


'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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





def 					a__       (					lowerCAmelCase__						)    ->	List[Any]:
    UpperCAmelCase__			:		List[str]													=				DPTConfig(embedding_type='''hybrid'''						)

    if "large" in checkpoint_url:
        UpperCAmelCase__			:		Union[str, Any]													=				10_24
        UpperCAmelCase__			:		List[str]													=				40_96
        UpperCAmelCase__			:		str													=				24
        UpperCAmelCase__			:		List[Any]													=				16
        UpperCAmelCase__			:		Optional[Any]													=				[5, 11, 17, 23]
        UpperCAmelCase__			:		str													=				[2_56, 5_12, 10_24, 10_24]
        UpperCAmelCase__			:		Any													=				(1, 3_84, 3_84)

    if "nyu" or "midas" in checkpoint_url:
        UpperCAmelCase__			:		Union[str, Any]													=				7_68
        UpperCAmelCase__			:		int													=				[1, 1, 1, 0.5]
        UpperCAmelCase__			:		Any													=				[2_56, 5_12, 7_68, 7_68]
        UpperCAmelCase__			:		List[str]													=				1_50
        UpperCAmelCase__			:		Optional[int]													=				16
        UpperCAmelCase__			:		Union[str, Any]													=				(1, 3_84, 3_84)
        UpperCAmelCase__			:		str													=				False
        UpperCAmelCase__			:		Optional[Any]													=				'''project'''

    if "ade" in checkpoint_url:
        UpperCAmelCase__			:		Optional[Any]													=				True
        UpperCAmelCase__			:		int													=				7_68
        UpperCAmelCase__			:		Optional[Any]													=				[1, 1, 1, 0.5]
        UpperCAmelCase__			:		int													=				1_50
        UpperCAmelCase__			:		Tuple													=				16
        UpperCAmelCase__			:		Optional[Any]													=				'''huggingface/label-files'''
        UpperCAmelCase__			:		int													=				'''ade20k-id2label.json'''
        UpperCAmelCase__			:		Union[str, Any]													=				json.load(open(cached_download(hf_hub_url(lowerCAmelCase__  ,					lowerCAmelCase__  ,					repo_type='''dataset'''						)						)  ,					'''r'''						)						)
        UpperCAmelCase__			:		str													=				{int(lowerCAmelCase__						): v for k, v in idalabel.items()}
        UpperCAmelCase__			:		Optional[int]													=				idalabel
        UpperCAmelCase__			:		Optional[int]													=				{v: k for k, v in idalabel.items()}
        UpperCAmelCase__			:		int													=				[1, 1_50, 4_80, 4_80]

    return config, expected_shape





def 					a__       (					lowerCAmelCase__						)    ->	Optional[int]:
    UpperCAmelCase__			:		Optional[Any]													=				['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase__  ,					lowerCAmelCase__						)





