






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]=0.999			,				__lowercase   :			Any="cosine"			,				)						->      str:





    """simple docstring"""

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__lowercase   :			Any    ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__lowercase   :			Union[str, Any]    ):
            return math.exp(t * -12.0    )

    else:
        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"    )

    __A         =	[]
    for i in range(__lowercase    ):
        __A         =	i / num_diffusion_timesteps
        __A         =	(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__lowercase    ) / alpha_bar_fn(__lowercase    )			,				__lowercase    )    )
    return torch.tensor(__lowercase			,				dtype=torch.floataa    )






class       __lowercase      (	lowercase_			,      lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				[e.name for e in KarrasDiffusionSchedulers]
     SCREAMING_SNAKE_CASE   						=				2




     @register_to_config
     def __init__(				self       :			str ,	UpperCamelCase_       :			int = 1_000 ,	UpperCamelCase_       :			float = 0.00085 ,	UpperCamelCase_       :			float = 0.012 ,	UpperCamelCase_       :			str = "linear" ,	UpperCamelCase_       :			Optional[Union[np.ndarray, List[float]]] = None ,	UpperCamelCase_       :			str = "epsilon" ,	UpperCamelCase_       :			str = "linspace" ,	UpperCamelCase_       :			int = 0 ,	):


         """simple docstring"""



         if trained_betas is not None:
             __A         =	torch.tensor(UpperCamelCase_ ,	dtype=torch.floataa				)
         elif beta_schedule == "linear":
             __A         =	torch.linspace(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_ ,	dtype=torch.floataa				)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             __A         =	(
                 torch.linspace(beta_start**0.5 ,	beta_end**0.5 ,	UpperCamelCase_ ,	dtype=torch.floataa				) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __A         =	betas_for_alpha_bar(UpperCamelCase_				)
         else:
             raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"				)

         __A         =	1.0 - self.betas
         __A         =	torch.cumprod(self.alphas ,	dim=0				)

         #  set all values
         self.set_timesteps(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			str=None				):


         """simple docstring"""



         if schedule_timesteps is None:
             __A         =	self.timesteps

         __A         =	(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter				) == 0:
             __A         =	1 if len(UpperCamelCase_				) > 1 else 0
         else:
             __A         =	timestep.cpu().item() if torch.is_tensor(UpperCamelCase_				) else timestep
             __A         =	self._index_counter[timestep_int]

         return indices[pos].item()




     @property
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5




     def 		lowerCAmelCase_     (				self       :			List[Any] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			Union[float, torch.FloatTensor] ,	):


         """simple docstring"""



         __A         =	self.index_for_timestep(UpperCamelCase_				)

         if self.state_in_first_order:
             __A         =	self.sigmas[step_index]
         else:
             __A         =	self.sigmas_interpol[step_index]

         __A         =	sample / ((sigma**2 + 1) ** 0.5)
         return sample




     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Union[str, torch.device] = None ,	UpperCamelCase_       :			Optional[int] = None ,	):


         """simple docstring"""



         __A         =	num_inference_steps

         __A         =	num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             __A         =	np.linspace(0 ,	num_train_timesteps - 1 ,	UpperCamelCase_ ,	dtype=UpperCamelCase_				)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             __A         =	num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             __A         =	(np.arange(0 ,	UpperCamelCase_				) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_				)
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             __A         =	num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             __A         =	(np.arange(UpperCamelCase_ ,	0 ,	-step_ratio				)).round().copy().astype(UpperCamelCase_				)
             timesteps -= 1
         else:
             raise ValueError(
                 F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."				)

         __A         =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
         __A         =	torch.from_numpy(np.log(UpperCamelCase_				)				).to(UpperCamelCase_				)

         __A         =	np.interp(UpperCamelCase_ ,	np.arange(0 ,	len(UpperCamelCase_				)				) ,	UpperCamelCase_				)
         __A         =	np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
         __A         =	torch.from_numpy(UpperCamelCase_				).to(device=UpperCamelCase_				)

         # interpolate sigmas
         __A         =	sigmas.log().lerp(sigmas.roll(1				).log() ,	0.5				).exp()

         __A         =	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
         __A         =	torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)

         if str(UpperCamelCase_				).startswith("""mps"""				):
             # mps does not support float64
             __A         =	torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_ ,	dtype=torch.floataa				)
         else:
             __A         =	torch.from_numpy(UpperCamelCase_				).to(UpperCamelCase_				)

         # interpolate timesteps
         __A         =	self.sigma_to_t(UpperCamelCase_				).to(UpperCamelCase_ ,	dtype=timesteps.dtype				)
         __A         =	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,	dim=-1				).flatten()

         __A         =	torch.cat([timesteps[:1], interleaved_timesteps]				)

         __A         =	None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         __A         =	defaultdict(UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[Any] ,	UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         __A         =	sigma.log()

         # get distribution
         __A         =	log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         __A         =	dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
         __A         =	low_idx + 1

         __A         =	self.log_sigmas[low_idx]
         __A         =	self.log_sigmas[high_idx]

         # interpolate sigmas
         __A         =	(low - log_sigma) / (low - high)
         __A         =	w.clamp(0 ,	1				)

         # transform interpolation to time range
         __A         =	(1 - w) * low_idx + w * high_idx
         __A         =	t.view(sigma.shape				)
         return t




     @property
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         return self.sample is None




     def 		lowerCAmelCase_     (				self       :			List[Any] ,	UpperCamelCase_       :			Union[torch.FloatTensor, np.ndarray] ,	UpperCamelCase_       :			Union[float, torch.FloatTensor] ,	UpperCamelCase_       :			Union[torch.FloatTensor, np.ndarray] ,	UpperCamelCase_       :			bool = True ,	):


         """simple docstring"""



         __A         =	self.index_for_timestep(UpperCamelCase_				)

         # advance index counter by 1
         __A         =	timestep.cpu().item() if torch.is_tensor(UpperCamelCase_				) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
             __A         =	self.sigmas[step_index]
             __A         =	self.sigmas_interpol[step_index + 1]
             __A         =	self.sigmas[step_index + 1]
         else:
             # 2nd order / KDPM2's method
             __A         =	self.sigmas[step_index - 1]
             __A         =	self.sigmas_interpol[step_index]
             __A         =	self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         __A         =	0
         __A         =	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             __A         =	sigma_hat if self.state_in_first_order else sigma_interpol
             __A         =	sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             __A         =	sigma_hat if self.state_in_first_order else sigma_interpol
             __A         =	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             raise NotImplementedError("""prediction_type not implemented yet: sample"""				)
         else:
             raise ValueError(
                 F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"				)

         if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             __A         =	(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             __A         =	sigma_interpol - sigma_hat

             # store for 2nd order step
             __A         =	sample
         else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             __A         =	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             __A         =	sigma_next - sigma_hat

             __A         =	self.sample
             __A         =	None

         __A         =	sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			torch.FloatTensor ,	UpperCamelCase_       :			torch.FloatTensor ,	):


         """simple docstring"""



         __A         =	self.sigmas.to(device=original_samples.device ,	dtype=original_samples.dtype				)
         if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_				):
             # mps does not support float64
             __A         =	self.timesteps.to(original_samples.device ,	dtype=torch.floataa				)
             __A         =	timesteps.to(original_samples.device ,	dtype=torch.floataa				)
         else:
             __A         =	self.timesteps.to(original_samples.device				)
             __A         =	timesteps.to(original_samples.device				)

         __A         =	[self.index_for_timestep(UpperCamelCase_ ,	UpperCamelCase_				) for t in timesteps]

         __A         =	sigmas[step_indices].flatten()
         while len(sigma.shape				) < len(original_samples.shape				):
             __A         =	sigma.unsqueeze(-1				)

         __A         =	original_samples + noise * sigma
         return noisy_samples





     def __len__(				self       :			Any				):


         """simple docstring"""



         return self.config.num_train_timesteps












import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      Dict:





    """simple docstring"""

