






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   __lowercase       (			snake_case,	snake_case=0.999,	snake_case="cosine",	):







				"""simple docstring"""






				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(snake_case			):
												return math.exp(t * -12.0			)

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

				__magic_name__		:Any   =   []
				for i in range(_UpperCamelCase			):
								__magic_name__		:Union[str, Any]   =   i / num_diffusion_timesteps
								__magic_name__		:Tuple   =   (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(_UpperCamelCase			) / alpha_bar_fn(_UpperCamelCase			),	_UpperCamelCase			)			)
				return torch.tensor(_UpperCamelCase,	dtype=torch.floataa			)

class     lowerCamelCase_					(   lowerCAmelCase__      ,					lowerCAmelCase__			):
				a__       							=	[e.name for e in KarrasDiffusionSchedulers]
				a__       							=	2







				@register_to_config
				def __init__(    self							,       __lowerCAmelCase = 1_0_0_0							,       __lowerCAmelCase = 0.00085							,       __lowerCAmelCase = 0.012							,       __lowerCAmelCase = "linear"							,       __lowerCAmelCase = None							,       __lowerCAmelCase = "epsilon"							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = 1.0							,       __lowerCAmelCase = "linspace"							,       __lowerCAmelCase = 0							,       ):
								"""simple docstring"""
								if trained_betas is not None:
												__magic_name__		:int   =   torch.tensor(__UpperCAmelCase							,       dtype=torch.floataa  )
								elif beta_schedule == "linear":
												__magic_name__		:Optional[Any]   =   torch.linspace(__UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       dtype=torch.floataa  )
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												__magic_name__		:Tuple   =   (
												    torch.linspace(beta_start**0.5							,       beta_end**0.5							,       __UpperCAmelCase							,       dtype=torch.floataa  ) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												__magic_name__		:Union[str, Any]   =   betas_for_alpha_bar(__UpperCAmelCase							,       alpha_transform_type='''cosine'''  )
								elif beta_schedule == "exp":
												__magic_name__		:str   =   betas_for_alpha_bar(__UpperCAmelCase							,       alpha_transform_type='''exp'''  )
								else:
												raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

								__magic_name__		:Any   =   1.0 - self.betas
								__magic_name__		:Optional[int]   =   torch.cumprod(self.alphas							,       dim=0  )

								#  set all values
								self.set_timesteps(__UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase  )
								__magic_name__		:Dict   =   use_karras_sigmas







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								if schedule_timesteps is None:
												__magic_name__		:str   =   self.timesteps

								__magic_name__		:int   =   (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:
												__magic_name__		:List[Any]   =   1 if len(__UpperCAmelCase  ) > 1 else 0
								else:
												__magic_name__		:Optional[int]   =   timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase  ) else timestep
												__magic_name__		:Optional[int]   =   self._index_counter[timestep_int]

								return indices[pos].item()







				@property
				def       A						(    self  ):
								"""simple docstring"""
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

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







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.index_for_timestep(__UpperCAmelCase  )

								__magic_name__		:Union[str, Any]   =   self.sigmas[step_index]
								__magic_name__		:int   =   sample / ((sigma**2 + 1) ** 0.5)
								return sample







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase = None							,       ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   num_inference_steps

								__magic_name__		:Any   =   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":
												__magic_name__		:Optional[Any]   =   np.linspace(0							,       num_train_timesteps - 1							,       __UpperCAmelCase							,       dtype=__UpperCAmelCase  )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
												__magic_name__		:Dict   =   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
												__magic_name__		:Optional[int]   =   (np.arange(0							,       __UpperCAmelCase  ) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase  )
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												__magic_name__		:List[Any]   =   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
												__magic_name__		:Optional[int]   =   (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\'.'''  )

								__magic_name__		:str   =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
								__magic_name__		:Optional[int]   =   np.log(__UpperCAmelCase  )
								__magic_name__		:List[str]   =   np.interp(__UpperCAmelCase							,       np.arange(0							,       len(__UpperCAmelCase  )  )							,       __UpperCAmelCase  )

								if self.config.use_karras_sigmas:
												__magic_name__		:Dict   =   self._convert_to_karras(in_sigmas=__UpperCAmelCase							,       num_inference_steps=self.num_inference_steps  )
												__magic_name__		:Any   =   np.array([self._sigma_to_t(__UpperCAmelCase							,       __UpperCAmelCase  ) for sigma in sigmas]  )

								__magic_name__		:Any   =   np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
								__magic_name__		:Union[str, Any]   =   torch.from_numpy(__UpperCAmelCase  ).to(device=__UpperCAmelCase  )
								__magic_name__		:List[str]   =   torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2  ), sigmas[-1:]]  )

								__magic_name__		:Any   =   torch.from_numpy(__UpperCAmelCase  )
								__magic_name__		:List[Any]   =   torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2  )]  )

								if str(__UpperCAmelCase  ).startswith('''mps'''  ):
												# mps does not support float64
												__magic_name__		:Tuple   =   timesteps.to(__UpperCAmelCase							,       dtype=torch.floataa  )
								else:
												__magic_name__		:str   =   timesteps.to(device=__UpperCAmelCase  )

								# empty dt and derivative
								__magic_name__		:Optional[Any]   =   None
								__magic_name__		:str   =   None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								__magic_name__		:Tuple   =   defaultdict(__UpperCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   np.log(__UpperCAmelCase  )

								# get distribution
								__magic_name__		:str   =   log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								__magic_name__		:Dict   =   np.cumsum((dists >= 0)							,       axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
								__magic_name__		:int   =   low_idx + 1

								__magic_name__		:Any   =   log_sigmas[low_idx]
								__magic_name__		:Union[str, Any]   =   log_sigmas[high_idx]

								# interpolate sigmas
								__magic_name__		:List[Any]   =   (low - log_sigma) / (low - high)
								__magic_name__		:int   =   np.clip(__UpperCAmelCase							,       0							,       1  )

								# transform interpolation to time range
								__magic_name__		:List[str]   =   (1 - w) * low_idx + w * high_idx
								__magic_name__		:int   =   t.reshape(sigma.shape  )
								return t







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Any   =   in_sigmas[-1].item()
								__magic_name__		:Optional[int]   =   in_sigmas[0].item()

								__magic_name__		:List[Any]   =   7.0  # 7.0 is the value used in the paper
								__magic_name__		:Optional[int]   =   np.linspace(0							,       1							,       __UpperCAmelCase  )
								__magic_name__		:Optional[int]   =   sigma_min ** (1 / rho)
								__magic_name__		:Union[str, Any]   =   sigma_max ** (1 / rho)
								__magic_name__		:int   =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self.dt is None







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = True							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   self.index_for_timestep(__UpperCAmelCase  )

								# advance index counter by 1
								__magic_name__		:Tuple   =   timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase  ) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
												__magic_name__		:List[str]   =   self.sigmas[step_index]
												__magic_name__		:Union[str, Any]   =   self.sigmas[step_index + 1]
								else:
												# 2nd order / Heun's method
												__magic_name__		:Optional[int]   =   self.sigmas[step_index - 1]
												__magic_name__		:Union[str, Any]   =   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
								__magic_name__		:Union[str, Any]   =   0
								__magic_name__		:List[str]   =   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":
												__magic_name__		:Any   =   sigma_hat if self.state_in_first_order else sigma_next
												__magic_name__		:Optional[Any]   =   sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
												__magic_name__		:Tuple   =   sigma_hat if self.state_in_first_order else sigma_next
												__magic_name__		:int   =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
												    sample / (sigma_input**2 + 1)
												)
								elif self.config.prediction_type == "sample":
												__magic_name__		:List[str]   =   model_output
								else:
												raise ValueError(
												    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

								if self.config.clip_sample:
												__magic_name__		:Optional[Any]   =   pred_original_sample.clamp(
												    -self.config.clip_sample_range							,       self.config.clip_sample_range  )

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

												# store for 2nd order step
												__magic_name__		:Optional[int]   =   derivative
												__magic_name__		:str   =   dt
												__magic_name__		:str   =   sample
								else:
												# 2. 2nd order / Heun's method
												__magic_name__		:Any   =   (sample - pred_original_sample) / sigma_next
												__magic_name__		:Optional[int]   =   (self.prev_derivative + derivative) / 2

												# 3. take prev timestep & sample
												__magic_name__		:Tuple   =   self.dt
												__magic_name__		:str   =   self.sample

												# free dt and derivative
												# Note, this puts the scheduler in "first order mode"
												__magic_name__		:str   =   None
												__magic_name__		:Dict   =   None
												__magic_name__		:Optional[Any]   =   None

								__magic_name__		:Tuple   =   sample + derivative * dt

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=__UpperCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   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
												__magic_name__		:Optional[Any]   =   self.timesteps.to(original_samples.device							,       dtype=torch.floataa  )
												__magic_name__		:int   =   timesteps.to(original_samples.device							,       dtype=torch.floataa  )
								else:
												__magic_name__		:Any   =   self.timesteps.to(original_samples.device  )
												__magic_name__		:Union[str, Any]   =   timesteps.to(original_samples.device  )

								__magic_name__		:List[Any]   =   [self.index_for_timestep(__UpperCAmelCase							,       __UpperCAmelCase  ) for t in timesteps]

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

								__magic_name__		:List[Any]   =   original_samples + noise * sigma
								return noisy_samples







				def __len__(    self  ):
								"""simple docstring"""
								return self.config.num_train_timesteps











import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def 		a__      (		_UpperCamelCase      :			List[str]				):
   __lowerCamelCase    				=     SwinaSRConfig()

   if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      __lowerCamelCase    				=     4
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      __lowerCamelCase    				=     4
      __lowerCamelCase    				=     48
      __lowerCamelCase    				=     '''pixelshuffle_aux'''
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      __lowerCamelCase    				=     [6, 6, 6, 6]
      __lowerCamelCase    				=     60
      __lowerCamelCase    				=     [6, 6, 6, 6]
      __lowerCamelCase    				=     '''pixelshuffledirect'''
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      __lowerCamelCase    				=     4
      __lowerCamelCase    				=     '''nearest+conv'''
   elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
      __lowerCamelCase    				=     1
      __lowerCamelCase    				=     1
      __lowerCamelCase    				=     1_26
      __lowerCamelCase    				=     7
      __lowerCamelCase    				=     255.0
      __lowerCamelCase    				=     ''''''

   return config





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			List[str]				):
   if "patch_embed.proj" in name and "layers" not in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.proj'''			,'''embeddings.patch_embeddings.projection'''				)
   if "patch_embed.norm" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.norm'''			,'''embeddings.patch_embeddings.layernorm'''				)
   if "layers" in name:
      __lowerCamelCase    				=     name.replace('''layers'''			,'''encoder.stages'''				)
   if "residual_group.blocks" in name:
      __lowerCamelCase    				=     name.replace('''residual_group.blocks'''			,'''layers'''				)
   if "attn.proj" in name:
      __lowerCamelCase    				=     name.replace('''attn.proj'''			,'''attention.output.dense'''				)
   if "attn" in name:
      __lowerCamelCase    				=     name.replace('''attn'''			,'''attention.self'''				)
   if "norm1" in name:
      __lowerCamelCase    				=     name.replace('''norm1'''			,'''layernorm_before'''				)
   if "norm2" in name:
      __lowerCamelCase    				=     name.replace('''norm2'''			,'''layernorm_after'''				)
   if "mlp.fc1" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc1'''			,'''intermediate.dense'''				)
   if "mlp.fc2" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc2'''			,'''output.dense'''				)
   if "q_bias" in name:
      __lowerCamelCase    				=     name.replace('''q_bias'''			,'''query.bias'''				)
   if "k_bias" in name:
      __lowerCamelCase    				=     name.replace('''k_bias'''			,'''key.bias'''				)
   if "v_bias" in name:
      __lowerCamelCase    				=     name.replace('''v_bias'''			,'''value.bias'''				)
   if "cpb_mlp" in name:
      __lowerCamelCase    				=     name.replace('''cpb_mlp'''			,'''continuous_position_bias_mlp'''				)
   if "patch_embed.proj" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.proj'''			,'''patch_embed.projection'''				)

   if name == "norm.weight":
      __lowerCamelCase    				=     '''layernorm.weight'''
   if name == "norm.bias":
      __lowerCamelCase    				=     '''layernorm.bias'''

   if "conv_first" in name:
      __lowerCamelCase    				=     name.replace('''conv_first'''			,'''first_convolution'''				)

   if (
       "upsample" in name
       or "conv_before_upsample" in name
       or "conv_bicubic" in name
       or "conv_up" in name
       or "conv_hr" in name
       or "conv_last" in name
       or "aux" in name
   ):
      # heads
      if "conv_last" in name:
         __lowerCamelCase    				=     name.replace('''conv_last'''			,'''final_convolution'''				)
      if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
         if "conv_before_upsample.0" in name:
            __lowerCamelCase    				=     name.replace('''conv_before_upsample.0'''			,'''conv_before_upsample'''				)
         if "upsample.0" in name:
            __lowerCamelCase    				=     name.replace('''upsample.0'''			,'''upsample.convolution_0'''				)
         if "upsample.2" in name:
            __lowerCamelCase    				=     name.replace('''upsample.2'''			,'''upsample.convolution_1'''				)
         __lowerCamelCase    				=     '''upsample.''' + name
      elif config.upsampler == "pixelshuffledirect":
         __lowerCamelCase    				=     name.replace('''upsample.0.weight'''			,'''upsample.conv.weight'''				)
         __lowerCamelCase    				=     name.replace('''upsample.0.bias'''			,'''upsample.conv.bias'''				)
      else:
         pass
   else:
      __lowerCamelCase    				=     '''swin2sr.''' + name

