

"""simple docstring"""




import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase = None							,    __UpperCamelCase = None							,    __UpperCamelCase = None							,    ):
	'''simple docstring'''





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

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

	if question_encoder_tokenizer_name_or_path is None:
		UpperCAmelCase__					:  str					   =					question_encoder_name_or_path

	UpperCAmelCase__					:  Optional[Any]					   =					RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

	# Save model.
	UpperCAmelCase__					:  Optional[Any]					   =					RagConfig.from_pretrained(__UpperCamelCase      )
	UpperCAmelCase__					:  Any					   =					AutoConfig.from_pretrained(__UpperCamelCase      )
	UpperCAmelCase__					:  Any					   =					AutoConfig.from_pretrained(__UpperCamelCase      )

	UpperCAmelCase__					:  Union[str, Any]					   =					gen_config
	UpperCAmelCase__					:  Tuple					   =					question_encoder_config

	UpperCAmelCase__					:  List[Any]					   =					model_class.from_pretrained_question_encoder_generator(
	    __UpperCamelCase							,    __UpperCamelCase							,    config=__UpperCamelCase      )
	rag_model.save_pretrained(__UpperCamelCase      )

	# Sanity check.
	model_class.from_pretrained(__UpperCamelCase      )

	# Save tokenizers.
	UpperCAmelCase__					:  Dict					   =					AutoTokenizer.from_pretrained(__UpperCamelCase      )
	gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""      )
	UpperCAmelCase__					:  Union[str, Any]					   =					AutoTokenizer.from_pretrained(__UpperCamelCase      )
	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"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase =				{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				['RobertaTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RobertaForCausalLM',
          'RobertaForMaskedLM',
          'RobertaForMultipleChoice',
          'RobertaForQuestionAnswering',
          'RobertaForSequenceClassification',
          'RobertaForTokenClassification',
          'RobertaModel',
          'RobertaPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRobertaForCausalLM',
          'TFRobertaForMaskedLM',
          'TFRobertaForMultipleChoice',
          'TFRobertaForQuestionAnswering',
          'TFRobertaForSequenceClassification',
          'TFRobertaForTokenClassification',
          'TFRobertaMainLayer',
          'TFRobertaModel',
          'TFRobertaPreTrainedModel',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'FlaxRobertaForCausalLM',
          'FlaxRobertaForMaskedLM',
          'FlaxRobertaForMultipleChoice',
          'FlaxRobertaForQuestionAnswering',
          'FlaxRobertaForSequenceClassification',
          'FlaxRobertaForTokenClassification',
          'FlaxRobertaModel',
          'FlaxRobertaPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





