
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()
__lowerCamelCase							=       logging.get_logger(__name__)


def 			UpperCamelCase					(    __lowerCamelCase     : Tuple    ):
       snake_case       :   List[str]							=      DPTConfig(embedding_type="hybrid"    )

       if "large" in checkpoint_url:
              snake_case       :   List[str]							=      1024
              snake_case       :   Optional[int]							=      4096
              snake_case       :   int							=      24
              snake_case       :   Any							=      16
              snake_case       :   int							=      [5, 11, 17, 23]
              snake_case       :   Tuple							=      [256, 512, 1024, 1024]
              snake_case       :   int							=      (1, 384, 384)

       if "nyu" or "midas" in checkpoint_url:
              snake_case       :   List[str]							=      768
              snake_case       :   Tuple							=      [1, 1, 1, 0.5]
              snake_case       :   Any							=      [256, 512, 768, 768]
              snake_case       :   Dict							=      150
              snake_case       :   Optional[int]							=      16
              snake_case       :   str							=      (1, 384, 384)
              snake_case       :   List[Any]							=      False
              snake_case       :   List[str]							=      "project"

       if "ade" in checkpoint_url:
              snake_case       :   Any							=      True
              snake_case       :   Dict							=      768
              snake_case       :   Dict							=      [1, 1, 1, 0.5]
              snake_case       :   int							=      150
              snake_case       :   Union[str, Any]							=      16
              snake_case       :   int							=      "huggingface/label-files"
              snake_case       :   Optional[Any]							=      "ade20k-id2label.json"
              snake_case       :   Optional[int]							=      json.load(open(cached_download(hf_hub_url(__lowerCamelCase				,					__lowerCamelCase				,					repo_type="dataset"    )    )				,					"r"    )    )
              snake_case       :   Optional[Any]							=      {int(__lowerCamelCase    ): v for k, v in idalabel.items()}
              snake_case       :   Tuple							=      idalabel
              snake_case       :   Dict							=      {v: k for k, v in idalabel.items()}
              snake_case       :   List[str]							=      [1, 150, 480, 480]

       return config, expected_shape


def 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   Optional[Any]							=      ["pretrained.model.head.weight", "pretrained.model.head.bias"]
       for k in ignore_keys:
              state_dict.pop(__lowerCamelCase				,					__lowerCamelCase    )


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

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

       if ".." in name:
              snake_case       :   int							=      name.replace(".."				,					"."    )

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


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


def 			UpperCamelCase					(    ):
       snake_case       :   Any							=      "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case       :   Union[str, Any]							=      Image.open(requests.get(__lowerCamelCase				,					stream=__lowerCamelCase    ).raw    )
       return im


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int				,					__lowerCamelCase     : List[str]    ):
       snake_case				,					snake_case       :   Any							=      get_dpt_config(__lowerCamelCase    )
       # load original state_dict from URL
       # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
       snake_case       :   str							=      torch.load(__lowerCamelCase				,					map_location="cpu"    )
       # remove certain keys
       remove_ignore_keys_(__lowerCamelCase    )
       # rename keys
       for key in state_dict.copy().keys():
              snake_case       :   Optional[Any]							=      state_dict.pop(__lowerCamelCase    )
              snake_case       :   Optional[int]							=      val
       # read in qkv matrices
       read_in_q_k_v(__lowerCamelCase				,					__lowerCamelCase    )

       # load HuggingFace model
       snake_case       :   Union[str, Any]							=      DPTForSemanticSegmentation(__lowerCamelCase    ) if "ade" in checkpoint_url else DPTForDepthEstimation(__lowerCamelCase    )
       model.load_state_dict(__lowerCamelCase    )
       model.eval()

       # Check outputs on an image
       snake_case       :   Tuple							=      480 if "ade" in checkpoint_url else 384
       snake_case       :   Tuple							=      DPTImageProcessor(size=__lowerCamelCase    )

       snake_case       :   int							=      prepare_img()
       snake_case       :   Tuple							=      image_processor(__lowerCamelCase				,					return_tensors="pt"    )

       # forward pass
       snake_case       :   Tuple							=      model(**__lowerCamelCase    ).logits if "ade" in checkpoint_url else model(**__lowerCamelCase    ).predicted_depth

       if show_prediction:
              snake_case       :   List[Any]							=      (
                  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()) * 255    ).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__":
   __lowerCamelCase							=       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""",
   )

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







import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str] )		->	List[str]:


          '''simple docstring'''


          return f"""gaussian_noise_s={seed}_shape={'_'.join([str(snake_case__ ) for s in shape] )}.npy"""



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          super().tearDown()
          gc.collect()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Optional[Any]=0    ,						snake_case__		:					Any=(4, 4, 64, 64)    ,						snake_case__		:					List[Any]=False )		->	int:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Optional[int]							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return image



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					List[Any]="CompVis/stable-diffusion-v1-4" )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   str							=      "bf16" if fpaa else None

          snake_case				,					snake_case       :   Optional[int]							=      FlaxUNetaDConditionModel.from_pretrained(
              snake_case__    ,						subfolder="unet"    ,						dtype=snake_case__    ,						revision=snake_case__ )
          return model, params



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Union[str, Any]=0    ,						snake_case__		:					Union[str, Any]=(4, 77, 7_68)    ,						snake_case__		:					Dict=False )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Any							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Any							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return hidden_states



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[str]							=      self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_latents(snake_case__    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_encoder_hidden_states(snake_case__    ,						fpaa=snake_case__ )

          snake_case       :   Dict							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   Optional[Any]							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Optional[int]							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					str    ,						snake_case__		:					Tuple )		->	str:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[Any]							=      self.get_unet_model(model_id="stabilityai/stable-diffusion-2"    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_latents(snake_case__    ,						shape=(4, 4, 96, 96)    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_encoder_hidden_states(snake_case__    ,						shape=(4, 77, 10_24)    ,						fpaa=snake_case__ )

          snake_case       :   Optional[int]							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   int							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Dict							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )





