


"""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()
lowerCamelCase_       :       Any      						=  logging.get_logger(__name__)
def _A						(						lowercase		):







    """simple docstring"""
    a					=DPTConfig()

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

    if "ade" in checkpoint_url:
        a					=True

        a					=1_50
        a					='''huggingface/label-files'''
        a					='''ade20k-id2label.json'''
        a					=json.load(open(cached_download(hf_hub_url(lowercase	,   lowercase	,   repo_type='''dataset'''		)		)	,   '''r'''		)		)
        a					={int(lowercase		): v for k, v in idalabel.items()}
        a					=idalabel
        a					={v: k for k, v in idalabel.items()}
        a					=[1, 1_50, 4_80, 4_80]

    return config, expected_shape
def _A						(						lowercase		):







    """simple docstring"""
    a					=['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
    for k in ignore_keys:
        state_dict.pop(lowercase	,   lowercase		)
def _A						(						lowercase		):







    """simple docstring"""
    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        a					=name.replace('''pretrained.model'''	,   '''dpt.encoder'''		)
    if "pretrained.model" in name:
        a					=name.replace('''pretrained.model'''	,   '''dpt.embeddings'''		)
    if "patch_embed" in name:
        a					=name.replace('''patch_embed'''	,   '''patch_embeddings'''		)
    if "pos_embed" in name:
        a					=name.replace('''pos_embed'''	,   '''position_embeddings'''		)
    if "attn.proj" in name:
        a					=name.replace('''attn.proj'''	,   '''attention.output.dense'''		)
    if "proj" in name and "project" not in name:
        a					=name.replace('''proj'''	,   '''projection'''		)
    if "blocks" in name:
        a					=name.replace('''blocks'''	,   '''layer'''		)
    if "mlp.fc1" in name:
        a					=name.replace('''mlp.fc1'''	,   '''intermediate.dense'''		)
    if "mlp.fc2" in name:
        a					=name.replace('''mlp.fc2'''	,   '''output.dense'''		)
    if "norm1" in name:
        a					=name.replace('''norm1'''	,   '''layernorm_before'''		)
    if "norm2" in name:
        a					=name.replace('''norm2'''	,   '''layernorm_after'''		)
    if "scratch.output_conv" in name:
        a					=name.replace('''scratch.output_conv'''	,   '''head'''		)
    if "scratch" in name:
        a					=name.replace('''scratch'''	,   '''neck'''		)
    if "layer1_rn" in name:
        a					=name.replace('''layer1_rn'''	,   '''convs.0'''		)
    if "layer2_rn" in name:
        a					=name.replace('''layer2_rn'''	,   '''convs.1'''		)
    if "layer3_rn" in name:
        a					=name.replace('''layer3_rn'''	,   '''convs.2'''		)
    if "layer4_rn" in name:
        a					=name.replace('''layer4_rn'''	,   '''convs.3'''		)
    if "refinenet" in name:
        a					=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
        a					=name.replace(f'''refinenet{layer_idx}'''	,   f'''fusion_stage.layers.{abs(layer_idx-4		)}'''		)
    if "out_conv" in name:
        a					=name.replace('''out_conv'''	,   '''projection'''		)
    if "resConfUnit1" in name:
        a					=name.replace('''resConfUnit1'''	,   '''residual_layer1'''		)
    if "resConfUnit2" in name:
        a					=name.replace('''resConfUnit2'''	,   '''residual_layer2'''		)
    if "conv1" in name:
        a					=name.replace('''conv1'''	,   '''convolution1'''		)
    if "conv2" in name:
        a					=name.replace('''conv2'''	,   '''convolution2'''		)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        a					=name.replace('''pretrained.act_postprocess1.0.project.0'''	,   '''neck.reassemble_stage.readout_projects.0.0'''		)
    if "pretrained.act_postprocess2.0.project.0" in name:
        a					=name.replace('''pretrained.act_postprocess2.0.project.0'''	,   '''neck.reassemble_stage.readout_projects.1.0'''		)
    if "pretrained.act_postprocess3.0.project.0" in name:
        a					=name.replace('''pretrained.act_postprocess3.0.project.0'''	,   '''neck.reassemble_stage.readout_projects.2.0'''		)
    if "pretrained.act_postprocess4.0.project.0" in name:
        a					=name.replace('''pretrained.act_postprocess4.0.project.0'''	,   '''neck.reassemble_stage.readout_projects.3.0'''		)
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        a					=name.replace('''pretrained.act_postprocess1.3'''	,   '''neck.reassemble_stage.layers.0.projection'''		)
    if "pretrained.act_postprocess1.4" in name:
        a					=name.replace('''pretrained.act_postprocess1.4'''	,   '''neck.reassemble_stage.layers.0.resize'''		)
    if "pretrained.act_postprocess2.3" in name:
        a					=name.replace('''pretrained.act_postprocess2.3'''	,   '''neck.reassemble_stage.layers.1.projection'''		)
    if "pretrained.act_postprocess2.4" in name:
        a					=name.replace('''pretrained.act_postprocess2.4'''	,   '''neck.reassemble_stage.layers.1.resize'''		)
    if "pretrained.act_postprocess3.3" in name:
        a					=name.replace('''pretrained.act_postprocess3.3'''	,   '''neck.reassemble_stage.layers.2.projection'''		)
    if "pretrained.act_postprocess4.3" in name:
        a					=name.replace('''pretrained.act_postprocess4.3'''	,   '''neck.reassemble_stage.layers.3.projection'''		)
    if "pretrained.act_postprocess4.4" in name:
        a					=name.replace('''pretrained.act_postprocess4.4'''	,   '''neck.reassemble_stage.layers.3.resize'''		)
    if "pretrained" in name:
        a					=name.replace('''pretrained'''	,   '''dpt'''		)
    if "bn" in name:
        a					=name.replace('''bn'''	,   '''batch_norm'''		)
    if "head" in name:
        a					=name.replace('''head'''	,   '''head.head'''		)
    if "encoder.norm" in name:
        a					=name.replace('''encoder.norm'''	,   '''layernorm'''		)
    if "auxlayer" in name:
        a					=name.replace('''auxlayer'''	,   '''auxiliary_head.head'''		)

    return name
def _A						(						lowercase	,   lowercase		):







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







    """simple docstring"""
    a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    a					=Image.open(requests.get(lowercase	,   stream=lowercase		).raw		)
    return im
@torch.no_grad()
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    a		,				a					=get_dpt_config(lowercase		)
    # load original state_dict from URL
    a					=torch.hub.load_state_dict_from_url(lowercase	,   map_location='''cpu'''		)
    # remove certain keys
    remove_ignore_keys_(lowercase		)
    # rename keys
    for key in state_dict.copy().keys():
        a					=state_dict.pop(lowercase		)
        a					=val
    # read in qkv matrices
    read_in_q_k_v(lowercase	,   lowercase		)

    # load HuggingFace model
    a					=DPTForSemanticSegmentation(lowercase		) if '''ade''' in checkpoint_url else DPTForDepthEstimation(lowercase		)
    model.load_state_dict(lowercase		)
    model.eval()

    # Check outputs on an image
    a					=4_80 if '''ade''' in checkpoint_url else 3_84
    a					=DPTImageProcessor(size=lowercase		)

    a					=prepare_img()
    a					=image_processor(lowercase	,   return_tensors='''pt'''		)

    # forward pass
    a					=model(**lowercase		).logits if '''ade''' in checkpoint_url else model(**lowercase		).predicted_depth

    # Assert logits
    a					=torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]		)
    if "ade" in checkpoint_url:
        a					=torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]		)
    assert outputs.shape == torch.Size(lowercase		)
    assert (
        torch.allclose(outputs[0, 0, :3, :3]	,   lowercase	,   atol=1E-4		)
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3]	,   lowercase		)
    )

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

    if push_to_hub:
        print('''Pushing model to hub...'''		)
        model.push_to_hub(
            repo_path_or_name=Path(lowercase	,   lowercase		)	,   organization='''nielsr'''	,   commit_message='''Add model'''	,   use_temp_dir=lowercase	,   )
        image_processor.push_to_hub(
            repo_path_or_name=Path(lowercase	,   lowercase		)	,   organization='''nielsr'''	,   commit_message='''Add image processor'''	,   use_temp_dir=lowercase	,   )


if __name__ == "__main__":
       lowerCamelCase_       :       Optional[Any]      						=  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=True,
           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.""",
       )

       lowerCamelCase_       :       Union[str, Any]      						=  parser.parse_args()
       convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)



