






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 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     question_encoder
       SCREAMING_SNAKE_CASE							:   str						    =     generator
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.question_encoder


     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any   ):
       if os.path.isfile(UpperCAmelCase_   ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''   )
       os.makedirs(UpperCAmelCase_       ,			exist_ok=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     os.path.join(UpperCAmelCase_       ,			"question_encoder_tokenizer"   )
       SCREAMING_SNAKE_CASE							:   Any						    =     os.path.join(UpperCAmelCase_       ,			"generator_tokenizer"   )
       self.question_encoder.save_pretrained(UpperCAmelCase_   )
       self.generator.save_pretrained(UpperCAmelCase_   )


     @classmethod
     def 					_A							(					cls							:						List[Any]       ,			UpperCAmelCase_							:						List[Any]       ,			**UpperCAmelCase_							:						List[str]   ):
       # dynamically import AutoTokenizer
       from ..auto.tokenization_auto import AutoTokenizer

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     kwargs.pop("config"       ,			UpperCAmelCase_   )

       if config is None:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     RagConfig.from_pretrained(UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     AutoTokenizer.from_pretrained(
           UpperCAmelCase_       ,			config=config.question_encoder       ,			subfolder="question_encoder_tokenizer"   )
       SCREAMING_SNAKE_CASE							:   str						    =     AutoTokenizer.from_pretrained(
           UpperCAmelCase_       ,			config=config.generator       ,			subfolder="generator_tokenizer"   )
       return cls(question_encoder=UpperCAmelCase_       ,			generator=UpperCAmelCase_   )


     def __call__(					self							:						Optional[Any]       ,			*UpperCAmelCase_							:						int       ,			**UpperCAmelCase_							:						List[str]   ):
       return self.current_tokenizer(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[int]       ,			*UpperCAmelCase_							:						Union[str, Any]       ,			**UpperCAmelCase_							:						Tuple   ):
       return self.generator.batch_decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]       ,			*UpperCAmelCase_							:						Optional[int]       ,			**UpperCAmelCase_							:						int   ):
       return self.generator.decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self.question_encoder


     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.generator






     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						List[str]       ,			UpperCAmelCase_							:						Optional[List[str]] = None       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						str = "longest"       ,			UpperCAmelCase_							:						str = None       ,			UpperCAmelCase_							:						bool = True       ,			**UpperCAmelCase_							:						Optional[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"       ,			UpperCAmelCase_       ,			)
       if max_length is None:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE							:   List[str]						    =     self(
           UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			padding=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)
       if tgt_texts is None:
         return model_inputs
       # Process tgt_texts
       if max_target_length is None:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE							:   str						    =     self(
           text_target=UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			padding=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)
       SCREAMING_SNAKE_CASE							:   str						    =     labels["input_ids"]
       return model_inputs












'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							a    :							List[str]										=    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
							parser.add_argument(
							    '--original_config_file',
							    default=None,
							    type=str,
							    help='The YAML config file corresponding to the original architecture.',
							)
							parser.add_argument(
							    '--num_in_channels',
							    default=None,
							    type=int,
							    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
							)
							parser.add_argument(
							    '--scheduler_type',
							    default='pndm',
							    type=str,
							    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
							)
							parser.add_argument(
							    '--pipeline_type',
							    default=None,
							    type=str,
							    help=(
							        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
							        '. If `None` pipeline will be automatically inferred.'
							    ),
							)
							parser.add_argument(
							    '--image_size',
							    default=None,
							    type=int,
							    help=(
							        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
							        ' Base. Use 768 for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--prediction_type',
							    default=None,
							    type=str,
							    help=(
							        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
							        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--extract_ema',
							    action='store_true',
							    help=(
							        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
							        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
							        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
							    ),
							)
							parser.add_argument(
							    '--upcast_attention',
							    action='store_true',
							    help=(
							        'Whether the attention computation should always be upcasted. This is necessary when running stable'
							        ' diffusion 2.1.'
							    ),
							)
							parser.add_argument(
							    '--from_safetensors',
							    action='store_true',
							    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
							)
							parser.add_argument(
							    '--to_safetensors',
							    action='store_true',
							    help='Whether to store pipeline in safetensors format or not.',
							)
							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
							parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
							parser.add_argument(
							    '--stable_unclip',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
							)
							parser.add_argument(
							    '--stable_unclip_prior',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
							)
							parser.add_argument(
							    '--clip_stats_path',
							    type=str,
							    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
							    required=False,
							)
							parser.add_argument(
							    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
							)
							parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
							parser.add_argument(
							    '--vae_path',
							    type=str,
							    default=None,
							    required=False,
							    help='Set to a path, hub id to an already converted vae to not convert it again.',
							)
							a    :							str										=    parser.parse_args()

							a    :							List[Any]										=    download_from_original_stable_diffusion_ckpt(
							    checkpoint_path=args.checkpoint_path,
							    original_config_file=args.original_config_file,
							    image_size=args.image_size,
							    prediction_type=args.prediction_type,
							    model_type=args.pipeline_type,
							    extract_ema=args.extract_ema,
							    scheduler_type=args.scheduler_type,
							    num_in_channels=args.num_in_channels,
							    upcast_attention=args.upcast_attention,
							    from_safetensors=args.from_safetensors,
							    device=args.device,
							    stable_unclip=args.stable_unclip,
							    stable_unclip_prior=args.stable_unclip_prior,
							    clip_stats_path=args.clip_stats_path,
							    controlnet=args.controlnet,
							    vae_path=args.vae_path,
							)

							if args.half:
														pipe.to(torch_dtype=torch.floataa)

							if args.controlnet:
														# only save the controlnet model
														pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
							else:
														pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

