






from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Optional[int] ,  __lowerCamelCase				:					CLIPSegForImageSegmentation ,  __lowerCamelCase				:					CLIPSegProcessor ,  __lowerCamelCase				:					AutoencoderKL ,  __lowerCamelCase				:					CLIPTextModel ,  __lowerCamelCase				:					CLIPTokenizer ,  __lowerCamelCase				:					UNetaDConditionModel ,  __lowerCamelCase				:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,  __lowerCamelCase				:					StableDiffusionSafetyChecker ,  __lowerCamelCase				:					CLIPImageProcessor ,  ):
		super().__init__()

		if hasattr(scheduler.config ,  """steps_offset"""	) and scheduler.config.steps_offset != 1:
			UpperCamelCase     :str          =	(
			    F"""The configuration file of this scheduler: {scheduler} is outdated.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     :Optional[Any]          =	dict(scheduler.config	)
			UpperCamelCase     :Tuple          =	1
			UpperCamelCase     :Dict          =	FrozenDict(__lowerCamelCase	)

		if hasattr(scheduler.config ,  """skip_prk_steps"""	) and scheduler.config.skip_prk_steps is False:
			UpperCamelCase     :Any          =	(
			    F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
			    """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
			    """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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("""skip_prk_steps not set""" ,  """1.0.0""" ,  __lowerCamelCase ,  standard_warn=__lowerCamelCase	)
			UpperCamelCase     :Tuple          =	dict(scheduler.config	)
			UpperCamelCase     :Any          =	True
			UpperCamelCase     :List[str]          =	FrozenDict(__lowerCamelCase	)

		if safety_checker is None:
			logger.warning(
			    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
			    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
			    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
			    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
			    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
			    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""	)

		self.register_modules(
		    segmentation_model=__lowerCamelCase ,  segmentation_processor=__lowerCamelCase ,  vae=__lowerCamelCase ,  text_encoder=__lowerCamelCase ,  tokenizer=__lowerCamelCase ,  unet=__lowerCamelCase ,  scheduler=__lowerCamelCase ,  safety_checker=__lowerCamelCase ,  feature_extractor=__lowerCamelCase ,  )





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					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     :Optional[Any]          =	self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		self.enable_attention_slicing(__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		if is_accelerate_available():
			from accelerate import cpu_offload
		else:
			raise ImportError("""Please install accelerate via `pip install accelerate`"""	)

		UpperCamelCase     :Any          =	torch.device("""cuda"""	)

		for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
			if cpu_offloaded_model is not None:
				cpu_offload(__lowerCamelCase ,  __lowerCamelCase	)





	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 						_A (		self				:					Tuple	):
		if self.device != torch.device("""meta"""	) or not hasattr(self.unet ,  """_hf_hook"""	):
			return self.device
		for module in self.unet.modules():
			if (
			    hasattr(__lowerCamelCase ,  """_hf_hook"""	)
			    and hasattr(module._hf_hook ,  """execution_device"""	)
			    and module._hf_hook.execution_device is not None
			):
				return torch.device(module._hf_hook.execution_device	)
		return self.device





	@torch.no_grad()
	def __call__(		self				:					Optional[Any] ,  __lowerCamelCase				:					Union[str, List[str]] ,  __lowerCamelCase				:					Union[torch.FloatTensor, PIL.Image.Image] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					int = 512 ,  __lowerCamelCase				:					int = 512 ,  __lowerCamelCase				:					int = 50 ,  __lowerCamelCase				:					float = 7.5 ,  __lowerCamelCase				:					Optional[Union[str, List[str]]] = None ,  __lowerCamelCase				:					Optional[int] = 1 ,  __lowerCamelCase				:					float = 0.0 ,  __lowerCamelCase				:					Optional[torch.Generator] = None ,  __lowerCamelCase				:					Optional[torch.FloatTensor] = None ,  __lowerCamelCase				:					Optional[str] = "pil" ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,  __lowerCamelCase				:					int = 1 ,  **__lowerCamelCase				:					List[Any] ,  ):
		UpperCamelCase     :Any          =	self.segmentation_processor(
		    text=[text] ,  images=[image] ,  padding="""max_length""" ,  return_tensors="""pt"""	).to(self.device	)
		UpperCamelCase     :Any          =	self.segmentation_model(**__lowerCamelCase	)
		UpperCamelCase     :Dict          =	torch.sigmoid(outputs.logits	).cpu().detach().unsqueeze(-1	).numpy()
		UpperCamelCase     :Optional[int]          =	self.numpy_to_pil(__lowerCamelCase	)[0].resize(image.size	)

		# Run inpainting pipeline with the generated mask
		UpperCamelCase     :Tuple          =	StableDiffusionInpaintPipeline(
		    vae=self.vae ,  text_encoder=self.text_encoder ,  tokenizer=self.tokenizer ,  unet=self.unet ,  scheduler=self.scheduler ,  safety_checker=self.safety_checker ,  feature_extractor=self.feature_extractor ,  )
		return inpainting_pipeline(
		    prompt=__lowerCamelCase ,  image=__lowerCamelCase ,  mask_image=__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
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





