




'''simple docstring'''


import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowerCAmelCase_      :  Union[str, Any]				     =  get_tests_dir('fixtures')







class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  mock.Mock()
           a_     :							Dict     						=  500
           a_     :							Any     						=  {}
           a_     :							int     						=  HTTPError
           a_     :							Optional[int]     						=  {}

           # Download this model to make sure it's in the cache.
           a_     :							Dict     						=  ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""       )
           # Under the mock environment we get a 500 error when trying to reach the model.
           with mock.patch("""requests.Session.request"""						,						return_value=lowerCamelCase__       ) as mock_head:
                a_     :							Dict     						=  ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""       )
                # This check we did call the fake head request
                mock_head.assert_called()






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  ViTImageProcessor.from_pretrained(
               """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           with self.assertRaises(lowerCamelCase__       ):
                # config is in subfolder, the following should not work without specifying the subfolder
                a_     :							List[str]     						=  AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""       )

           a_     :							Any     						=  AutoImageProcessor.from_pretrained(
               """hf-internal-testing/stable-diffusion-all-variants"""						,						subfolder="""feature_extractor"""       )

           self.assertIsNotNone(lowerCamelCase__       )







@is_staging_test
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      @classmethod
      def 							lowercase_						( cls   :  Tuple       ):


           '''simple docstring'''

           a_     :							str     						=  TOKEN
           HfFolder.save_token(lowerCamelCase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict       ):


           '''simple docstring'''

           try:
                delete_repo(token=cls._token						,						repo_id="""test-image-processor"""       )
           except HTTPError:
                pass

           try:
                delete_repo(token=cls._token						,						repo_id="""valid_org/test-image-processor-org"""       )
           except HTTPError:
                pass

           try:
                delete_repo(token=cls._token						,						repo_id="""test-dynamic-image-processor"""       )
           except HTTPError:
                pass






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  ViTImageProcessor.from_pretrained(lowerCamelCase__       )
           image_processor.push_to_hub("""test-image-processor"""						,						use_auth_token=self._token       )

           a_     :							str     						=  ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"       )
           for k, v in image_processor.__dict__.items():
                self.assertEqual(lowerCamelCase__						,						getattr(lowerCamelCase__						,						lowerCamelCase__       )       )

           # Reset repo
           delete_repo(token=self._token						,						repo_id="""test-image-processor"""       )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(
                    lowerCamelCase__						,						repo_id="""test-image-processor"""						,						push_to_hub=lowerCamelCase__						,						use_auth_token=self._token       )

           a_     :							Union[str, Any]     						=  ViTImageProcessor.from_pretrained(F"{USER}/test-image-processor"       )
           for k, v in image_processor.__dict__.items():
                self.assertEqual(lowerCamelCase__						,						getattr(lowerCamelCase__						,						lowerCamelCase__       )       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Tuple     						=  ViTImageProcessor.from_pretrained(lowerCamelCase__       )
           image_processor.push_to_hub("""valid_org/test-image-processor"""						,						use_auth_token=self._token       )

           a_     :							Any     						=  ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""       )
           for k, v in image_processor.__dict__.items():
                self.assertEqual(lowerCamelCase__						,						getattr(lowerCamelCase__						,						lowerCamelCase__       )       )

           # Reset repo
           delete_repo(token=self._token						,						repo_id="""valid_org/test-image-processor"""       )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(
                    lowerCamelCase__						,						repo_id="""valid_org/test-image-processor-org"""						,						push_to_hub=lowerCamelCase__						,						use_auth_token=self._token       )

           a_     :							List[Any]     						=  ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""       )
           for k, v in image_processor.__dict__.items():
                self.assertEqual(lowerCamelCase__						,						getattr(lowerCamelCase__						,						lowerCamelCase__       )       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           CustomImageProcessor.register_for_auto_class()
           a_     :							List[str]     						=  CustomImageProcessor.from_pretrained(lowerCamelCase__       )

           image_processor.push_to_hub("""test-dynamic-image-processor"""						,						use_auth_token=self._token       )

           # This has added the proper auto_map field to the config
           self.assertDictEqual(
               image_processor.auto_map						,						{"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}						,						)

           a_     :							Dict     						=  AutoImageProcessor.from_pretrained(
               F"{USER}/test-dynamic-image-processor"						,						trust_remote_code=lowerCamelCase__       )
           # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
           self.assertEqual(new_image_processor.__class__.__name__						,						"""CustomImageProcessor"""       )




"""simple docstring"""


import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Path       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		):
 if config_name_or_path is None:
  lowercase__       :       Tuple				=						"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

 if generator_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						question_encoder_name_or_path

 lowercase__       :       Any				=						RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

 # Save model.
 lowercase__       :       Union[str, Any]				=						RagConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						AutoConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__  )

 lowercase__       :       Union[str, Any]				=						gen_config
 lowercase__       :       Any				=						question_encoder_config

 lowercase__       :       str				=						model_class.from_pretrained_question_encoder_generator(
     lowerCamelCase__       ,		lowerCamelCase__       ,		config=lowerCamelCase__  )
 rag_model.save_pretrained(lowerCamelCase__  )

 # Sanity check.
 model_class.from_pretrained(lowerCamelCase__  )

 # Save tokenizers.
 lowercase__       :       Optional[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""  )
 lowercase__       :       Optional[int]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""  )


if __name__ == "__main__":
   __snake_case    					=					argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   __snake_case    					=					parser.parse_args()

   __snake_case    					=					Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )