASE__       :			Optional[Any]													=_re_single_line_import.search(UpperCamelCase__							)
                        if single_line_import_search is not None:
                              objects.extend(single_line_import_search.groups()[0].split(''', '''							)							)
                        elif line.startswith(''' ''' * 8							):
                              objects.append(line[8:-2]							)
                        line_index += 1

                  if len(UpperCamelCase__							) > 0:
                        SCREAMING_SNAKE_CASE__       :			Any													=objects
            else:
                  line_index += 1

      return backend_specific_objects





def 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      if name.isupper():
            return DUMMY_CONSTANT.format(UpperCamelCase__							)
      elif name.islower():
            return DUMMY_FUNCTION.format(UpperCamelCase__,		UpperCamelCase__							)
      else:
            return DUMMY_CLASS.format(UpperCamelCase__,		UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  Any=None							):




      '''simple docstring'''






      if backend_specific_objects is None:
            SCREAMING_SNAKE_CASE__       :			int													=read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      SCREAMING_SNAKE_CASE__       :			Optional[int]													={}

      for backend, objects in backend_specific_objects.items():
            SCREAMING_SNAKE_CASE__       :			Tuple													='''[''' + ''', '''.join(f"\"{b}\"" for b in backend.split('''_and_'''							)							) + ''']'''
            SCREAMING_SNAKE_CASE__       :			List[str]													='''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(UpperCamelCase__,		UpperCamelCase__							) for o in objects]							)
            SCREAMING_SNAKE_CASE__       :			Tuple													=dummy_file

      return dummy_files





def 	_a(					UpperCamelCase__			:  Any=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      SCREAMING_SNAKE_CASE__       :			List[str]													={'''torch''': '''pt'''}

      # Locate actual dummy modules and read their content.
      SCREAMING_SNAKE_CASE__       :			List[str]													=os.path.join(UpperCamelCase__,		'''utils'''							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={
          backend: os.path.join(UpperCamelCase__,		f"dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py"							)
          for backend in dummy_files.keys()
      }

      SCREAMING_SNAKE_CASE__       :			str													={}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(UpperCamelCase__							):
                  with open(UpperCamelCase__,		'''r''',		encoding='''utf-8''',		newline='''\n'''							) as f:
                        SCREAMING_SNAKE_CASE__       :			List[Any]													=f.read()
            else:
                  SCREAMING_SNAKE_CASE__       :			int													=''''''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f"Updating diffusers.utils.dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py as the main "
                            '''__init__ has new objects.'''							)
                        with open(dummy_file_paths[backend],		'''w''',		encoding='''utf-8''',		newline='''\n'''							) as f:
                              f.write(dummy_files[backend]							)
                  else:
                        raise ValueError(
                            '''The main __init__ has objects that are not present in '''
                            f"diffusers.utils.dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py. Run `make fix-copies` "
                            '''to fix this.'''							)


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  a_					=	parser.parse_args()

  check_dummies(args.fix_and_overwrite)