
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from transformers.modeling_outputs import BaseModelOutput

     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

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



@add_end_docstrings(UpperCAmelCase_				)
class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   if self.framework == "tf":
     raise ValueError(f'The {self.__class__} is only available in PyTorch.')

   requires_backends(self       ,	"""vision""")
   self.check_model_type(SCREAMING_SNAKE_CASE_)


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   if "text_queries" in kwargs:
     lowercase__	: Any     		=   kwargs.pop("""text_queries""")

   if isinstance(SCREAMING_SNAKE_CASE_       ,	(str, Image.Image)):
     lowercase__	: Optional[Any]     		=   {"""image""": image, """candidate_labels""": candidate_labels}
   else:
     lowercase__	: int     		=   image
   lowercase__	: List[str]     		=   super().__call__(SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   return results


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {}
   if "threshold" in kwargs:
     lowercase__	: List[Any]     		=   kwargs["""threshold"""]
   if "top_k" in kwargs:
     lowercase__	: int     		=   kwargs["""top_k"""]
   return {}, {}, postprocess_params


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   load_image(inputs["""image"""])
   lowercase__	: Any     		=   inputs["""candidate_labels"""]
   if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     lowercase__	: List[str]     		=   candidate_labels.split(""",""")

   lowercase__	: Tuple     		=   torch.tensor([[image.height, image.width]]       ,	dtype=torch.intaa)
   for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_):
     lowercase__	: Optional[Any]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     lowercase__	: Union[str, Any]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=self.framework)
     yield {
         "is_last": i == len(SCREAMING_SNAKE_CASE_) - 1,
         "target_size": target_size,
         "candidate_label": candidate_label,
         **text_inputs,
         **image_features,
     }


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   model_inputs.pop("""target_size""")
   lowercase__	: Optional[int]     		=   model_inputs.pop("""candidate_label""")
   lowercase__	: Dict     		=   model_inputs.pop("""is_last""")

   lowercase__	: Union[str, Any]     		=   self.model(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Union[str, Any]     		=   {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
   return model_outputs


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   []
   for model_output in model_outputs:
     lowercase__	: Optional[int]     		=   model_output["""candidate_label"""]
     lowercase__	: Tuple     		=   BaseModelOutput(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   self.image_processor.post_process_object_detection(
         outputs=SCREAMING_SNAKE_CASE_       ,	threshold=SCREAMING_SNAKE_CASE_       ,	target_sizes=model_output["""target_size"""])[0]

     for index in outputs["scores"].nonzero():
       lowercase__	: Optional[Any]     		=   outputs["""scores"""][index].item()
       lowercase__	: Optional[Any]     		=   self._get_bounding_box(outputs["""boxes"""][index][0])

       lowercase__	: Tuple     		=   {"""score""": score, """label""": label, """box""": box}
       results.append(SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   sorted(SCREAMING_SNAKE_CASE_       ,	key=lambda SCREAMING_SNAKE_CASE_: x["score"]       ,	reverse=SCREAMING_SNAKE_CASE_)
   if top_k:
     lowercase__	: Any     		=   results[:top_k]

   return results



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if self.framework != "pt":
     raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""")
   lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: List[Any]     		=   box.int().tolist()
   lowercase__	: Optional[int]     		=   {
       """xmin""": xmin,
       """ymin""": ymin,
       """xmax""": xmax,
       """ymax""": ymax,
   }
   return bbox








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						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    lowercase__	: List[str]     		=   4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    lowercase__	: Optional[int]     		=   4
    lowercase__	: Optional[Any]     		=   48
    lowercase__	: int     		=   """pixelshuffle_aux"""
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    lowercase__	: List[str]     		=   [6, 6, 6, 6]
    lowercase__	: Any     		=   60
    lowercase__	: Tuple     		=   [6, 6, 6, 6]
    lowercase__	: Dict     		=   """pixelshuffledirect"""
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    lowercase__	: Tuple     		=   4
    lowercase__	: Any     		=   """nearest+conv"""
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    lowercase__	: str     		=   1
    lowercase__	: Optional[int]     		=   1
    lowercase__	: Optional[int]     		=   1_26
    lowercase__	: Any     		=   7
    lowercase__	: int     		=   255.0
    lowercase__	: List[Any]     		=   """"""

  return config

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






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

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

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

  return name

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  for key in orig_state_dict.copy().keys():
    lowercase__	: str     		=   orig_state_dict.pop(lowercase_ )

    if "qkv" in key:
      lowercase__	: Any     		=   key.split(""".""" )
      lowercase__	: List[Any]     		=   int(key_split[1] )
      lowercase__	: Dict     		=   int(key_split[4] )
      lowercase__	: Optional[Any]     		=   config.embed_dim

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

  return orig_state_dict

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Dict     		=   get_config(lowercase_ )
  lowercase__	: Any     		=   SwinaSRForImageSuperResolution(lowercase_ )
  model.eval()

  lowercase__	: List[str]     		=   torch.hub.load_state_dict_from_url(lowercase_			,			map_location="""cpu""" )
  lowercase__	: Union[str, Any]     		=   convert_state_dict(lowercase_			,			lowercase_ )
  lowercase__		,     lowercase__	: Dict     		=   model.load_state_dict(lowercase_			,			strict=lowercase_ )

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

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

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

  lowercase__	: Union[str, Any]     		=   model(lowercase_ )

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: Optional[Any]     		=   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:
    lowercase__	: List[str]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: int     		=   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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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]			,			lowercase_			,			atol=1E-3 )
  print("""Looks ok!""" )

  lowercase__	: 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"""
      ),
  }
  lowercase__	: 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(lowercase_ )
    print(F'Saving image processor to {pytorch_dump_folder_path}' )
    processor.save_pretrained(lowercase_ )

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


if __name__ == "__main__":
     lowerCamelCase__     :		List[str]        = 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.""")

     lowerCamelCase__     :		Any        = parser.parse_args()
     convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






