






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__snake_case												=  logging.get_logger(__name__)







class 			UpperCAmelCase_						:



      """simple docstring"""
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          UpperCamelCase       :List[Any]								=   question_encoder
          UpperCamelCase       :Dict								=   generator
          UpperCamelCase       :Dict								=   self.question_encoder
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      int:



          if os.path.isfile(__lowerCAmelCase ):
              raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
          os.makedirs(__lowerCAmelCase    ,				exist_ok=__lowerCAmelCase )
          UpperCamelCase       :Optional[Any]								=   os.path.join(__lowerCAmelCase    ,				'''question_encoder_tokenizer''' )
          UpperCamelCase       :List[str]								=   os.path.join(__lowerCAmelCase    ,				'''generator_tokenizer''' )
          self.question_encoder.save_pretrained(__lowerCAmelCase )
          self.generator.save_pretrained(__lowerCAmelCase )
      @classmethod
      def     UpperCAmelCase						(				cls    ,				SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          from ..auto.tokenization_auto import AutoTokenizer

          UpperCamelCase       :Union[str, Any]								=   kwargs.pop('''config'''    ,				__lowerCAmelCase )

          if config is None:
              UpperCamelCase       :Union[str, Any]								=   RagConfig.from_pretrained(__lowerCAmelCase )

          UpperCamelCase       :int								=   AutoTokenizer.from_pretrained(
              __lowerCAmelCase    ,				config=config.question_encoder    ,				subfolder='''question_encoder_tokenizer''' )
          UpperCamelCase       :Dict								=   AutoTokenizer.from_pretrained(
              __lowerCAmelCase    ,				config=config.generator    ,				subfolder='''generator_tokenizer''' )
          return cls(question_encoder=__lowerCAmelCase    ,				generator=__lowerCAmelCase )
      def __call__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          return self.current_tokenizer(*__lowerCAmelCase    ,				**__lowerCAmelCase )
      def     UpperCAmelCase						(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Any:
          return self.generator.batch_decode(*__lowerCAmelCase    ,				**__lowerCAmelCase )
      def     UpperCAmelCase						(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      int:
          return self.generator.decode(*__lowerCAmelCase    ,				**__lowerCAmelCase )
      def     UpperCAmelCase						(				self )				->      str:
          UpperCamelCase       :Optional[int]								=   self.question_encoder
      def     UpperCAmelCase						(				self )				->      List[Any]:
          UpperCamelCase       :str								=   self.generator




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = "longest"    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = True    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      List[Any]:
          warnings.warn(
              '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
              '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
              '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
              '''details'''    ,				__lowerCAmelCase    ,				)
          if max_length is None:
              UpperCamelCase       :Union[str, Any]								=   self.current_tokenizer.model_max_length
          UpperCamelCase       :Any								=   self(
              __lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase    ,				return_tensors=__lowerCAmelCase    ,				max_length=__lowerCAmelCase    ,				padding=__lowerCAmelCase    ,				truncation=__lowerCAmelCase    ,				**__lowerCAmelCase    ,				)
          if tgt_texts is None:
              return model_inputs
          # Process tgt_texts
          if max_target_length is None:
              UpperCamelCase       :Any								=   self.current_tokenizer.model_max_length
          UpperCamelCase       :str								=   self(
              text_target=__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase    ,				return_tensors=__lowerCAmelCase    ,				padding=__lowerCAmelCase    ,				max_length=__lowerCAmelCase    ,				truncation=__lowerCAmelCase    ,				**__lowerCAmelCase    ,				)
          UpperCamelCase       :int								=   labels['''input_ids''']
          return model_inputs






"""simple docstring"""






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      output_path.parent.mkdir(parents=lowerCAmelCase__					,		exist_ok=lowerCAmelCase__		)
      # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
      # so we check the torch version for backwards compatibility
      if is_torch_less_than_1_11:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		use_external_data_format=lowerCAmelCase__					,		enable_onnx_checker=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)
      else:
            export(
                lowerCAmelCase__					,		lowerCAmelCase__					,		f=output_path.as_posix()					,		input_names=lowerCAmelCase__					,		output_names=lowerCAmelCase__					,		dynamic_axes=lowerCAmelCase__					,		do_constant_folding=lowerCAmelCase__					,		opset_version=lowerCAmelCase__					,		)


@torch.no_grad()
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :bool = False		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).to(device=lowerCAmelCase__					,		dtype=lowerCAmelCase__		),
              False,
          )					,		output_path=output_path / """vae_decoder""" / """model.onnx"""					,		ordered_input_names=["""latent_sample""", """return_dict"""]					,		output_names=["""sample"""]					,		dynamic_axes={
              """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
          }					,		opset=lowerCAmelCase__					,		)
      del vae_decoder


if __name__ == "__main__":
       __lowerCAmelCase    :       Tuple					     =argparse.ArgumentParser()

       parser.add_argument(
           """--model_path""",
           type=str,
           required=True,
           help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
       )

       parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
       parser.add_argument(
           """--opset""",
           default=1_4,
           type=int,
           help="""The version of the ONNX operator set to use.""",
       )
       parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

       __lowerCAmelCase    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



