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





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      ):
            super().__init__()

            if hasattr(scheduler.config				,      'steps_offset'       ) and scheduler.config.steps_offset != 1:
                   _A=	(
                       f"The configuration file of this scheduler: {scheduler} is outdated.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__       )
                   _A=	dict(scheduler.config       )
                   _A=	1
                   _A=	FrozenDict(lowerCAmelCase__       )

            if hasattr(scheduler.config				,      'skip_prk_steps'       ) and scheduler.config.skip_prk_steps is False:
                   _A=	(
                       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__       )
                   _A=	dict(scheduler.config       )
                   _A=	True
                   _A=	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				,      lowerCAmelCase__ = "auto"       ):
            if slice_size == "auto":
                   # half the attention head size is usually a good trade-off between
                   # speed and memory
                   _A=	self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCAmelCase__       )



     def 			a__  (					self       ):
            self.enable_attention_slicing(lowerCAmelCase__       )



     def 			a__  (					self       ):
            if is_accelerate_available():
                   from accelerate import cpu_offload
            else:
                   raise ImportError('Please install accelerate via `pip install accelerate`'       )

            _A=	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       ):
            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				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = 512				,      lowerCAmelCase__ = 512				,      lowerCAmelCase__ = 50				,      lowerCAmelCase__ = 7.5				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = 1				,      lowerCAmelCase__ = 0.0				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = "pil"				,      lowerCAmelCase__ = True				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = 1				,      **lowerCAmelCase__				,      ):
            _A=	self.segmentation_processor(
                text=[text]				,      images=[image]				,      padding='max_length'				,      return_tensors='pt'       ).to(self.device       )
            _A=	self.segmentation_model(**lowerCAmelCase__       )
            _A=	torch.sigmoid(outputs.logits       ).cpu().detach().unsqueeze(-1       ).numpy()
            _A=	self.numpy_to_pil(lowerCAmelCase__       )[0].resize(image.size       )

            # Run inpainting pipeline with the generated mask
            _A=	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__				,      )
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
      UpperCAmelCase_								=							argparse.ArgumentParser(
          description=(
              '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
              ''' Distillation'''
          )
      )
      parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
      parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
      parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
      parser.add_argument('''--vocab_transform''', action='''store_true''')
      UpperCAmelCase_								=							parser.parse_args()

      if args.model_type == "bert":
            UpperCAmelCase_								=							BertForMaskedLM.from_pretrained(args.model_name)
            UpperCAmelCase_								=							'''bert'''
      else:
            raise ValueError('''args.model_type should be "bert".''')

      UpperCAmelCase_								=							model.state_dict()
      UpperCAmelCase_								=							{}

      for w in ["word_embeddings", "position_embeddings"]:
            UpperCAmelCase_								=							state_dict[F"{prefix}.embeddings.{w}.weight"]
      for w in ["weight", "bias"]:
            UpperCAmelCase_								=							state_dict[F"{prefix}.embeddings.LayerNorm.{w}"]

      UpperCAmelCase_								=							0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            for w in ["weight", "bias"]:
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
                  ]

                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
                  ]

                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
                  ]
                  UpperCAmelCase_								=							state_dict[
                      F"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
                  ]
            std_idx += 1

      UpperCAmelCase_								=							state_dict['''cls.predictions.decoder.weight''']
      UpperCAmelCase_								=							state_dict['''cls.predictions.bias''']
      if args.vocab_transform:
            for w in ["weight", "bias"]:
                  UpperCAmelCase_								=							state_dict[F"cls.predictions.transform.dense.{w}"]
                  UpperCAmelCase_								=							state_dict[F"cls.predictions.transform.LayerNorm.{w}"]

      print(F"N layers selected for distillation: {std_idx}")
      print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

      print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
      torch.save(compressed_sd, args.dump_checkpoint)