




"""simple docstring"""




import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class 					__magic_name__							( lowercase__       ):






    '''simple docstring'''
    __UpperCamelCase    						=		["image_processor", "tokenizer"]
    __UpperCamelCase    						=		"BlipImageProcessor"
    __UpperCamelCase    						=		"AutoTokenizer"





    def __init__( self	,				_a	,				_a	,				_a				):





       """simple docstring"""
       super().__init__(UpperCAmelCase__	,				UpperCAmelCase__				)

       # add QFormer tokenizer
       lowerCamelCase       =      qformer_tokenizer





    def __call__( self	,				_a = None	,				_a = None	,				_a = True	,				_a = False	,				_a = None	,				_a = None	,				_a = 0	,				_a = None	,				_a = None	,				_a = False	,				_a = False	,				_a = False	,				_a = False	,				_a = False	,				_a = True	,				_a = None	,				**_a	,				):





       """simple docstring"""
       if images is None and text is None:
          raise ValueError("""You have to specify at least images or text."""				)

       lowerCamelCase       =      BatchFeature()

       if text is not None:
          lowerCamelCase       =      self.tokenizer(
              text=UpperCAmelCase__	,				add_special_tokens=UpperCAmelCase__	,				padding=UpperCAmelCase__	,				truncation=UpperCAmelCase__	,				max_length=UpperCAmelCase__	,				stride=UpperCAmelCase__	,				pad_to_multiple_of=UpperCAmelCase__	,				return_attention_mask=UpperCAmelCase__	,				return_overflowing_tokens=UpperCAmelCase__	,				return_special_tokens_mask=UpperCAmelCase__	,				return_offsets_mapping=UpperCAmelCase__	,				return_token_type_ids=UpperCAmelCase__	,				return_length=UpperCAmelCase__	,				verbose=UpperCAmelCase__	,				return_tensors=UpperCAmelCase__	,				**UpperCAmelCase__	,				)
          encoding.update(UpperCAmelCase__				)
          lowerCamelCase       =      self.qformer_tokenizer(
              text=UpperCAmelCase__	,				add_special_tokens=UpperCAmelCase__	,				padding=UpperCAmelCase__	,				truncation=UpperCAmelCase__	,				max_length=UpperCAmelCase__	,				stride=UpperCAmelCase__	,				pad_to_multiple_of=UpperCAmelCase__	,				return_attention_mask=UpperCAmelCase__	,				return_overflowing_tokens=UpperCAmelCase__	,				return_special_tokens_mask=UpperCAmelCase__	,				return_offsets_mapping=UpperCAmelCase__	,				return_token_type_ids=UpperCAmelCase__	,				return_length=UpperCAmelCase__	,				verbose=UpperCAmelCase__	,				return_tensors=UpperCAmelCase__	,				**UpperCAmelCase__	,				)
          lowerCamelCase       =      qformer_text_encoding.pop("""input_ids"""				)
          lowerCamelCase       =      qformer_text_encoding.pop("""attention_mask"""				)

       if images is not None:
          lowerCamelCase       =      self.image_processor(UpperCAmelCase__	,				return_tensors=UpperCAmelCase__				)
          encoding.update(UpperCAmelCase__				)

       return encoding





    def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





       """simple docstring"""
       return self.tokenizer.batch_decode(*UpperCAmelCase__	,				**UpperCAmelCase__				)





    def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





       """simple docstring"""
       return self.tokenizer.decode(*UpperCAmelCase__	,				**UpperCAmelCase__				)





    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def      _lowerCAmelCase       ( self				):





       """simple docstring"""
       lowerCamelCase       =      self.tokenizer.model_input_names
       lowerCamelCase       =      self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





    def      _lowerCAmelCase       ( self	,				_a	,				**_a				):





       """simple docstring"""
       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__				)
       lowerCamelCase       =      os.path.join(UpperCAmelCase__	,				"""qformer_tokenizer"""				)
       self.qformer_tokenizer.save_pretrained(UpperCAmelCase__				)
       return super().save_pretrained(UpperCAmelCase__	,				**UpperCAmelCase__				)





    @classmethod
    def      _lowerCAmelCase       ( cls	,				_a	,				**_a				):





       """simple docstring"""
       lowerCamelCase       =      AutoTokenizer.from_pretrained(UpperCAmelCase__	,				subfolder="""qformer_tokenizer"""				)
       lowerCamelCase       =      cls._get_arguments_from_pretrained(UpperCAmelCase__	,				**UpperCAmelCase__				)
       args.append(UpperCAmelCase__				)
       return cls(*UpperCAmelCase__				)









"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					lowerCAmelCase							:    Dict  							=							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.""",
					)
					lowerCAmelCase							:    Dict  							=							parser.parse_args()

					lowerCAmelCase							:    str  							=							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)