def 					a__       (					lowerCAmelCase__						)    ->	Tuple:
    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        UpperCAmelCase__			:		Optional[Any]													=				name.replace('''pretrained.model'''  ,					'''dpt.encoder'''						)
    if "pretrained.model" in name:
        UpperCAmelCase__			:		List[Any]													=				name.replace('''pretrained.model'''  ,					'''dpt.embeddings'''						)
    if "patch_embed" in name:
        UpperCAmelCase__			:		List[str]													=				name.replace('''patch_embed'''  ,					''''''						)
    if "pos_embed" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''pos_embed'''  ,					'''position_embeddings'''						)
    if "attn.proj" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''attn.proj'''  ,					'''attention.output.dense'''						)
    if "proj" in name and "project" not in name:
        UpperCAmelCase__			:		Tuple													=				name.replace('''proj'''  ,					'''projection'''						)
    if "blocks" in name:
        UpperCAmelCase__			:		Tuple													=				name.replace('''blocks'''  ,					'''layer'''						)
    if "mlp.fc1" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''mlp.fc1'''  ,					'''intermediate.dense'''						)
    if "mlp.fc2" in name:
        UpperCAmelCase__			:		Tuple													=				name.replace('''mlp.fc2'''  ,					'''output.dense'''						)
    if "norm1" in name and "backbone" not in name:
        UpperCAmelCase__			:		Any													=				name.replace('''norm1'''  ,					'''layernorm_before'''						)
    if "norm2" in name and "backbone" not in name:
        UpperCAmelCase__			:		str													=				name.replace('''norm2'''  ,					'''layernorm_after'''						)
    if "scratch.output_conv" in name:
        UpperCAmelCase__			:		List[Any]													=				name.replace('''scratch.output_conv'''  ,					'''head'''						)
    if "scratch" in name:
        UpperCAmelCase__			:		str													=				name.replace('''scratch'''  ,					'''neck'''						)
    if "layer1_rn" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''layer1_rn'''  ,					'''convs.0'''						)
    if "layer2_rn" in name:
        UpperCAmelCase__			:		Optional[int]													=				name.replace('''layer2_rn'''  ,					'''convs.1'''						)
    if "layer3_rn" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''layer3_rn'''  ,					'''convs.2'''						)
    if "layer4_rn" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''layer4_rn'''  ,					'''convs.3'''						)
    if "refinenet" in name:
        UpperCAmelCase__			:		Any													=				int(name[len('''neck.refinenet'''						) : len('''neck.refinenet'''						) + 1]						)
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace(F"""refinenet{layer_idx}"""  ,					F"""fusion_stage.layers.{abs(layer_idx-4						)}"""						)
    if "out_conv" in name:
        UpperCAmelCase__			:		List[str]													=				name.replace('''out_conv'''  ,					'''projection'''						)
    if "resConfUnit1" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''resConfUnit1'''  ,					'''residual_layer1'''						)
    if "resConfUnit2" in name:
        UpperCAmelCase__			:		str													=				name.replace('''resConfUnit2'''  ,					'''residual_layer2'''						)
    if "conv1" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''conv1'''  ,					'''convolution1'''						)
    if "conv2" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''conv2'''  ,					'''convolution2'''						)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''pretrained.act_postprocess1.0.project.0'''  ,					'''neck.reassemble_stage.readout_projects.0.0'''						)
    if "pretrained.act_postprocess2.0.project.0" in name:
        UpperCAmelCase__			:		List[str]													=				name.replace('''pretrained.act_postprocess2.0.project.0'''  ,					'''neck.reassemble_stage.readout_projects.1.0'''						)
    if "pretrained.act_postprocess3.0.project.0" in name:
        UpperCAmelCase__			:		int													=				name.replace('''pretrained.act_postprocess3.0.project.0'''  ,					'''neck.reassemble_stage.readout_projects.2.0'''						)
    if "pretrained.act_postprocess4.0.project.0" in name:
        UpperCAmelCase__			:		Dict													=				name.replace('''pretrained.act_postprocess4.0.project.0'''  ,					'''neck.reassemble_stage.readout_projects.3.0'''						)

