

'''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 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     Dict						 =			SwinaSRConfig()

 if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
  UpperCAmelCase	:     int						 =			4
 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
  UpperCAmelCase	:     Optional[Any]						 =			4
  UpperCAmelCase	:     int						 =			48
  UpperCAmelCase	:     Optional[int]						 =			'pixelshuffle_aux'
 elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
  UpperCAmelCase	:     str						 =			[6, 6, 6, 6]
  UpperCAmelCase	:     Optional[Any]						 =			60
  UpperCAmelCase	:     str						 =			[6, 6, 6, 6]
  UpperCAmelCase	:     Union[str, Any]						 =			'pixelshuffledirect'
 elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
  UpperCAmelCase	:     Dict						 =			4
  UpperCAmelCase	:     Optional[Any]						 =			'nearest+conv'
 elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
  UpperCAmelCase	:     List[str]						 =			1
  UpperCAmelCase	:     Optional[int]						 =			1
  UpperCAmelCase	:     Union[str, Any]						 =			1_26
  UpperCAmelCase	:     Tuple						 =			7
  UpperCAmelCase	:     Union[str, Any]						 =			2_55.0
  UpperCAmelCase	:     List[Any]						 =			''

 return config




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):






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

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

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

 return name




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):






 for key in orig_state_dict.copy().keys():
  UpperCAmelCase	:     Union[str, Any]						 =			orig_state_dict.pop(_a     )

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

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

 return orig_state_dict




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			get_config(_a     )
 UpperCAmelCase	:     Dict						 =			SwinaSRForImageSuperResolution(_a     )
 model.eval()

 UpperCAmelCase	:     List[str]						 =			torch.hub.load_state_dict_from_url(_a  ,	map_location='cpu'     )
 UpperCAmelCase	:     Tuple						 =			convert_state_dict(_a  ,	_a     )
 UpperCAmelCase ,			UpperCAmelCase	:     List[str]						 =			model.load_state_dict(_a  ,	strict=_a     )

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

 UpperCAmelCase	:     Union[str, Any]						 =			1_26 if 'Jpeg' in checkpoint_url else 2_56
 UpperCAmelCase	:     Optional[int]						 =			Compose(
     [
         Resize((image_size, image_size)     ),
         ToTensor(),
         Normalize(mean=[0.485, 0.456, 0.406]  ,	std=[0.229, 0.224, 0.225]     ),
     ]     )
 UpperCAmelCase	:     Dict						 =			transforms(_a     ).unsqueeze(0     )

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

 UpperCAmelCase	:     Union[str, Any]						 =			model(_a     )

 # assert values
 if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
  UpperCAmelCase	:     Dict						 =			torch.Size([1, 3, 5_12, 5_12]     )
  UpperCAmelCase	:     Dict						 =			torch.tensor(
      [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]     )
 elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
  UpperCAmelCase	:     List[Any]						 =			torch.Size([1, 3, 10_24, 10_24]     )
  UpperCAmelCase	:     Dict						 =			torch.tensor(
      [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]     )
 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
  # TODO values didn't match exactly here
  UpperCAmelCase	:     int						 =			torch.Size([1, 3, 10_24, 10_24]     )
  UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor(
      [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]     )
 elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
  UpperCAmelCase	:     List[str]						 =			torch.Size([1, 3, 5_12, 5_12]     )
  UpperCAmelCase	:     str						 =			torch.tensor(
      [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]     )
 elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
  UpperCAmelCase	:     int						 =			torch.Size([1, 3, 10_24, 10_24]     )
  UpperCAmelCase	:     Dict						 =			torch.tensor(
      [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]     )

 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]  ,	_a  ,	atol=1E-3     )
 print('Looks ok!'     )

 UpperCAmelCase	:     str						 =			{
     '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'
     ),
 }
 UpperCAmelCase	:     List[str]						 =			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(_a     )
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
  processor.save_pretrained(_a     )

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


if __name__ == "__main__":
 lowercase__         =      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.")

 lowercase__         =      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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase__         =      {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMForMultipleChoice",
     "XLMForQuestionAnswering",
     "XLMForQuestionAnsweringSimple",
     "XLMForSequenceClassification",
     "XLMForTokenClassification",
     "XLMModel",
     "XLMPreTrainedModel",
     "XLMWithLMHeadModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFXLMForMultipleChoice",
     "TFXLMForQuestionAnsweringSimple",
     "TFXLMForSequenceClassification",
     "TFXLMForTokenClassification",
     "TFXLMMainLayer",
     "TFXLMModel",
     "TFXLMPreTrainedModel",
     "TFXLMWithLMHeadModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
 from .tokenization_xlm import XLMTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMForMultipleChoice,
      XLMForQuestionAnswering,
      XLMForQuestionAnsweringSimple,
      XLMForSequenceClassification,
      XLMForTokenClassification,
      XLMModel,
      XLMPreTrainedModel,
      XLMWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_xlm import (
      TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXLMForMultipleChoice,
      TFXLMForQuestionAnsweringSimple,
      TFXLMForSequenceClassification,
      TFXLMForTokenClassification,
      TFXLMMainLayer,
      TFXLMModel,
      TFXLMPreTrainedModel,
      TFXLMWithLMHeadModel,
  )

else:
 import sys

 lowercase__         =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