   return name





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Union[str, Any]				):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase    				=     orig_state_dict.pop(_UpperCamelCase				)

      if "qkv" in key:
         __lowerCamelCase    				=     key.split('''.'''				)
         __lowerCamelCase    				=     int(key_split[1]				)
         __lowerCamelCase    				=     int(key_split[4]				)
         __lowerCamelCase    				=     config.embed_dim

         if "weight" in key:
            __lowerCamelCase    				=     val[:dim, :]
            __lowerCamelCase    				=     val[dim : dim * 2, :]
            __lowerCamelCase    				=     val[-dim:, :]
         else:
            __lowerCamelCase    				=     val[:dim]
            __lowerCamelCase    				=     val[dim : dim * 2]
            __lowerCamelCase    				=     val[-dim:]
         pass
      else:
         __lowerCamelCase    				=     val

   return orig_state_dict





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			int			,_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     get_config(_UpperCamelCase				)
   __lowerCamelCase    				=     SwinaSRForImageSuperResolution(_UpperCamelCase				)
   model.eval()

   __lowerCamelCase    				=     torch.hub.load_state_dict_from_url(_UpperCamelCase			,map_location='''cpu'''				)
   __lowerCamelCase    				=     convert_state_dict(_UpperCamelCase			,_UpperCamelCase				)
   __lowerCamelCase				,__lowerCamelCase    				=     model.load_state_dict(_UpperCamelCase			,strict=_UpperCamelCase				)

   if len(_UpperCamelCase				) > 0:
      raise ValueError('''Missing keys when converting: {}'''.format(_UpperCamelCase				)				)
   for key in unexpected_keys:
      if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
         raise ValueError(F"""Unexpected key {key} in state_dict"""				)

    # verify values
   __lowerCamelCase    				=     '''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				).convert('''RGB'''				)
   __lowerCamelCase    				=     SwinaSRImageProcessor()
   # pixel_values = processor(image, return_tensors="pt").pixel_values

   __lowerCamelCase    				=     1_26 if '''Jpeg''' in checkpoint_url else 2_56
   __lowerCamelCase    				=     Compose(
       [
           Resize((image_size, image_size)				),
           ToTensor(),
           Normalize(mean=[0.485, 0.456, 0.406]			,std=[0.229, 0.224, 0.225]				),
       ]				)
   __lowerCamelCase    				=     transforms(_UpperCamelCase				).unsqueeze(0				)

   if config.num_channels == 1:
      __lowerCamelCase    				=     pixel_values[:, 0, :, :].unsqueeze(1				)

   __lowerCamelCase    				=     model(_UpperCamelCase				)

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      __lowerCamelCase    				=     torch.Size([1, 3, 5_12, 5_12]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]				)
   elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      __lowerCamelCase    				=     torch.Size([1, 3, 10_24, 10_24]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]				)
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      # TODO values didn't match exactly here
      __lowerCamelCase    				=     torch.Size([1, 3, 10_24, 10_24]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]				)
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      __lowerCamelCase    				=     torch.Size([1, 3, 5_12, 5_12]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]				)
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      __lowerCamelCase    				=     torch.Size([1, 3, 10_24, 10_24]				)
      __lowerCamelCase    				=     torch.tensor(
          [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]				)

   assert (
       outputs.reconstruction.shape == expected_shape
   ), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
   assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]			,_UpperCamelCase			,atol=1e-3				)
   print('''Looks ok!'''				)

   __lowerCamelCase    				=     {
       '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''': (
           '''swin2SR-classical-sr-x2-64'''
       ),
       '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth''': (
           '''swin2SR-classical-sr-x4-64'''
       ),
       '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth''': (
           '''swin2SR-compressed-sr-x4-48'''
       ),
       '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth''': (
           '''swin2SR-lightweight-x2-64'''
       ),
       '''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth''': (
           '''swin2SR-realworld-sr-x4-64-bsrgan-psnr'''
       ),
   }
   __lowerCamelCase    				=     url_to_name[checkpoint_url]

   if pytorch_dump_folder_path is not None:
      print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
      model.save_pretrained(_UpperCamelCase				)
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
      processor.save_pretrained(_UpperCamelCase				)

   if push_to_hub:
      model.push_to_hub(F"""caidas/{model_name}"""				)
      processor.push_to_hub(F"""caidas/{model_name}"""				)


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
      type=str,
      help="""URL of the original Swin2SR checkpoint 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 to push the converted model to the hub.""")

  a_								=       parser.parse_args()
  convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