    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        UpperCAmelCase__			:		Tuple													=				name.replace('''pretrained.act_postprocess1.3'''  ,					'''neck.reassemble_stage.layers.0.projection'''						)
    if "pretrained.act_postprocess1.4" in name:
        UpperCAmelCase__			:		str													=				name.replace('''pretrained.act_postprocess1.4'''  ,					'''neck.reassemble_stage.layers.0.resize'''						)
    if "pretrained.act_postprocess2.3" in name:
        UpperCAmelCase__			:		Optional[int]													=				name.replace('''pretrained.act_postprocess2.3'''  ,					'''neck.reassemble_stage.layers.1.projection'''						)
    if "pretrained.act_postprocess2.4" in name:
        UpperCAmelCase__			:		Optional[int]													=				name.replace('''pretrained.act_postprocess2.4'''  ,					'''neck.reassemble_stage.layers.1.resize'''						)
    if "pretrained.act_postprocess3.3" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''pretrained.act_postprocess3.3'''  ,					'''neck.reassemble_stage.layers.2.projection'''						)
    if "pretrained.act_postprocess4.3" in name:
        UpperCAmelCase__			:		List[Any]													=				name.replace('''pretrained.act_postprocess4.3'''  ,					'''neck.reassemble_stage.layers.3.projection'''						)
    if "pretrained.act_postprocess4.4" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''pretrained.act_postprocess4.4'''  ,					'''neck.reassemble_stage.layers.3.resize'''						)
    if "pretrained" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''pretrained'''  ,					'''dpt'''						)
    if "bn" in name:
        UpperCAmelCase__			:		Optional[int]													=				name.replace('''bn'''  ,					'''batch_norm'''						)
    if "head" in name:
        UpperCAmelCase__			:		List[str]													=				name.replace('''head'''  ,					'''head.head'''						)
    if "encoder.norm" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''encoder.norm'''  ,					'''layernorm'''						)
    if "auxlayer" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''auxlayer'''  ,					'''auxiliary_head.head'''						)
    if "backbone" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''backbone'''  ,					'''backbone.bit.encoder'''						)

    if ".." in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''..'''  ,					'''.'''						)

    if "stem.conv" in name:
        UpperCAmelCase__			:		Union[str, Any]													=				name.replace('''stem.conv'''  ,					'''bit.embedder.convolution'''						)
    if "blocks" in name:
        UpperCAmelCase__			:		List[Any]													=				name.replace('''blocks'''  ,					'''layers'''						)
    if "convolution" in name and "backbone" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''convolution'''  ,					'''conv'''						)
    if "layer" in name and "backbone" in name:
        UpperCAmelCase__			:		Optional[int]													=				name.replace('''layer'''  ,					'''layers'''						)
    if "backbone.bit.encoder.bit" in name:
        UpperCAmelCase__			:		List[str]													=				name.replace('''backbone.bit.encoder.bit'''  ,					'''backbone.bit'''						)
    if "embedder.conv" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''embedder.conv'''  ,					'''embedder.convolution'''						)
    if "backbone.bit.encoder.stem.norm" in name:
        UpperCAmelCase__			:		Optional[Any]													=				name.replace('''backbone.bit.encoder.stem.norm'''  ,					'''backbone.bit.embedder.norm'''						)
    return name





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    for i in range(config.num_hidden_layers						):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCAmelCase__			:		List[str]													=				state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""						)
        UpperCAmelCase__			:		Union[str, Any]													=				state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""						)
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase__			:		Tuple													=				in_proj_weight[: config.hidden_size, :]
        UpperCAmelCase__			:		List[Any]													=				in_proj_bias[: config.hidden_size]
        UpperCAmelCase__			:		List[Any]													=				in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase__			:		Dict													=				in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCAmelCase__			:		Union[str, Any]													=				in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase__			:		Optional[Any]													=				in_proj_bias[-config.hidden_size :]





def 					a__       (					)    ->	Dict:
    UpperCAmelCase__			:		Optional[int]													=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase__			:		Dict													=				Image.open(requests.get(lowerCAmelCase__  ,					stream=lowerCAmelCase__						).raw						)
    return im





@torch.no_grad()
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	List[Any]:
    UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				get_dpt_config(lowerCAmelCase__						)
    # load original state_dict from URL
    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    UpperCAmelCase__			:		List[str]													=				torch.load(lowerCAmelCase__  ,					map_location='''cpu'''						)
    # remove certain keys
    remove_ignore_keys_(lowerCAmelCase__						)
    # rename keys
    for key in state_dict.copy().keys():
        UpperCAmelCase__			:		List[Any]													=				state_dict.pop(lowerCAmelCase__						)
        UpperCAmelCase__			:		int													=				val
    # read in qkv matrices
    read_in_q_k_v(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # load HuggingFace model
    UpperCAmelCase__			:		str													=				DPTForSemanticSegmentation(lowerCAmelCase__						) if '''ade''' in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase__						)
    model.load_state_dict(lowerCAmelCase__						)
    model.eval()

