


'''simple docstring'''




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 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     List[Any]=BITS				):
   lowerCAmelCase     =			x.device

   lowerCAmelCase     =			(x * 255).int().clamp(0	, 255				)

   lowerCAmelCase     =			2 ** torch.arange(bits - 1	, -1	, -1	, device=lowerCamelCase				)
   lowerCAmelCase     =			rearrange(lowerCamelCase	, 'd -> d 1 1'				)
   lowerCAmelCase     =			rearrange(lowerCamelCase	, 'b c h w -> b c 1 h w'				)

   lowerCAmelCase     =			((x & mask) != 0).float()
   lowerCAmelCase     =			rearrange(lowerCamelCase	, 'b c d h w -> b (c d) h w'				)
   lowerCAmelCase     =			bits * 2 - 1
   return bits





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     List[str]=BITS				):
   lowerCAmelCase     =			x.device

   lowerCAmelCase     =			(x > 0).int()
   lowerCAmelCase     =			2 ** torch.arange(bits - 1	, -1	, -1	, device=lowerCamelCase	, dtype=torch.intaa				)

   lowerCAmelCase     =			rearrange(lowerCamelCase	, 'd -> d 1 1'				)
   lowerCAmelCase     =			rearrange(lowerCamelCase	, 'b (c d) h w -> b c d h w'	, d=8				)
   lowerCAmelCase     =			reduce(x * mask	, 'b c d h w -> b c h w'	, 'sum'				)
   return (dec / 255).clamp(0.0	, 1.0				)





def 	a_					( self	:     Optional[int]	, lowerCamelCase	:     torch.FloatTensor	, lowerCamelCase	:     int	, lowerCamelCase	:     torch.FloatTensor	, lowerCamelCase	:     float = 0.0	, lowerCamelCase	:     bool = True	, lowerCamelCase	:     Any=None	, lowerCamelCase	:     bool = True	, ):
   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     =			timestep - self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   lowerCAmelCase     =			self.alphas_cumprod[timestep]
   lowerCAmelCase     =			self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

   lowerCAmelCase     =			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     =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

   # 4. Clip "predicted x_0"
   lowerCAmelCase     =			self.bit_scale
   if self.config.clip_sample:
      lowerCAmelCase     =			torch.clamp(lowerCamelCase	, -scale	, lowerCamelCase				)

   # 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     =			self._get_variance(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      lowerCAmelCase     =			(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     =			(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     =			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     =			model_output.device if torch.is_tensor(lowerCamelCase				) else 'cpu'
      lowerCAmelCase     =			torch.randn(model_output.shape	, dtype=model_output.dtype	, generator=lowerCamelCase				).to(lowerCamelCase				)
      lowerCAmelCase     =			self._get_variance(lowerCamelCase	, lowerCamelCase				) ** 0.5 * eta * noise

      lowerCAmelCase     =			prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=lowerCamelCase	, pred_original_sample=lowerCamelCase				)





def 	a_					( self	:     List[Any]	, lowerCamelCase	:     torch.FloatTensor	, lowerCamelCase	:     int	, lowerCamelCase	:     torch.FloatTensor	, lowerCamelCase	:     List[Any]="epsilon"	, lowerCamelCase	:     Optional[Any]=None	, lowerCamelCase	:     bool = True	, ):
   lowerCAmelCase     =			timestep

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

   # 1. compute alphas, betas
   lowerCAmelCase     =			self.alphas_cumprod[t]
   lowerCAmelCase     =			self.alphas_cumprod[t - 1] if t > 0 else self.one
   lowerCAmelCase     =			1 - alpha_prod_t
   lowerCAmelCase     =			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     =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif prediction_type == "sample":
      lowerCAmelCase     =			model_output
   else:
      raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''				)

   # 3. Clip "predicted x_0"
   lowerCAmelCase     =			self.bit_scale
   if self.config.clip_sample:
      lowerCAmelCase     =			torch.clamp(lowerCamelCase	, -scale	, lowerCamelCase				)

   # 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     =			(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
   lowerCAmelCase     =			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     =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

   lowerCAmelCase     =			pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=lowerCamelCase	, pred_original_sample=lowerCamelCase				)




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      int    ,      UpperCAmelCase__       :      UNetaDConditionModel    ,      UpperCAmelCase__       :      Union[DDIMScheduler, DDPMScheduler]    ,      UpperCAmelCase__       :      Optional[float] = 1.0    ,      )				->  int:
        super().__init__()
        lowerCAmelCase     =			bit_scale
        lowerCAmelCase     =			(
            ddim_bit_scheduler_step if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else ddpm_bit_scheduler_step
        )

