




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_lowerCamelCase			 =						logging.get_logger(__name__)  # pylint: disable=invalid-name



class 			a     (   __UpperCAmelCase			):

     '''simple docstring'''





     lowerCAmelCase				:       Optional[Any]   		=  ['safety_checker', 'feature_extractor']
     def __init__(	self   :			List[Any]					,    __snake_case   :			AutoencoderKL					,    __snake_case   :			CLIPTextModel					,    __snake_case   :			CLIPTokenizer					,    __snake_case   :			UNetaDConditionModel					,    __snake_case   :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,    __snake_case   :			StableDiffusionSafetyChecker					,    __snake_case   :			CLIPImageProcessor					,    __snake_case   :			bool = True					,    ):
         super().__init__()
         if hasattr(scheduler.config					,    '''steps_offset'''				) and scheduler.config.steps_offset != 1:
             UpperCAmelCase_							 =     (
                 F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
                 F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
                 '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
                 ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
                 ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
                 ''' file'''
             )
             deprecate('''steps_offset!=1'''					,    '''1.0.0'''					,    _lowerCamelCase					,    standard_warn=_lowerCamelCase				)
             UpperCAmelCase_							 =     dict(scheduler.config				)
             UpperCAmelCase_							 =     1
             UpperCAmelCase_							 =     FrozenDict(_lowerCamelCase				)

         self.register_modules(
             vae=_lowerCamelCase					,    text_encoder=_lowerCamelCase					,    tokenizer=_lowerCamelCase					,    unet=_lowerCamelCase					,    scheduler=_lowerCamelCase					,    safety_checker=_lowerCamelCase					,    feature_extractor=_lowerCamelCase					,    )
         self.register_to_config(requires_safety_checker=_lowerCamelCase				)
     @property
     def   lowerCamelCase_   (	self   :			List[str]				):
         return {k: getattr(self					,    _lowerCamelCase				) for k in self.config.keys() if not k.startswith('''_'''				)}
     def   lowerCamelCase_   (	self   :			List[str]					,    __snake_case   :			Optional[Union[str, int]] = "auto"				):
         if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             UpperCAmelCase_							 =     self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(_lowerCamelCase				)
     def   lowerCamelCase_   (	self   :			List[str]				):
         self.enable_attention_slicing(_lowerCamelCase				)
     @torch.no_grad()
     def   lowerCamelCase_   (	self   :			List[Any]					,    __snake_case   :			Union[str, List[str]]					,    __snake_case   :			Union[torch.FloatTensor, PIL.Image.Image]					,    __snake_case   :			Union[torch.FloatTensor, PIL.Image.Image]					,    __snake_case   :			float = 0.8					,    __snake_case   :			Optional[int] = 50					,    __snake_case   :			Optional[float] = 7.5					,    __snake_case   :			Optional[Union[str, List[str]]] = None					,    __snake_case   :			Optional[int] = 1					,    __snake_case   :			Optional[float] = 0.0					,    __snake_case   :			Optional[torch.Generator] = None					,    __snake_case   :			Optional[str] = "pil"					,    __snake_case   :			bool = True					,    __snake_case   :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,    __snake_case   :			int = 1					,    ):
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionInpaintPipelineLegacy(**self.components				)(
             prompt=_lowerCamelCase					,    image=_lowerCamelCase					,    mask_image=_lowerCamelCase					,    strength=_lowerCamelCase					,    num_inference_steps=_lowerCamelCase					,    guidance_scale=_lowerCamelCase					,    negative_prompt=_lowerCamelCase					,    num_images_per_prompt=_lowerCamelCase					,    eta=_lowerCamelCase					,    generator=_lowerCamelCase					,    output_type=_lowerCamelCase					,    return_dict=_lowerCamelCase					,    callback=_lowerCamelCase					,    )
     @torch.no_grad()
     def   lowerCamelCase_   (	self   :			Optional[int]					,    __snake_case   :			Union[str, List[str]]					,    __snake_case   :			Union[torch.FloatTensor, PIL.Image.Image]					,    __snake_case   :			float = 0.8					,    __snake_case   :			Optional[int] = 50					,    __snake_case   :			Optional[float] = 7.5					,    __snake_case   :			Optional[Union[str, List[str]]] = None					,    __snake_case   :			Optional[int] = 1					,    __snake_case   :			Optional[float] = 0.0					,    __snake_case   :			Optional[torch.Generator] = None					,    __snake_case   :			Optional[str] = "pil"					,    __snake_case   :			bool = True					,    __snake_case   :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,    __snake_case   :			int = 1					,    **__snake_case   :			List[str]					,    ):
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionImgaImgPipeline(**self.components				)(
             prompt=_lowerCamelCase					,    image=_lowerCamelCase					,    strength=_lowerCamelCase					,    num_inference_steps=_lowerCamelCase					,    guidance_scale=_lowerCamelCase					,    negative_prompt=_lowerCamelCase					,    num_images_per_prompt=_lowerCamelCase					,    eta=_lowerCamelCase					,    generator=_lowerCamelCase					,    output_type=_lowerCamelCase					,    return_dict=_lowerCamelCase					,    callback=_lowerCamelCase					,    callback_steps=_lowerCamelCase					,    )




     @torch.no_grad()
     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			Union[str, List[str]]					,    __snake_case   :			int = 5_12					,    __snake_case   :			int = 5_12					,    __snake_case   :			int = 50					,    __snake_case   :			float = 7.5					,    __snake_case   :			Optional[Union[str, List[str]]] = None					,    __snake_case   :			Optional[int] = 1					,    __snake_case   :			float = 0.0					,    __snake_case   :			Optional[torch.Generator] = None					,    __snake_case   :			Optional[torch.FloatTensor] = None					,    __snake_case   :			Optional[str] = "pil"					,    __snake_case   :			bool = True					,    __snake_case   :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,    __snake_case   :			int = 1					,    ):
         # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
         return StableDiffusionPipeline(**self.components				)(
             prompt=_lowerCamelCase					,    height=_lowerCamelCase					,    width=_lowerCamelCase					,    num_inference_steps=_lowerCamelCase					,    guidance_scale=_lowerCamelCase					,    negative_prompt=_lowerCamelCase					,    num_images_per_prompt=_lowerCamelCase					,    eta=_lowerCamelCase					,    generator=_lowerCamelCase					,    latents=_lowerCamelCase					,    output_type=_lowerCamelCase					,    return_dict=_lowerCamelCase					,    callback=_lowerCamelCase					,    callback_steps=_lowerCamelCase					,    )












"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

