


'''simple docstring'''



import argparse
from pathlib import Path

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


def    lowerCamelCase__   ( A     :    Any   ,     A     :    int   ,     A     :    List[Any]   ,     A     :    Union[str, Any]   ,     A     :    Tuple = None   ,     A     :    str = None   ,     A     :    Union[str, Any] = 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(_lowerCAmelCase  )
						UpperCAmelCase         =			AutoConfig.from_pretrained(_lowerCAmelCase  )
						UpperCAmelCase         =			AutoConfig.from_pretrained(_lowerCAmelCase  )

						UpperCAmelCase         =			gen_config
						UpperCAmelCase         =			question_encoder_config

						UpperCAmelCase         =			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         =			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
						gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''  )
						UpperCAmelCase         =			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
						question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''  )


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

						_lowercase    :    Dict			     =  parser.parse_args()

						_lowercase    :    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,
						)









'''simple docstring'''



import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers.generation import (
	    MaxLengthCriteria,
	    MaxNewTokensCriteria,
	    MaxTimeCriteria,
	    StoppingCriteriaList,
	    validate_stopping_criteria,
	)






@require_torch
class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Optional[Any]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			3
										UpperCAmelCase         =			250

										UpperCAmelCase         =			ids_tensor((batch_size, length)   ,					lowerCAmelCase   )
										UpperCAmelCase         =			torch.ones((batch_size, length)   ,					device=lowerCAmelCase   ,					dtype=torch.float   ) / length
										return input_ids, scores




				def   a__(	self    :				Dict   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )

										UpperCAmelCase         =			StoppingCriteriaList(
										    [
										        MaxLengthCriteria(max_length=10   ),
										        MaxTimeCriteria(max_time=0.1   ),
										    ]   )

										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )




				def   a__(	self    :				Union[str, Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			MaxLengthCriteria(max_length=10   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )




				def   a__(	self    :				Optional[Any]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			MaxNewTokensCriteria(start_length=5   ,					max_new_tokens=5   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase         =			StoppingCriteriaList([criteria]   )
										self.assertEqual(criteria_list.max_length   ,					10   )




				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )

										UpperCAmelCase         =			MaxTimeCriteria(max_time=0.1   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase         =			MaxTimeCriteria(max_time=0.1   ,					initial_timestamp=time.time() - 0.2   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )





				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




										validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,					10   )

										with self.assertWarns(lowerCAmelCase   ):
																validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,					11   )

										UpperCAmelCase         =			validate_stopping_criteria(StoppingCriteriaList()   ,					11   )

										self.assertEqual(len(lowerCAmelCase   )   ,					1   )