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





     @torch.no_grad()
     def __call__( self       :      Tuple    ,      UpperCAmelCase__       :      Optional[int] = 2_5_6    ,      UpperCAmelCase__       :      Optional[int] = 2_5_6    ,      UpperCAmelCase__       :      Optional[int] = 5_0    ,      UpperCAmelCase__       :      Optional[torch.Generator] = None    ,      UpperCAmelCase__       :      Optional[int] = 1    ,      UpperCAmelCase__       :      Optional[str] = "pil"    ,      UpperCAmelCase__       :      bool = True    ,      **UpperCAmelCase__       :      str    ,      )				->  Union[Tuple, ImagePipelineOutput]:
        lowerCAmelCase     =			torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)    ,      generator=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			decimal_to_bits(UpperCAmelCase__    ) * self.bit_scale
        lowerCAmelCase     =			latents.to(self.device    )

        self.scheduler.set_timesteps(UpperCAmelCase__    )

        for t in self.progress_bar(self.scheduler.timesteps    ):
           # predict the noise residual
           lowerCAmelCase     =			self.unet(UpperCAmelCase__    ,      UpperCAmelCase__    ).sample

           # compute the previous noisy sample x_t -> x_t-1
           lowerCAmelCase     =			self.scheduler.step(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ).prev_sample

        lowerCAmelCase     =			bits_to_decimal(UpperCAmelCase__    )

        if output_type == "pil":
           lowerCAmelCase     =			self.numpy_to_pil(UpperCAmelCase__    )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=UpperCAmelCase__    )









'''simple docstring'''




import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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





def 	a_					( lowerCamelCase	:     Any				):
   lowerCAmelCase     =			OrderedDict()
   for key, value in state_dict.items():
      if key.startswith('module.encoder'				):
         lowerCAmelCase     =			key.replace('module.encoder'	, 'glpn.encoder'				)
      if key.startswith('module.decoder'				):
         lowerCAmelCase     =			key.replace('module.decoder'	, 'decoder.stages'				)
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         lowerCAmelCase     =			key[key.find('patch_embed'				) + len('patch_embed'				)]
         lowerCAmelCase     =			key.replace(f'''patch_embed{idx}'''	, f'''patch_embeddings.{int(lowerCamelCase				)-1}'''				)
      if "norm" in key:
         lowerCAmelCase     =			key.replace('norm'	, 'layer_norm'				)
      if "glpn.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         lowerCAmelCase     =			key[key.find('glpn.encoder.layer_norm'				) + len('glpn.encoder.layer_norm'				)]
         lowerCAmelCase     =			key.replace(f'''layer_norm{idx}'''	, f'''layer_norm.{int(lowerCamelCase				)-1}'''				)
      if "layer_norm1" in key:
         lowerCAmelCase     =			key.replace('layer_norm1'	, 'layer_norm_1'				)
      if "layer_norm2" in key:
         lowerCAmelCase     =			key.replace('layer_norm2'	, 'layer_norm_2'				)
      if "block" in key:
         # replace for example block1 by block.0
         lowerCAmelCase     =			key[key.find('block'				) + len('block'				)]
         lowerCAmelCase     =			key.replace(f'''block{idx}'''	, f'''block.{int(lowerCamelCase				)-1}'''				)
      if "attn.q" in key:
         lowerCAmelCase     =			key.replace('attn.q'	, 'attention.self.query'				)
      if "attn.proj" in key:
         lowerCAmelCase     =			key.replace('attn.proj'	, 'attention.output.dense'				)
      if "attn" in key:
         lowerCAmelCase     =			key.replace('attn'	, 'attention.self'				)
      if "fc1" in key:
         lowerCAmelCase     =			key.replace('fc1'	, 'dense1'				)
      if "fc2" in key:
         lowerCAmelCase     =			key.replace('fc2'	, 'dense2'				)
      if "linear_pred" in key:
         lowerCAmelCase     =			key.replace('linear_pred'	, 'classifier'				)
      if "linear_fuse" in key:
         lowerCAmelCase     =			key.replace('linear_fuse.conv'	, 'linear_fuse'				)
         lowerCAmelCase     =			key.replace('linear_fuse.bn'	, 'batch_norm'				)
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         lowerCAmelCase     =			key[key.find('linear_c'				) + len('linear_c'				)]
         lowerCAmelCase     =			key.replace(f'''linear_c{idx}'''	, f'''linear_c.{int(lowerCamelCase				)-1}'''				)
      if "bot_conv" in key:
         lowerCAmelCase     =			key.replace('bot_conv'	, '0.convolution'				)
      if "skip_conv1" in key:
         lowerCAmelCase     =			key.replace('skip_conv1'	, '1.convolution'				)
      if "skip_conv2" in key:
         lowerCAmelCase     =			key.replace('skip_conv2'	, '2.convolution'				)
      if "fusion1" in key:
         lowerCAmelCase     =			key.replace('fusion1'	, '1.fusion'				)
      if "fusion2" in key:
         lowerCAmelCase     =			key.replace('fusion2'	, '2.fusion'				)
      if "fusion3" in key:
         lowerCAmelCase     =			key.replace('fusion3'	, '3.fusion'				)
      if "fusion" in key and "conv" in key:
         lowerCAmelCase     =			key.replace('conv'	, 'convolutional_layer'				)
      if key.startswith('module.last_layer_depth'				):
         lowerCAmelCase     =			key.replace('module.last_layer_depth'	, 'head.head'				)
      lowerCAmelCase     =			value

