


'''simple docstring'''






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			(lowerCAmelCase__					:Tuple						)				->					Tuple:




     '''simple docstring'''



     _a      =							SwinaSRConfig()

     if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
          _a      =							4
     elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
          _a      =							4
          _a      =							48
          _a      =							'pixelshuffle_aux'
     elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
          _a      =							[6, 6, 6, 6]
          _a      =							60
          _a      =							[6, 6, 6, 6]
          _a      =							'pixelshuffledirect'
     elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
          _a      =							4
          _a      =							'nearest+conv'
     elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
          _a      =							1
          _a      =							1
          _a      =							1_26
          _a      =							7
          _a      =							2_5_5.0
          _a      =							''

     return config






def 		_A			(lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:Dict						)				->					Optional[Any]:




     '''simple docstring'''



     if "patch_embed.proj" in name and "layers" not in name:
          _a      =							name.replace('patch_embed.proj'			,  'embeddings.patch_embeddings.projection'						)
     if "patch_embed.norm" in name:
          _a      =							name.replace('patch_embed.norm'			,  'embeddings.patch_embeddings.layernorm'						)
     if "layers" in name:
          _a      =							name.replace('layers'			,  'encoder.stages'						)
     if "residual_group.blocks" in name:
          _a      =							name.replace('residual_group.blocks'			,  'layers'						)
     if "attn.proj" in name:
          _a      =							name.replace('attn.proj'			,  'attention.output.dense'						)
     if "attn" in name:
          _a      =							name.replace('attn'			,  'attention.self'						)
     if "norm1" in name:
          _a      =							name.replace('norm1'			,  'layernorm_before'						)
     if "norm2" in name:
          _a      =							name.replace('norm2'			,  'layernorm_after'						)
     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 "q_bias" in name:
          _a      =							name.replace('q_bias'			,  'query.bias'						)
     if "k_bias" in name:
          _a      =							name.replace('k_bias'			,  'key.bias'						)
     if "v_bias" in name:
          _a      =							name.replace('v_bias'			,  'value.bias'						)
     if "cpb_mlp" in name:
          _a      =							name.replace('cpb_mlp'			,  'continuous_position_bias_mlp'						)
     if "patch_embed.proj" in name:
          _a      =							name.replace('patch_embed.proj'			,  'patch_embed.projection'						)

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

     if "conv_first" in name:
          _a      =							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:
               _a      =							name.replace('conv_last'			,  'final_convolution'						)
          if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
               if "conv_before_upsample.0" in name:
                    _a      =							name.replace('conv_before_upsample.0'			,  'conv_before_upsample'						)
               if "upsample.0" in name:
                    _a      =							name.replace('upsample.0'			,  'upsample.convolution_0'						)
               if "upsample.2" in name:
                    _a      =							name.replace('upsample.2'			,  'upsample.convolution_1'						)
               _a      =							'upsample.' + name
          elif config.upsampler == "pixelshuffledirect":
               _a      =							name.replace('upsample.0.weight'			,  'upsample.conv.weight'						)
               _a      =							name.replace('upsample.0.bias'			,  'upsample.conv.bias'						)
          else:
               pass
     else:
          _a      =							'swin2sr.' + name

     return name






def 		_A			(lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:List[Any]						)				->					Union[str, Any]:




     '''simple docstring'''



     for key in orig_state_dict.copy().keys():
          _a      =							orig_state_dict.pop(UpperCamelCase__						)

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

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

     return orig_state_dict






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:Union[str, Any]						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							get_config(UpperCamelCase__						)
     _a      =							SwinaSRForImageSuperResolution(UpperCamelCase__						)
     model.eval()

     _a      =							torch.hub.load_state_dict_from_url(UpperCamelCase__			,  map_location='cpu'						)
     _a      =							convert_state_dict(UpperCamelCase__			,  UpperCamelCase__						)
     _a ,				_a      =							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
     _a      =							'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
     _a      =							Image.open(requests.get(UpperCamelCase__			,  stream=UpperCamelCase__						).raw						).convert('RGB'						)
     _a      =							SwinaSRImageProcessor()
     # pixel_values = processor(image, return_tensors="pt").pixel_values

     _a      =							1_26 if 'Jpeg' in checkpoint_url else 2_56
     _a      =							Compose(
         [
             Resize((image_size, image_size)						),
             ToTensor(),
             Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]			,  std=[0.2_2_9, 0.2_2_4, 0.2_2_5]						),
         ]						)
     _a      =							transforms(UpperCamelCase__						).unsqueeze(0						)

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

     _a      =							model(UpperCamelCase__						)

     # assert values
     if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
          _a      =							torch.Size([1, 3, 5_12, 5_12]						)
          _a      =							torch.tensor(
              [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]						)
     elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
          _a      =							torch.Size([1, 3, 10_24, 10_24]						)
          _a      =							torch.tensor(
              [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]						)
     elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
          # TODO values didn't match exactly here
          _a      =							torch.Size([1, 3, 10_24, 10_24]						)
          _a      =							torch.tensor(
              [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]						)
     elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
          _a      =							torch.Size([1, 3, 5_12, 5_12]						)
          _a      =							torch.tensor(
              [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]						)
     elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
          _a      =							torch.Size([1, 3, 10_24, 10_24]						)
          _a      =							torch.tensor(
              [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]						)

     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!'						)

     _a      =							{
         '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'
         ),
     }
     _a      =							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_   :      Optional[Any]				    =      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_   :      str				    =      parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

 a_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

