

"""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  _lowercase					(    __lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	4
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	48
            SCREAMING_SNAKE_CASE__   :      str      		=	"""pixelshuffle_aux"""
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	[6, 6, 6, 6]
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	60
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[6, 6, 6, 6]
            SCREAMING_SNAKE_CASE__   :      int      		=	"""pixelshuffledirect"""
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	4
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""nearest+conv"""
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Any      		=	1
            SCREAMING_SNAKE_CASE__   :      str      		=	1
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	126
            SCREAMING_SNAKE_CASE__   :      Dict      		=	7
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	255.0
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	""""""

      return config




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

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE__   :      str      		=	"""layernorm.bias"""

      if "conv_first" in name:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	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:
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	name.replace("""conv_last"""  ,					"""final_convolution"""							)
            if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
                  if "conv_before_upsample.0" in name:
                        SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""conv_before_upsample.0"""  ,					"""conv_before_upsample"""							)
                  if "upsample.0" in name:
                        SCREAMING_SNAKE_CASE__   :      Tuple      		=	name.replace("""upsample.0"""  ,					"""upsample.convolution_0"""							)
                  if "upsample.2" in name:
                        SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	name.replace("""upsample.2"""  ,					"""upsample.convolution_1"""							)
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	"""upsample.""" + name
            elif config.upsampler == "pixelshuffledirect":
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace("""upsample.0.weight"""  ,					"""upsample.conv.weight"""							)
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""upsample.0.bias"""  ,					"""upsample.conv.bias"""							)
            else:
                  pass
      else:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""swin2sr.""" + name

      return name




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	orig_state_dict.pop(__lowerCAmelCase							)

            if "qkv" in key:
                  SCREAMING_SNAKE_CASE__   :      str      		=	key.split("""."""							)
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	int(key_split[1]							)
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	int(key_split[4]							)
                  SCREAMING_SNAKE_CASE__   :      Any      		=	config.embed_dim

                  if "weight" in key:
                        SCREAMING_SNAKE_CASE__   :      Any      		=	val[:dim, :]
                        SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	val[dim : dim * 2, :]
                        SCREAMING_SNAKE_CASE__   :      List[str]      		=	val[-dim:, :]
                  else:
                        SCREAMING_SNAKE_CASE__   :      List[Any]      		=	val[:dim]
                        SCREAMING_SNAKE_CASE__   :      Any      		=	val[dim : dim * 2]
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	val[-dim:]
                  pass
            else:
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	val

      return orig_state_dict




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[Any]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	get_config(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	SwinaSRForImageSuperResolution(__lowerCAmelCase							)
      model.eval()

      SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.hub.load_state_dict_from_url(__lowerCAmelCase  ,					map_location="""cpu"""							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	convert_state_dict(__lowerCAmelCase  ,					__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model.load_state_dict(__lowerCAmelCase  ,					strict=__lowerCAmelCase							)

      if len(__lowerCAmelCase							) > 0:
            raise ValueError("""Missing keys when converting: {}""".format(__lowerCAmelCase							)							)
      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
      SCREAMING_SNAKE_CASE__   :      Any      		=	"""https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	Image.open(requests.get(__lowerCAmelCase  ,					stream=__lowerCAmelCase							).raw							).convert("""RGB"""							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	SwinaSRImageProcessor()
      # pixel_values = processor(image, return_tensors="pt").pixel_values

      SCREAMING_SNAKE_CASE__   :      str      		=	126 if """Jpeg""" in checkpoint_url else 256
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	Compose(
          [
              Resize((image_size, image_size)							),
              ToTensor(),
              Normalize(mean=[0.485, 0.456, 0.406]  ,					std=[0.229, 0.224, 0.225]							),
          ]							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	transforms(__lowerCAmelCase							).unsqueeze(0							)

      if config.num_channels == 1:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	pixel_values[:, 0, :, :].unsqueeze(1							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model(__lowerCAmelCase							)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Any      		=	torch.Size([1, 3, 512, 512]							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	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:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.Size([1, 3, 1024, 1024]							)
            SCREAMING_SNAKE_CASE__   :      Any      		=	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
            SCREAMING_SNAKE_CASE__   :      str      		=	torch.Size([1, 3, 1024, 1024]							)
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	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:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.Size([1, 3, 512, 512]							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	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:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.Size([1, 3, 1024, 1024]							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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]  ,					__lowerCAmelCase  ,					atol=1E-3							)
      print("""Looks ok!"""							)

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{
          """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"""
          ),
      }
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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(__lowerCAmelCase							)
            print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
            processor.save_pretrained(__lowerCAmelCase							)

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


if __name__ == "__main__":
  a					:int    = 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					:List[Any]    = parser.parse_args()
  convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a					:Optional[Any]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """t5"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:Any       = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(						self	,    _a=32_128	,    _a=512	,    _a=64	,    _a=2_048	,    _a=6	,    _a=None	,    _a=8	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="relu"	,    _a=True	,    _a=True	,    _a=0	,    _a=1	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	d_model
           SCREAMING_SNAKE_CASE__   :      int      		=	d_kv
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d_ff
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	relative_attention_num_buckets
           SCREAMING_SNAKE_CASE__   :      str      		=	relative_attention_max_distance
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dropout_rate
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	initializer_factor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	feed_forward_proj
           SCREAMING_SNAKE_CASE__   :      str      		=	use_cache

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.feed_forward_proj.split("""-"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_info[-1]
           SCREAMING_SNAKE_CASE__   :      str      		=	act_info[0] == """gated"""

           if len(_a  ) > 1 and act_info[0] != "gated" or len(_a  ) > 2:
                 raise ValueError(
                     f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                     """'gated-gelu' or 'relu'"""  )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""gelu_new"""

           super().__init__(
               pad_token_id=_a	,    eos_token_id=_a	,    is_encoder_decoder=_a	,    **_a	,    )





class      __a							(UpperCamelCase_):






     '''simple docstring'''

     @property
     def 					_a			(						self  )  ->      Mapping[str, Mapping[int, str]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""past_encoder_sequence + sequence"""
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch"""}
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      str      		=	{0: """batch""", 1: """decoder_sequence"""}
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                 self.fill_with_past_key_values_(_a	,    direction="""inputs"""  )

           return common_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13