    __A         =	3_8_4
    __A         =	7
    if "tiny" in model_name:
        __A         =	9_6
        __A         =	(2, 2, 6, 2)
        __A         =	(3, 6, 1_2, 2_4)
    elif "small" in model_name:
        __A         =	9_6
        __A         =	(2, 2, 1_8, 2)
        __A         =	(3, 6, 1_2, 2_4)
    elif "base" in model_name:
        __A         =	1_2_8
        __A         =	(2, 2, 1_8, 2)
        __A         =	(4, 8, 1_6, 3_2)
        __A         =	1_2
        __A         =	5_1_2
    elif "large" in model_name:
        __A         =	1_9_2
        __A         =	(2, 2, 1_8, 2)
        __A         =	(6, 1_2, 2_4, 4_8)
        __A         =	1_2
        __A         =	7_6_8

    # set label information
    __A         =	1_5_0
    __A         =	"""huggingface/label-files"""
    __A         =	"""ade20k-id2label.json"""
    __A         =	json.load(open(hf_hub_download(__lowercase			,				__lowercase			,				repo_type="""dataset"""    )			,				"""r"""    )    )
    __A         =	{int(__lowercase    ): v for k, v in idalabel.items()}
    __A         =	{v: k for k, v in idalabel.items()}

    __A         =	SwinConfig(
        embed_dim=__lowercase			,				depths=__lowercase			,				num_heads=__lowercase			,				window_size=__lowercase			,				out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			,				)
    __A         =	UperNetConfig(
        backbone_config=__lowercase			,				auxiliary_in_channels=__lowercase			,				num_labels=__lowercase			,				idalabel=__lowercase			,				labelaid=__lowercase			,				)

    return config



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      Dict:





    """simple docstring"""

    __A         =	[]

    # fmt: off
    # stem
    rename_keys.append(("""backbone.patch_embed.projection.weight""", """backbone.embeddings.patch_embeddings.projection.weight""")    )
    rename_keys.append(("""backbone.patch_embed.projection.bias""", """backbone.embeddings.patch_embeddings.projection.bias""")    )
    rename_keys.append(("""backbone.patch_embed.norm.weight""", """backbone.embeddings.norm.weight""")    )
    rename_keys.append(("""backbone.patch_embed.norm.bias""", """backbone.embeddings.norm.bias""")    )
    # stages
    for i in range(len(config.backbone_config.depths    )    ):
        for j in range(config.backbone_config.depths[i]    ):
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")    )
            rename_keys.append((f"backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")    )

        if i < 3:
            rename_keys.append((f"backbone.stages.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")    )
            rename_keys.append((f"backbone.stages.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")    )
            rename_keys.append((f"backbone.stages.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")    )
        rename_keys.append((f"backbone.norm{i}.weight", f"backbone.hidden_states_norms.stage{i+1}.weight")    )
        rename_keys.append((f"backbone.norm{i}.bias", f"backbone.hidden_states_norms.stage{i+1}.bias")    )

    # decode head
    rename_keys.extend(
        [
            ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
            ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
            ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
            ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
        ]    )
    # fmt: on

    return rename_keys



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]			,				__lowercase   :			int			,				__lowercase   :			Optional[int]    )						->      Any:





    """simple docstring"""

    __A         =	dct.pop(__lowercase    )
    __A         =	val



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			int    )						->      Any:





    """simple docstring"""

    __A         =	[int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
    for i in range(len(backbone_config.depths    )    ):
        __A         =	num_features[i]
        for j in range(backbone_config.depths[i]    ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            __A         =	state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"    )
            __A         =	state_dict.pop(f"backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"    )
            # next, add query, keys and values (in that order) to the state dict
            __A         =	in_proj_weight[:dim, :]
            __A         =	in_proj_bias[: dim]
            __A         =	in_proj_weight[
                dim : dim * 2, :
            ]
            __A         =	in_proj_bias[
                dim : dim * 2
            ]
            __A         =	in_proj_weight[
                -dim :, :
            ]
            __A         =	in_proj_bias[-dim :]
            # fmt: on



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any    )						->      List[str]:





    """simple docstring"""

    __A    ,	__A         =	x.shape
    __A         =	x.reshape(__lowercase			,				4			,				in_channel // 4    )
    __A         =	x[:, [0, 2, 1, 3], :].transpose(1			,				2    ).reshape(__lowercase			,				__lowercase    )
    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[Any]    )						->      Dict:





