





import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
    import jax

    from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
    from transformers.models.bert.modeling_flax_bert import FlaxBertModel
    from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel



@require_flax
class       A__     ( unittest.TestCase    ):



      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for model_name in ["bert-base-cased", "bert-large-uncased"]:
        with self.subTest(a_     ):
         UpperCamelCase			:      Optional[int] 			=     AutoConfig.from_pretrained(a_     )
         self.assertIsNotNone(a_     )
         self.assertIsInstance(a_ ,							a_     )

         UpperCamelCase			:      Dict 			=     FlaxAutoModel.from_pretrained(a_     )
         self.assertIsNotNone(a_     )
         self.assertIsInstance(a_ ,							a_     )



      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for model_name in ["roberta-base", "roberta-large"]:
        with self.subTest(a_     ):
         UpperCamelCase			:      Any 			=     AutoConfig.from_pretrained(a_     )
         self.assertIsNotNone(a_     )
         self.assertIsInstance(a_ ,							a_     )

         UpperCamelCase			:      Optional[int] 			=     FlaxAutoModel.from_pretrained(a_     )
         self.assertIsNotNone(a_     )
         self.assertIsInstance(a_ ,							a_     )



      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for model_name in ["bert-base-cased", "bert-large-uncased"]:
        UpperCamelCase			:      Union[str, Any] 			=     AutoTokenizer.from_pretrained(a_     )
        UpperCamelCase			:      Optional[int] 			=     FlaxBertModel.from_pretrained(a_     )
        UpperCamelCase			:      Optional[Any] 			=     tokenizer("Do you support jax jitted function?" ,							return_tensors=TensorType.JAX     )

        @jax.jit
        def eval(**A_     ):
         return model(**a_     )

        eval(**a_     ).block_until_ready()



      @slow
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       for model_name in ["roberta-base", "roberta-large"]:
        UpperCamelCase			:      Union[str, Any] 			=     AutoTokenizer.from_pretrained(a_     )
        UpperCamelCase			:      List[Any] 			=     FlaxRobertaModel.from_pretrained(a_     )
        UpperCamelCase			:      Tuple 			=     tokenizer("Do you support jax jitted function?" ,							return_tensors=TensorType.JAX     )

        @jax.jit
        def eval(**A_     ):
         return model(**a_     )

        eval(**a_     ).block_until_ready()



      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           a_ ,							"bert-base is not a local folder and is not a valid model identifier"     ):
        UpperCamelCase			:      Union[str, Any] 			=     FlaxAutoModel.from_pretrained("bert-base"     )



      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           a_ ,							R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"     ):
        UpperCamelCase			:      List[Any] 			=     FlaxAutoModel.from_pretrained(a_ ,							revision="aaaaaa"     )



      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           a_ ,							"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack" ,							):
        UpperCamelCase			:      str 			=     FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"     )



      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       with self.assertRaisesRegex(a_ ,							"Use `from_pt=True` to load this model"     ):
        UpperCamelCase			:      List[str] 			=     FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"     )







import argparse
from pathlib import Path

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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     )		->				str:
 if config_name_or_path is None:
  UpperCamelCase			:      Dict 			=     "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

 if generator_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     question_encoder_name_or_path

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

 # Save model.
 UpperCamelCase			:      Optional[Any] 			=     RagConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Tuple 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )

 UpperCamelCase			:      int 			=     gen_config
 UpperCamelCase			:      Dict 			=     question_encoder_config

 UpperCamelCase			:      Tuple 			=     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.
 UpperCamelCase			:      Optional[Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"  )


if __name__ == "__main__":
    __lowerCamelCase :  Tuple										=  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``"""
        ),
    )

    __lowerCamelCase :  Dict										=  parser.parse_args()

    __lowerCamelCase :  Dict										=  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,
    )
