


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_snake_case             =     8






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=BITS	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				x.device

  lowerCamelCase :					Tuple    =				(x * 255).int().clamp(0      ,						255	)

  lowerCamelCase :					Any    =				2 ** torch.arange(bits - 1      ,						-1      ,						-1      ,						device=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Union[str, Any]    =				rearrange(SCREAMING_SNAKE_CASE_      ,						"d -> d 1 1"	)
  lowerCamelCase :					Dict    =				rearrange(SCREAMING_SNAKE_CASE_      ,						"b c h w -> b c 1 h w"	)

  lowerCamelCase :					List[str]    =				((x & mask) != 0).float()
  lowerCamelCase :					Dict    =				rearrange(SCREAMING_SNAKE_CASE_      ,						"b c d h w -> b (c d) h w"	)
  lowerCamelCase :					Optional[int]    =				bits * 2 - 1
  return bits






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=BITS	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				x.device

  lowerCamelCase :					Any    =				(x > 0).int()
  lowerCamelCase :					Optional[Any]    =				2 ** torch.arange(bits - 1      ,						-1      ,						-1      ,						device=SCREAMING_SNAKE_CASE_      ,						dtype=torch.intaa	)

  lowerCamelCase :					List[Any]    =				rearrange(SCREAMING_SNAKE_CASE_      ,						"d -> d 1 1"	)
  lowerCamelCase :					Any    =				rearrange(SCREAMING_SNAKE_CASE_      ,						"b (c d) h w -> b c d h w"      ,						d=8	)
  lowerCamelCase :					Any    =				reduce(x * mask      ,						"b c d h w -> b c h w"      ,						"sum"	)
  return (dec / 255).clamp(0.0      ,						1.0	)






def 		lowercase_(  self      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = 0.0      ,						SCREAMING_SNAKE_CASE_ = True      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_ = True      ,						):


  '''simple docstring'''

  if self.num_inference_steps is None:
    raise ValueError(
        "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"	)

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  lowerCamelCase :					Union[str, Any]    =				timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  lowerCamelCase :					str    =				self.alphas_cumprod[timestep]
  lowerCamelCase :					List[Any]    =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  lowerCamelCase :					Tuple    =				1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  lowerCamelCase :					List[Any]    =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  lowerCamelCase :					int    =				self.bit_scale
  if self.config.clip_sample:
    lowerCamelCase :					Union[str, Any]    =				torch.clamp(SCREAMING_SNAKE_CASE_      ,						-scale      ,						SCREAMING_SNAKE_CASE_	)

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  lowerCamelCase :					Optional[int]    =				self._get_variance(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    lowerCamelCase :					Optional[Any]    =				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  lowerCamelCase :					int    =				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  lowerCamelCase :					List[Any]    =				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    lowerCamelCase :					List[str]    =				model_output.device if torch.is_tensor(SCREAMING_SNAKE_CASE_	) else "cpu"
    lowerCamelCase :					Dict    =				torch.randn(model_output.shape      ,						dtype=model_output.dtype      ,						generator=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					List[str]    =				self._get_variance(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	) ** 0.5 * eta * noise

    lowerCamelCase :					Tuple    =				prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      ,						pred_original_sample=SCREAMING_SNAKE_CASE_	)






def 		lowercase_(  self      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_="epsilon"      ,						SCREAMING_SNAKE_CASE_=None      ,						SCREAMING_SNAKE_CASE_ = True      ,						):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    lowerCamelCase    ,	lowerCamelCase :					Any    =				torch.split(SCREAMING_SNAKE_CASE_      ,						sample.shape[1]      ,						dim=1	)
  else:
    lowerCamelCase :					Tuple    =				None

  # 1. compute alphas, betas
  lowerCamelCase :					List[str]    =				self.alphas_cumprod[t]
  lowerCamelCase :					Union[str, Any]    =				self.alphas_cumprod[t - 1] if t > 0 else self.one
  lowerCamelCase :					int    =				1 - alpha_prod_t
  lowerCamelCase :					Union[str, Any]    =				1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if prediction_type == "epsilon":
    lowerCamelCase :					str    =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    lowerCamelCase :					Union[str, Any]    =				model_output
  else:
    raise ValueError(f"""Unsupported prediction_type {prediction_type}."""	)

  # 3. Clip "predicted x_0"
  lowerCamelCase :					Tuple    =				self.bit_scale
  if self.config.clip_sample:
    lowerCamelCase :					List[str]    =				torch.clamp(SCREAMING_SNAKE_CASE_      ,						-scale      ,						SCREAMING_SNAKE_CASE_	)

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  lowerCamelCase :					Optional[int]    =				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  lowerCamelCase :					Optional[Any]    =				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  lowerCamelCase :					Union[str, Any]    =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  lowerCamelCase :					Optional[int]    =				0
  if t > 0:
    lowerCamelCase :					int    =				torch.randn(
        model_output.size()      ,						dtype=model_output.dtype      ,						layout=model_output.layout      ,						generator=SCREAMING_SNAKE_CASE_	).to(model_output.device	)
    lowerCamelCase :					Optional[Any]    =				(self._get_variance(SCREAMING_SNAKE_CASE_      ,						predicted_variance=SCREAMING_SNAKE_CASE_	) ** 0.5) * noise

  lowerCamelCase :					int    =				pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      ,						pred_original_sample=SCREAMING_SNAKE_CASE_	)







class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A = 1.0						,				):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					str    =				bit_scale
     lowerCamelCase :					List[str]    =				(
         ddim_bit_scheduler_step if isinstance(__A						,				__A ) else ddpm_bit_scheduler_step
     )

     self.register_modules(unet=__A						,				scheduler=__A )