    """simple docstring"""

    __A    ,	__A         =	x.shape
    __A         =	x.reshape(__lowercase			,				in_channel // 4			,				4    )
    __A         =	x[:, :, [0, 2, 1, 3]].transpose(1			,				2    ).reshape(__lowercase			,				__lowercase    )

    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple    )						->      Tuple:





    """simple docstring"""

    __A         =	x.shape[0]
    __A         =	x.reshape(4			,				in_channel // 4    )
    __A         =	x[[0, 2, 1, 3], :].transpose(0			,				1    ).reshape(__lowercase    )
    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	x.shape[0]
    __A         =	x.reshape(in_channel // 4			,				4    )
    __A         =	x[:, [0, 2, 1, 3]].transpose(0			,				1    ).reshape(__lowercase    )
    return x



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			List[Any]			,				__lowercase   :			str    )						->      Tuple:





    """simple docstring"""

    __A         =	{
        """upernet-swin-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth""",
        """upernet-swin-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth""",
        """upernet-swin-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth""",
        """upernet-swin-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth""",
    }
    __A         =	model_name_to_url[model_name]
    __A         =	torch.hub.load_state_dict_from_url(__lowercase			,				map_location="""cpu"""			,				file_name=__lowercase    )[
        """state_dict"""
    ]

    for name, param in state_dict.items():
        print(__lowercase			,				param.shape    )

    __A         =	get_upernet_config(__lowercase    )
    __A         =	UperNetForSemanticSegmentation(__lowercase    )
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        __A         =	state_dict.pop(__lowercase    )
        if "bn" in key:
            __A         =	key.replace("""bn"""			,				"""batch_norm"""    )
        __A         =	val

    # rename keys
    __A         =	create_rename_keys(__lowercase    )
    for src, dest in rename_keys:
        rename_key(__lowercase			,				__lowercase			,				__lowercase    )
    read_in_q_k_v(__lowercase			,				config.backbone_config    )

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                __A         =	reverse_correct_unfold_reduction_order(__lowercase    )
            if "norm" in key:
                __A         =	reverse_correct_unfold_norm_order(__lowercase    )

    model.load_state_dict(__lowercase    )

    # verify on image
    __A         =	"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
    __A         =	Image.open(requests.get(__lowercase			,				stream=__lowercase    ).raw    ).convert("""RGB"""    )

    __A         =	SegformerImageProcessor()
    __A         =	processor(__lowercase			,				return_tensors="""pt"""    ).pixel_values

    with torch.no_grad():
        __A         =	model(__lowercase    )
        __A         =	outputs.logits

    print(logits.shape    )
    print("""First values of logits:"""			,				logits[0, 0, :3, :3]    )
    # assert values
    if model_name == "upernet-swin-tiny":
        __A         =	torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]    )
    elif model_name == "upernet-swin-small":
        __A         =	torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]    )
    elif model_name == "upernet-swin-base":
        __A         =	torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]    )
    elif model_name == "upernet-swin-large":
        __A         =	torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]    )
    print("""Logits:"""			,				outputs.logits[0, 0, :3, :3]    )
    assert torch.allclose(outputs.logits[0, 0, :3, :3]			,				__lowercase			,				atol=1E-4    )
    print("""Looks ok!"""    )

    if pytorch_dump_folder_path is not None:
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}"    )
        model.save_pretrained(__lowercase    )
        print(f"Saving processor to {pytorch_dump_folder_path}"    )
        processor.save_pretrained(__lowercase    )

    if push_to_hub:
        print(f"Pushing model and processor for {model_name} to hub"    )
        model.push_to_hub(f"openmmlab/{model_name}"    )
        processor.push_to_hub(f"openmmlab/{model_name}"    )


if __name__ == "__main__":
  __a      :       Union[str, Any]				  =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="upernet-swin-tiny",
      type=str,
      choices=[f"""upernet-swin-{size}""" for size in ["tiny", "small", "base", "large"]],
      help="Name of the Swin + UperNet model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  __a      :       List[Any]				  =   parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