"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class 				__A    :






 """simple docstring"""




 __lowerCAmelCase			 =  42

 # setable values
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  42
 __lowerCAmelCase			 =  None


 @classmethod
 def 				SCREAMING_SNAKE_CASE							(		cls			,   __A			,   __A			,   __A						) ->					List[str]:
     return cls(common=__A			,   init_noise_sigma=__A			,   timesteps=__A						)






@dataclass
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  42






class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCAmelCase			 =  42


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return True


 @register_to_config
 def __init__(		self			,   __A = 1000			,   __A = 0.0_001			,   __A = 0.02			,   __A = "linear"			,   __A = None			,   __A = "fixed_small"			,   __A = True			,   __A = "epsilon"			,   __A = jnp.floataa			,   ) ->					List[Any]:
     a					=dtype


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A = None						) ->					DDPMSchedulerState:
     if common is None:
         a					=CommonSchedulerState.create(self						)

     # standard deviation of the initial noise distribution
     a					=jnp.array(1.0			,   dtype=self.dtype						)

     a					=jnp.arange(0			,   self.config.num_train_timesteps						).round()[::-1]

     return DDPMSchedulerState.create(
         common=__A			,   init_noise_sigma=__A			,   timesteps=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None						) ->					jnp.ndarray:
     return sample


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = ()						) ->					DDPMSchedulerState:
     a					=self.config.num_train_timesteps // num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # rounding to avoid issues when num_inference_step is power of 3
     a					=(jnp.arange(0			,   __A						) * step_ratio).round()[::-1]

     return state.replace(
         num_inference_steps=__A			,   timesteps=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A=None			,   __A=None						) ->					str:
     a					=state.common.alphas_cumprod[t]
     a					=jnp.where(t > 0			,   state.common.alphas_cumprod[t - 1]			,   jnp.array(1.0			,   dtype=self.dtype						)						)

     # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
     # and sample from it to get previous sample
     # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
     a					=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         a					=self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         a					=jnp.clip(__A			,   a_min=1E-2_0						)
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         a					=jnp.log(jnp.clip(__A			,   a_min=1E-2_0						)						)
     elif variance_type == "fixed_large":
         a					=state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         a					=jnp.log(state.common.betas[t]						)
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         a					=variance
         a					=state.common.betas[t]
         a					=(predicted_variance + 1) / 2
         a					=frac * max_log + (1 - frac) * min_log

     return variance


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A = None			,   __A = True			,   ) ->					Union[FlaxDDPMSchedulerOutput, Tuple]:
     a					=timestep

     if key is None:
         a					=jax.random.PRNGKey(0						)

     if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         a		,				a					=jnp.split(__A			,   sample.shape[1]			,   axis=1						)
     else:
         a					=None

     # 1. compute alphas, betas
     a					=state.common.alphas_cumprod[t]
     a					=jnp.where(t > 0			,   state.common.alphas_cumprod[t - 1]			,   jnp.array(1.0			,   dtype=self.dtype						)						)
     a					=1 - alpha_prod_t
     a					=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 self.config.prediction_type == "epsilon":
         a					=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         a					=model_output
     elif self.config.prediction_type == "v_prediction":
         a					=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
             ''' for the FlaxDDPMScheduler.'''						)

     # 3. Clip "predicted x_0"
     if self.config.clip_sample:
         a					=jnp.clip(__A			,   -1			,   1						)

     # 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
     a					=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     a					=state.common.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
     a					=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
         a					=jax.random.split(__A			,   num=1						)
         a					=jax.random.normal(__A			,   shape=model_output.shape			,   dtype=self.dtype						)
         return (self._get_variance(__A			,   __A			,   predicted_variance=__A						) ** 0.5) * noise

     a					=jnp.where(t > 0			,   random_variance()			,   jnp.zeros(model_output.shape			,   dtype=self.dtype						)						)

     a					=pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__A			,   state=__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return add_noise_common(state.common			,   __A			,   __A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					jnp.ndarray:
     return get_velocity_common(state.common			,   __A			,   __A			,   __A						)





 def __len__(		self						) ->					Optional[int]:
     return self.config.num_train_timesteps