   @torch.no_grad()
   def __call__(		self						,				__A = 256						,				__A = 256						,				__A = 50						,				__A = None						,				__A = 1						,				__A = "pil"						,				__A = True						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				torch.randn(
         (batch_size, self.unet.config.in_channels, height, width)						,				generator=__A						,				)
     lowerCamelCase :					List[Any]    =				decimal_to_bits(__A ) * self.bit_scale
     lowerCamelCase :					Any    =				latents.to(self.device )

     self.scheduler.set_timesteps(__A )

     for t in self.progress_bar(self.scheduler.timesteps ):
       # predict the noise residual
       lowerCamelCase :					Union[str, Any]    =				self.unet(__A						,				__A ).sample

       # compute the previous noisy sample x_t -> x_t-1
       lowerCamelCase :					Optional[int]    =				self.scheduler.step(__A						,				__A						,				__A ).prev_sample

     lowerCamelCase :					Any    =				bits_to_decimal(__A )

     if output_type == "pil":
       lowerCamelCase :					Optional[Any]    =				self.numpy_to_pil(__A )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=__A )







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case             =     logging.get_logger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				"backbone." if is_semantic else ""

  lowerCamelCase :					int    =				[]
  for i in range(config.num_hidden_layers	):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")	)
    rename_keys.append(
        (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")	)
    rename_keys.append(
        (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")	)
    rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")	)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (f"""{prefix}cls_token""", "beit.embeddings.cls_token"),
          (f"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
          (f"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
          (f"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
      ]	)

  if has_lm_head:
    # mask token + layernorm
    rename_keys.extend(
        [
            ("mask_token", "beit.embeddings.mask_token"),
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
        ]	)
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("fc_norm.weight", "beit.pooler.layernorm.weight"),
            ("fc_norm.bias", "beit.pooler.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]	)

  return rename_keys






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  for i in range(config.num_hidden_layers	):
    lowerCamelCase :					Optional[Any]    =				"backbone." if is_semantic else ""
    # queries, keys and values
    lowerCamelCase :					Optional[Any]    =				state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""	)
    lowerCamelCase :					Optional[Any]    =				state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""	)
    lowerCamelCase :					Tuple    =				state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""	)

    lowerCamelCase :					str    =				in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase :					Any    =				q_bias
    lowerCamelCase :					Any    =				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase :					Optional[int]    =				in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase :					int    =				v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCamelCase :					Any    =				state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""	)
    lowerCamelCase :					Any    =				state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""	)

    lowerCamelCase :					int    =				gamma_a
    lowerCamelCase :					Optional[Any]    =				gamma_a






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				dct.pop(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				val






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase :					Optional[Any]    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	)
  return im






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				False if "rvlcdip" in checkpoint_url else True
  lowerCamelCase :					str    =				BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_      ,						use_mask_token=SCREAMING_SNAKE_CASE_	)

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    lowerCamelCase :					Union[str, Any]    =				1024
    lowerCamelCase :					Any    =				4096
    lowerCamelCase :					str    =				24
    lowerCamelCase :					List[Any]    =				16

  # labels
  if "rvlcdip" in checkpoint_url:
    lowerCamelCase :					Optional[Any]    =				16
    lowerCamelCase :					Tuple    =				"huggingface/label-files"
    lowerCamelCase :					List[str]    =				"rvlcdip-id2label.json"
    lowerCamelCase :					str    =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						repo_type="dataset"	)      ,						"r"	)	)
    lowerCamelCase :					Any    =				{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
    lowerCamelCase :					Tuple    =				idalabel
    lowerCamelCase :					Dict    =				{v: k for k, v in idalabel.items()}

  # load state_dict of original model, remove and rename some keys
  lowerCamelCase :					int    =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model"]

  lowerCamelCase :					Tuple    =				create_rename_keys(SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  read_in_q_k_v(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)

  # load HuggingFace model
  lowerCamelCase :					List[Any]    =				BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_	) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_	)
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  # Check outputs on an image
  lowerCamelCase :					str    =				BeitImageProcessor(
      size=config.image_size      ,						resample=PILImageResampling.BILINEAR      ,						do_center_crop=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				prepare_img()

  lowerCamelCase :					Optional[int]    =				image_processor(images=SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)
  lowerCamelCase :					Optional[Any]    =				encoding["pixel_values"]

  lowerCamelCase :					Optional[Any]    =				model(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Dict    =				outputs.logits

  # verify logits
  lowerCamelCase :					List[Any]    =				[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
  assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_	), "Shape of logits not as expected"

  Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
  print(f"""Saving model to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)

  if push_to_hub:
    if has_lm_head:
      lowerCamelCase :					Optional[Any]    =				"dit-base" if "base" in checkpoint_url else "dit-large"
    else:
      lowerCamelCase :					Dict    =				"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
    image_processor.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add image processor"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)
    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add model"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
       type=str,
       help='''URL to the original PyTorch checkpoint (.pth file).''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
   )
   _snake_case             =     parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