   return new_state_dict





def 	a_					( lowerCamelCase	:     List[str]	, lowerCamelCase	:     str				):
   # for each of the encoder blocks:
   for i in range(config.num_encoder_blocks				):
      for j in range(config.depths[i]				):
         # read in weights + bias of keys and values (which is a single matrix in the original implementation)
         lowerCAmelCase     =			state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''				)
         lowerCAmelCase     =			state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias'''				)
         # next, add keys and values (in that order) to the state dict
         lowerCAmelCase     =			kv_weight[
             : config.hidden_sizes[i], :
         ]
         lowerCAmelCase     =			kv_bias[: config.hidden_sizes[i]]
         lowerCAmelCase     =			kv_weight[
             config.hidden_sizes[i] :, :
         ]
         lowerCAmelCase     =			kv_bias[config.hidden_sizes[i] :]





def 	a_					( ):
   lowerCAmelCase     =			'http://images.cocodataset.org/val2017/000000039769.jpg'
   lowerCAmelCase     =			Image.open(requests.get(lowerCamelCase	, stream=lowerCamelCase				).raw				)

   return image





@torch.no_grad()
def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[Any]=False	, lowerCamelCase	:     List[str]=None				):
   lowerCAmelCase     =			GLPNConfig(hidden_sizes=[64, 128, 320, 512]	, decoder_hidden_size=64	, depths=[3, 8, 27, 3]				)

   # load image processor (only resize + rescale)
   lowerCAmelCase     =			GLPNImageProcessor()

   # prepare image
   lowerCAmelCase     =			prepare_img()
   lowerCAmelCase     =			image_processor(images=lowerCamelCase	, return_tensors='pt'				).pixel_values

   logger.info('Converting model...'				)

   # load original state dict
   lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location=torch.device('cpu'				)				)

   # rename keys
   lowerCAmelCase     =			rename_keys(lowerCamelCase				)

   # key and value matrices need special treatment
   read_in_k_v(lowerCamelCase	, lowerCamelCase				)

   # create HuggingFace model and load state dict
   lowerCAmelCase     =			GLPNForDepthEstimation(lowerCamelCase				)
   model.load_state_dict(lowerCamelCase				)
   model.eval()

   # forward pass
   lowerCAmelCase     =			model(lowerCamelCase				)
   lowerCAmelCase     =			outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         lowerCAmelCase     =			torch.tensor(
             [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]				)
      elif "kitti" in model_name:
         lowerCAmelCase     =			torch.tensor(
             [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]				)
      else:
         raise ValueError(f'''Unknown model name: {model_name}'''				)

      lowerCAmelCase     =			torch.Size([1, 480, 640]				)

      assert predicted_depth.shape == expected_shape
      assert torch.allclose(predicted_depth[0, :3, :3]	, lowerCamelCase	, atol=1e-4				)
      print('Looks ok!'				)

   # finally, push to hub if required
   if push_to_hub:
      logger.info('Pushing model and image processor to the hub...'				)
      model.push_to_hub(
          repo_path_or_name=Path(lowerCamelCase	, lowerCamelCase				)	, organization='nielsr'	, commit_message='Add model'	, use_temp_dir=lowerCamelCase	, )
      image_processor.push_to_hub(
          repo_path_or_name=Path(lowerCamelCase	, lowerCamelCase				)	, organization='nielsr'	, commit_message='Add image processor'	, use_temp_dir=lowerCamelCase	, )


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

       parser.add_argument(
           """--checkpoint_path""",
           default=None,
           type=str,
           help="""Path 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""", help="""Whether to upload the model to the HuggingFace hub."""
       )
       parser.add_argument(
           """--model_name""",
           default="""glpn-kitti""",
           type=str,
           help="""Name of the model in case you're pushing to the hub.""",
       )
       __snake_case    						=parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





