


"""simple docstring"""

import argparse
from pathlib import Path

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


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: str				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Path				,   UpperCamelCase__	: str = None				,   UpperCamelCase__	: str = None				,   UpperCamelCase__	: str = None				,   ):






 """simple docstring"""







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

 if generator_tokenizer_name_or_path is None:
  __lowercase									=	generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  __lowercase									=	question_encoder_name_or_path

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

 # Save model.
 __lowercase									=	RagConfig.from_pretrained(UpperCamelCase__ )
 __lowercase									=	AutoConfig.from_pretrained(UpperCamelCase__ )
 __lowercase									=	AutoConfig.from_pretrained(UpperCamelCase__ )

 __lowercase									=	gen_config
 __lowercase									=	question_encoder_config

 __lowercase									=	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.
 __lowercase									=	AutoTokenizer.from_pretrained(UpperCamelCase__ )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/""" )
 __lowercase									=	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"""

import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class    lowerCamelCase__						(		_a    ):
      a :     Optional[int]											=		"""segformer"""
      def __init__(		self	:	str      ,       A_	:	Optional[int]=3      ,       A_	:	Any=4      ,       A_	:	Optional[int]=[2, 2, 2, 2]      ,       A_	:	str=[8, 4, 2, 1]      ,       A_	:	Any=[3_2, 6_4, 1_6_0, 2_5_6]      ,       A_	:	Union[str, Any]=[7, 3, 3, 3]      ,       A_	:	Any=[4, 2, 2, 2]      ,       A_	:	int=[1, 2, 5, 8]      ,       A_	:	Union[str, Any]=[4, 4, 4, 4]      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	List[str]=0.0      ,       A_	:	Any=0.0      ,       A_	:	Optional[Any]=0.1      ,       A_	:	List[str]=0.02      ,       A_	:	Tuple=0.1      ,       A_	:	Optional[Any]=1e-6      ,       A_	:	Optional[int]=2_5_6      ,       A_	:	Optional[Any]=2_5_5      ,       **A_	:	Union[str, Any]      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
        warnings.warn(
            """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
            """ removed, as the behaviour will default to that of reshape_last_stage = True."""      ,       A_      ,       )

       __lowercase									=	num_channels
       __lowercase									=	num_encoder_blocks
       __lowercase									=	depths
       __lowercase									=	sr_ratios
       __lowercase									=	hidden_sizes
       __lowercase									=	patch_sizes
       __lowercase									=	strides
       __lowercase									=	mlp_ratios
       __lowercase									=	num_attention_heads
       __lowercase									=	hidden_act
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	classifier_dropout_prob
       __lowercase									=	initializer_range
       __lowercase									=	drop_path_rate
       __lowercase									=	layer_norm_eps
       __lowercase									=	decoder_hidden_size
       __lowercase									=	kwargs.get("""reshape_last_stage"""      ,       A_							)
       __lowercase									=	semantic_loss_ignore_index






class    lowerCamelCase__						(		_a    ):
      a :     List[str]											=		version.parse("""1.11"""    )
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       return 1e-4






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       return 1_2
