






'''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(  UpperCamelCase__				:						Tuple							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=SwinaSRConfig()

      if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=4
      elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
            SCREAMING_SNAKE_CASE__     :				int									=4
            SCREAMING_SNAKE_CASE__     :				Any									=4_8
            SCREAMING_SNAKE_CASE__     :				List[Any]									='''pixelshuffle_aux'''
      elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=[6, 6, 6, 6]
            SCREAMING_SNAKE_CASE__     :				Tuple									=6_0
            SCREAMING_SNAKE_CASE__     :				List[Any]									=[6, 6, 6, 6]
            SCREAMING_SNAKE_CASE__     :				Tuple									='''pixelshuffledirect'''
      elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=4
            SCREAMING_SNAKE_CASE__     :				Any									='''nearest+conv'''
      elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=1
            SCREAMING_SNAKE_CASE__     :				int									=1
            SCREAMING_SNAKE_CASE__     :				Any									=1_2_6
            SCREAMING_SNAKE_CASE__     :				Dict									=7
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=2_5_5.0
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=''''''

      return config





def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						Dict							):




      '''simple docstring'''






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

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									='''layernorm.weight'''
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE__     :				Optional[int]									='''layernorm.bias'''

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

      return name





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Tuple							):




      '''simple docstring'''






      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE__     :				List[Any]									=orig_state_dict.pop(UpperCamelCase__							)

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

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

      return orig_state_dict





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=get_config(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Dict									=SwinaSRForImageSuperResolution(UpperCamelCase__							)
      model.eval()

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

      SCREAMING_SNAKE_CASE__     :				List[Any]									=1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
      SCREAMING_SNAKE_CASE__     :				str									=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]							),
          ]							)
      SCREAMING_SNAKE_CASE__     :				int									=transforms(UpperCamelCase__							).unsqueeze(0							)

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

      SCREAMING_SNAKE_CASE__     :				List[str]									=model(UpperCamelCase__							)

      # assert values
      if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
            SCREAMING_SNAKE_CASE__     :				int									=torch.Size([1, 3, 5_1_2, 5_1_2]							)
            SCREAMING_SNAKE_CASE__     :				Tuple									=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:
            SCREAMING_SNAKE_CASE__     :				Any									=torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
            SCREAMING_SNAKE_CASE__     :				Dict									=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
            SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=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:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=torch.Size([1, 3, 5_1_2, 5_1_2]							)
            SCREAMING_SNAKE_CASE__     :				Tuple									=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:
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=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!'''							)

      SCREAMING_SNAKE_CASE__     :				List[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'''
          ),
      }
      SCREAMING_SNAKE_CASE__     :				int									=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)







'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
  raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a_					= False
a_					= False





def  _a(  UpperCamelCase__				:						Namespace							):




      '''simple docstring'''






      return TrainCommand(UpperCamelCase__							)







class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 @staticmethod
 def      __magic_name__	(      __lowercase	:							ArgumentParser      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=parser.add_parser('''train'''					,      help='''CLI tool to train a model on a task.'''      )

       train_parser.add_argument(
           '''--train_data'''					,      type=__lowercase					,      required=__lowercase					,      help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''					,      )
       train_parser.add_argument(
           '''--column_label'''					,      type=__lowercase					,      default=0					,      help='''Column of the dataset csv file with example labels.'''      )
       train_parser.add_argument(
           '''--column_text'''					,      type=__lowercase					,      default=1					,      help='''Column of the dataset csv file with example texts.'''      )
       train_parser.add_argument(
           '''--column_id'''					,      type=__lowercase					,      default=2					,      help='''Column of the dataset csv file with example ids.'''      )
       train_parser.add_argument(
           '''--skip_first_row'''					,      action='''store_true'''					,      help='''Skip the first row of the csv file (headers).'''      )

       train_parser.add_argument('''--validation_data'''					,      type=__lowercase					,      default=''''''					,      help='''path to validation dataset.'''      )
       train_parser.add_argument(
           '''--validation_split'''					,      type=__lowercase					,      default=0.1					,      help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.'''					,      )

       train_parser.add_argument('''--output'''					,      type=__lowercase					,      default='''./'''					,      help='''path to saved the trained model.'''      )

       train_parser.add_argument(
           '''--task'''					,      type=__lowercase					,      default='''text_classification'''					,      help='''Task to train the model on.'''      )
       train_parser.add_argument(
           '''--model'''					,      type=__lowercase					,      default='''bert-base-uncased'''					,      help='''Model\'s name or path to stored model.'''      )
       train_parser.add_argument('''--train_batch_size'''					,      type=__lowercase					,      default=32					,      help='''Batch size for training.'''      )
       train_parser.add_argument('''--valid_batch_size'''					,      type=__lowercase					,      default=64					,      help='''Batch size for validation.'''      )
       train_parser.add_argument('''--learning_rate'''					,      type=__lowercase					,      default=3e-5					,      help='''Learning rate.'''      )
       train_parser.add_argument('''--adam_epsilon'''					,      type=__lowercase					,      default=1e-08					,      help='''Epsilon for Adam optimizer.'''      )
       train_parser.set_defaults(func=__lowercase      )




 def __init__(      self	:							Tuple					,      __lowercase	:							Namespace      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=logging.get_logger('''transformers-cli/training'''      )

       SCREAMING_SNAKE_CASE__     :				int									='''tf''' if is_tf_available() else '''torch'''

       os.makedirs(args.output					,      exist_ok=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=args.output

       SCREAMING_SNAKE_CASE__     :				str									=args.column_label
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.column_text
       SCREAMING_SNAKE_CASE__     :				Tuple									=args.column_id

       self.logger.info(F"Loading {args.task} pipeline for {args.model}"      )
       if args.task == "text_classification":
             SCREAMING_SNAKE_CASE__     :				List[str]									=TextClassificationPipeline.from_pretrained(args.model      )
       elif args.task == "token_classification":
             raise NotImplementedError
       elif args.task == "question_answering":
             raise NotImplementedError

       self.logger.info(F"Loading dataset from {args.train_data}"      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Processor.create_from_csv(
           args.train_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None
       if args.validation_data:
             self.logger.info(F"Loading validation dataset from {args.validation_data}"      )
             SCREAMING_SNAKE_CASE__     :				List[Any]									=Processor.create_from_csv(
                 args.validation_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=args.validation_split
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.train_batch_size
       SCREAMING_SNAKE_CASE__     :				Any									=args.valid_batch_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=args.learning_rate
       SCREAMING_SNAKE_CASE__     :				int									=args.adam_epsilon




 def      __magic_name__	(      self	:							Any      )	->						str:
       if self.framework == "tf":
             return self.run_tf()
       return self.run_torch()




 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       raise NotImplementedError




 def      __magic_name__	(      self	:							Dict      )	->						List[Any]:
       self.pipeline.fit(
           self.train_dataset					,      validation_data=self.valid_dataset					,      validation_split=self.validation_split					,      learning_rate=self.learning_rate					,      adam_epsilon=self.adam_epsilon					,      train_batch_size=self.train_batch_size					,      valid_batch_size=self.valid_batch_size					,      )

       # Save trained pipeline
       self.pipeline.save_pretrained(self.output      )