






"""simple docstring"""







import argparse
from pathlib import Path

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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case = None				,	_snake_case = None				,	_snake_case = None				,	):







      """simple docstring"""






      if config_name_or_path is None:
            UpperCAmelCase          =	"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

      if generator_tokenizer_name_or_path is None:
            UpperCAmelCase          =	generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            UpperCAmelCase          =	question_encoder_name_or_path

      UpperCAmelCase          =	RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

      # Save model.
      UpperCAmelCase          =	RagConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	AutoConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	AutoConfig.from_pretrained(_snake_case							)

      UpperCAmelCase          =	gen_config
      UpperCAmelCase          =	question_encoder_config

      UpperCAmelCase          =	model_class.from_pretrained_question_encoder_generator(
          _snake_case				,	_snake_case				,	config=_snake_case							)
      rag_model.save_pretrained(_snake_case							)

      # Sanity check.
      model_class.from_pretrained(_snake_case							)

      # Save tokenizers.
      UpperCAmelCase          =	AutoTokenizer.from_pretrained(_snake_case							)
      gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""							)
      UpperCAmelCase          =	AutoTokenizer.from_pretrained(_snake_case							)
      question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""							)


if __name__ == "__main__":
  _UpperCamelCase					      =  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``"""
      ),
  )

  _UpperCamelCase					      =  parser.parse_args()

  _UpperCamelCase					      =  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,
  )











"""simple docstring"""







import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_UpperCamelCase					      =  get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase					      =  json.load(f)







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          if torch_device == "cuda":
                model.half()
          return model






    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ] )
    @slow
    def     _UpperCamelCase							(			self     ,A     ,A ):
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

          UpperCAmelCase          =	tokenizer(A     ,return_tensors="""pt"""     ,truncation=A     ,padding="""longest""" ).to(A )
          UpperCAmelCase          =	model.generate(
              input_ids=batch.input_ids     ,num_beams=8     ,)
          UpperCAmelCase          =	tokenizer.batch_decode(
              A     ,skip_special_tokens=A     ,clean_up_tokenization_spaces=A )
          UpperCAmelCase          =	calculate_bleu(A     ,A )
          print(A )
          self.assertGreaterEqual(scores["""bleu"""]     ,A )



