'''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 	lowerCAmelCase_				(					snake_case_       : int			)    ->      int:


     '''simple docstring'''




     UpperCAmelCase_           =   SwinaSRConfig()

     if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
          UpperCAmelCase_           =   4
     elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
          UpperCAmelCase_           =   4
          UpperCAmelCase_           =   48
          UpperCAmelCase_           =   "pixelshuffle_aux"
     elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
          UpperCAmelCase_           =   [6, 6, 6, 6]
          UpperCAmelCase_           =   60
          UpperCAmelCase_           =   [6, 6, 6, 6]
          UpperCAmelCase_           =   "pixelshuffledirect"
     elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
          UpperCAmelCase_           =   4
          UpperCAmelCase_           =   "nearest+conv"
     elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
          UpperCAmelCase_           =   1
          UpperCAmelCase_           =   1
          UpperCAmelCase_           =   1_26
          UpperCAmelCase_           =   7
          UpperCAmelCase_           =   255.0
          UpperCAmelCase_           =   ""

     return config





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : Optional[Any]			)    ->      List[str]:


     '''simple docstring'''




     if "patch_embed.proj" in name and "layers" not in name:
          UpperCAmelCase_           =   name.replace("patch_embed.proj"					,    "embeddings.patch_embeddings.projection"			)
     if "patch_embed.norm" in name:
          UpperCAmelCase_           =   name.replace("patch_embed.norm"					,    "embeddings.patch_embeddings.layernorm"			)
     if "layers" in name:
          UpperCAmelCase_           =   name.replace("layers"					,    "encoder.stages"			)
     if "residual_group.blocks" in name:
          UpperCAmelCase_           =   name.replace("residual_group.blocks"					,    "layers"			)
     if "attn.proj" in name:
          UpperCAmelCase_           =   name.replace("attn.proj"					,    "attention.output.dense"			)
     if "attn" in name:
          UpperCAmelCase_           =   name.replace("attn"					,    "attention.self"			)
     if "norm1" in name:
          UpperCAmelCase_           =   name.replace("norm1"					,    "layernorm_before"			)
     if "norm2" in name:
          UpperCAmelCase_           =   name.replace("norm2"					,    "layernorm_after"			)
     if "mlp.fc1" in name:
          UpperCAmelCase_           =   name.replace("mlp.fc1"					,    "intermediate.dense"			)
     if "mlp.fc2" in name:
          UpperCAmelCase_           =   name.replace("mlp.fc2"					,    "output.dense"			)
     if "q_bias" in name:
          UpperCAmelCase_           =   name.replace("q_bias"					,    "query.bias"			)
     if "k_bias" in name:
          UpperCAmelCase_           =   name.replace("k_bias"					,    "key.bias"			)
     if "v_bias" in name:
          UpperCAmelCase_           =   name.replace("v_bias"					,    "value.bias"			)
     if "cpb_mlp" in name:
          UpperCAmelCase_           =   name.replace("cpb_mlp"					,    "continuous_position_bias_mlp"			)
     if "patch_embed.proj" in name:
          UpperCAmelCase_           =   name.replace("patch_embed.proj"					,    "patch_embed.projection"			)

     if name == "norm.weight":
          UpperCAmelCase_           =   "layernorm.weight"
     if name == "norm.bias":
          UpperCAmelCase_           =   "layernorm.bias"

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

     return name





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : Optional[Any]			)    ->      Any:


     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_           =   orig_state_dict.pop(snake_case_			)

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

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

     return orig_state_dict





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : Dict					,    snake_case_       : Any			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   get_config(snake_case_			)
     UpperCAmelCase_           =   SwinaSRForImageSuperResolution(snake_case_			)
     model.eval()

     UpperCAmelCase_           =   torch.hub.load_state_dict_from_url(snake_case_					,    map_location="cpu"			)
     UpperCAmelCase_           =   convert_state_dict(snake_case_					,    snake_case_			)
     UpperCAmelCase_    , UpperCAmelCase_           =   model.load_state_dict(snake_case_					,    strict=snake_case_			)

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

     UpperCAmelCase_           =   1_26 if "Jpeg" in checkpoint_url else 2_56
     UpperCAmelCase_           =   Compose(
         [
             Resize((image_size, image_size)			),
             ToTensor(),
             Normalize(mean=[0.485, 0.456, 0.406]					,    std=[0.229, 0.224, 0.225]			),
         ]			)
     UpperCAmelCase_           =   transforms(snake_case_			).unsqueeze(0			)

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

     UpperCAmelCase_           =   model(snake_case_			)

     # assert values
     if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
          UpperCAmelCase_           =   torch.Size([1, 3, 5_12, 5_12]			)
          UpperCAmelCase_           =   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_           =   torch.Size([1, 3, 10_24, 10_24]			)
          UpperCAmelCase_           =   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_           =   torch.Size([1, 3, 10_24, 10_24]			)
          UpperCAmelCase_           =   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_           =   torch.Size([1, 3, 5_12, 5_12]			)
          UpperCAmelCase_           =   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_           =   torch.Size([1, 3, 10_24, 10_24]			)
          UpperCAmelCase_           =   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]					,    snake_case_					,    atol=1E-3			)
     print("Looks ok!"			)

     UpperCAmelCase_           =   {
         "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_           =   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(snake_case_			)
          print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
          processor.save_pretrained(snake_case_			)

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     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.')

    SCREAMING_SNAKE_CASE_:     List[Any]  =parser.parse_args()
    convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   TapasConfig.from_json_file(__lowercase       )
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     							=   reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   True
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   0.66_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "TABFACT":
          _UpperCAmelCase     							=   TapasForSequenceClassification(config=__lowercase       )
     elif task == "MLM":
          _UpperCAmelCase     							=   TapasForMaskedLM(config=__lowercase       )
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     							=   TapasModel(config=__lowercase       )
     else:
          raise ValueError(f'Task {task} not supported.'       )

     print(f'Building PyTorch model from configuration: {config}'       )
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(__lowercase			,     __lowercase			,     __lowercase       )

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     model.save_pretrained(__lowercase       )

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'       )
     _UpperCAmelCase     							=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,     model_max_length=512       )
     tokenizer.save_pretrained(__lowercase       )

     print("Used relative position embeddings:"			,     model.config.reset_position_index_per_cell       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
  )
  parser.add_argument(
      '''--reset_position_index_per_cell''',
      default=False,
      action='''store_true''',
      help='''Whether to use relative position embeddings or not. Defaults to True.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--tapas_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained TAPAS model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )




