





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class       A__     ( unittest.TestCase    ):



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




       UpperCamelCase			:      Tuple 			=     TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"     )

       UpperCamelCase			:      Optional[int] 			=     {
           "input_ids": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]] ,							dtype=tf.intaa     ),  # "My dog is cute"
           "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] ,							dtype=tf.intaa     ),
       }

       UpperCamelCase			:      Optional[Any] 			=     model(A_     )["last_hidden_state"]
       UpperCamelCase			:      Optional[int] 			=     tf.TensorShape((1, 6, 768)     )
       self.assertEqual(output.shape ,							A_     )
       # compare the actual values for a slice.
       UpperCamelCase			:      Any 			=     tf.convert_to_tensor(
           [
               [
                   [0.0_68_17_62, 0.10_89_44_51, 0.06_77_25_04],
                   [-0.06_42_36_68, 0.02_36_66_15, 0.04_32_93_44],
                   [-0.06_05_72_95, 0.09_97_41_35, -0.00_07_05_84],
               ]
           ] ,							dtype=tf.floataa ,							)

       self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,							expected_slice.numpy() ,							atol=1e-4     )     )







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,
    )