    # Check outputs on an image
    UpperCAmelCase__			:		int													=				4_80 if '''ade''' in checkpoint_url else 3_84
    UpperCAmelCase__			:		List[Any]													=				DPTImageProcessor(size=lowerCAmelCase__						)

    UpperCAmelCase__			:		Optional[int]													=				prepare_img()
    UpperCAmelCase__			:		int													=				image_processor(lowerCAmelCase__  ,					return_tensors='''pt'''						)

    # forward pass
    UpperCAmelCase__			:		Dict													=				model(**lowerCAmelCase__						).logits if '''ade''' in checkpoint_url else model(**lowerCAmelCase__						).predicted_depth

    if show_prediction:
        UpperCAmelCase__			:		Optional[int]													=				(
            torch.nn.functional.interpolate(
                outputs.unsqueeze(1						)  ,					size=(image.size[1], image.size[0])  ,					mode='''bicubic'''  ,					align_corners=lowerCAmelCase__  ,					)
            .squeeze()
            .cpu()
            .numpy()
        )

        Image.fromarray((prediction / prediction.max()) * 2_55						).show()

    if pytorch_dump_folder_path is not None:
        Path(lowerCAmelCase__						).mkdir(exist_ok=lowerCAmelCase__						)
        print(F"""Saving model to {pytorch_dump_folder_path}"""						)
        model.save_pretrained(lowerCAmelCase__						)
        print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
        image_processor.save_pretrained(lowerCAmelCase__						)

    if push_to_hub:
        model.push_to_hub('''ybelkada/dpt-hybrid-midas'''						)
        image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''						)


if __name__ == "__main__":
  UpperCamelCase__    					=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
      type=str,
      help='''URL of the original DPT checkpoint you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      required=False,
      help='''Path to the output PyTorch model directory.''',
  )
  parser.add_argument(
      '''--push_to_hub''',
      action='''store_true''',
  )
  parser.add_argument(
      '''--model_name''',
      default='''dpt-large''',
      type=str,
      help='''Name of the model, in case you\'re pushing to the hub.''',
  )
  parser.add_argument(
      '''--show_prediction''',
      action='''store_true''',
  )

  UpperCamelCase__    					=      parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )




'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    str ):


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=_A						,						scheduler=_A )






      @torch.no_grad()
      def __call__(					self				:    Union[str, Any]						,						_A				:    int = 1						,						_A				:    int = 100						,						_A				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None						,						_A				:    Optional[float] = None						,						_A				:    bool = True						,						):


          '''simple docstring'''


          if audio_length_in_s is None:
              UpperCAmelCase__			:		List[str]													=				self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__			:		Dict													=				audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__			:		Union[str, Any]													=				2 ** len(self.unet.up_blocks )
          if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                  f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )

          UpperCAmelCase__			:		Optional[Any]													=				int(_A )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__			:		List[str]													=				(
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                  f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                  ''' process.''' )
          UpperCAmelCase__			:		Union[str, Any]													=				int(_A )

          UpperCAmelCase__			:		Any													=				next(iter(self.unet.parameters() ) ).dtype
          UpperCAmelCase__			:		Union[str, Any]													=				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(_A						,						_A ) and len(_A ) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

          UpperCAmelCase__			:		int													=				randn_tensor(_A						,						generator=_A						,						device=self.device						,						dtype=_A )

          # set step values
          self.scheduler.set_timesteps(_A						,						device=audio.device )
          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.timesteps.to(_A )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # 1. predict noise model_output
              UpperCAmelCase__			:		Any													=				self.unet(_A						,						_A ).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.step(_A						,						_A						,						_A ).prev_sample

          UpperCAmelCase__			:		Any													=				audio.clamp(-1						,						1 ).float().cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A )
