


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       (		__A			)						->					List[Any]:



       '''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__										=     4_8
              UpperCAmelCase__										=     "pixelshuffle_aux"
       elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
              UpperCAmelCase__										=     [6, 6, 6, 6]
              UpperCAmelCase__										=     6_0
              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_2_6
              UpperCAmelCase__										=     7
              UpperCAmelCase__										=     255.0
              UpperCAmelCase__										=     ""

       return config





def __UpperCAmelCase       (		__A ,						__A			)						->					Any:



       '''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 __UpperCAmelCase       (		__A ,						__A			)						->					int:



       '''simple docstring'''





       for key in orig_state_dict.copy().keys():
              UpperCAmelCase__										=     orig_state_dict.pop(SCREAMING_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 __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Dict:



       '''simple docstring'''





       UpperCAmelCase__										=     get_config(SCREAMING_SNAKE_CASE_			)
       UpperCAmelCase__										=     SwinaSRForImageSuperResolution(SCREAMING_SNAKE_CASE_			)
       model.eval()

       UpperCAmelCase__										=     torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ ,						map_location="cpu"			)
       UpperCAmelCase__										=     convert_state_dict(SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_			)
       UpperCAmelCase__						,			UpperCAmelCase__										=     model.load_state_dict(SCREAMING_SNAKE_CASE_ ,						strict=SCREAMING_SNAKE_CASE_			)

       if len(SCREAMING_SNAKE_CASE_			) > 0:
              raise ValueError("Missing keys when converting: {}".format(SCREAMING_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(SCREAMING_SNAKE_CASE_ ,						stream=SCREAMING_SNAKE_CASE_			).raw			).convert("RGB"			)
       UpperCAmelCase__										=     SwinaSRImageProcessor()
       # pixel_values = processor(image, return_tensors="pt").pixel_values

       UpperCAmelCase__										=     1_2_6 if "Jpeg" in checkpoint_url else 2_5_6
       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(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)

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

       UpperCAmelCase__										=     model(SCREAMING_SNAKE_CASE_			)

       # assert values
       if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
              UpperCAmelCase__										=     torch.Size([1, 3, 5_1_2, 5_1_2]			)
              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, 1_0_2_4, 1_0_2_4]			)
              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, 1_0_2_4, 1_0_2_4]			)
              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_1_2, 5_1_2]			)
              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, 1_0_2_4, 1_0_2_4]			)
              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] ,						SCREAMING_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(SCREAMING_SNAKE_CASE_			)
              print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
              processor.save_pretrained(SCREAMING_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__":
    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)









import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def __UpperCAmelCase       (		__A			)						->					Union[str, Any]:



       '''simple docstring'''





       if (
           (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
           or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
           or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
           or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
           or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
           or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
           or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
           or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
       ):  #
              return True

       return False





def __UpperCAmelCase       (		__A			)						->					List[str]:



       '''simple docstring'''





       for char in word:
              UpperCAmelCase__										=     ord(__A			)
              if not _is_chinese_char(__A			):
                     return 0
       return 1





def __UpperCAmelCase       (		__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     set()

       for token in tokens:
              UpperCAmelCase__										=     len(__A			) > 1 and is_chinese(__A			)
              if chinese_word:
                     word_set.add(__A			)
       UpperCAmelCase__										=     list(__A			)
       return word_list





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





       if not chinese_word_set:
              return bert_tokens
       UpperCAmelCase__										=     max([len(__A			) for w in chinese_word_set]			)

       UpperCAmelCase__										=     bert_tokens
       UpperCAmelCase__						,			UpperCAmelCase__										=     0, len(__A			)
       while start < end:
              UpperCAmelCase__										=     True
              if is_chinese(bert_word[start]			):
                     UpperCAmelCase__										=     min(end - start ,						__A			)
                     for i in range(__A ,						1 ,						-1			):
                            UpperCAmelCase__										=     "".join(bert_word[start : start + i]			)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,						start + i			):
                                          UpperCAmelCase__										=     "##" + bert_word[j]
                                   UpperCAmelCase__										=     start + i
                                   UpperCAmelCase__										=     False
                                   break
              if single_word:
                     start += 1
       return bert_word





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []

       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     ltp_tokenizer.seg(lines[i : i + 1_0_0]			)[0]
              UpperCAmelCase__										=     [get_chinese_word(__A			) for r in res]
              ltp_res.extend(__A			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     bert_tokenizer(lines[i : i + 1_0_0] ,						add_special_tokens=__A ,						truncation=__A ,						max_length=5_1_2			)
              bert_res.extend(res["input_ids"]			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for input_ids, chinese_word in zip(__A ,						__A			):
              UpperCAmelCase__										=     []
              for id in input_ids:
                     UpperCAmelCase__										=     bert_tokenizer._convert_id_to_token(__A			)
                     input_tokens.append(__A			)
              UpperCAmelCase__										=     add_sub_symbol(__A ,						__A			)
              UpperCAmelCase__										=     []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__A			):
                     if token[:2] == "##":
                            UpperCAmelCase__										=     token[2:]
                            # save chinese tokens' pos
                            if len(__A			) == 1 and _is_chinese_char(ord(__A			)			):
                                   ref_id.append(__A			)
              ref_ids.append(__A			)

       assert len(__A			) == len(__A			)

       return ref_ids





def __UpperCAmelCase       (		__A			)						->					Optional[int]:



       '''simple docstring'''





       with open(args.file_name ,						"r" ,						encoding="utf-8"			) as f:
              UpperCAmelCase__										=     f.readlines()
       UpperCAmelCase__										=     [line.strip() for line in data if len(__A			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       UpperCAmelCase__										=     LTP(args.ltp			)  # faster in GPU device
       UpperCAmelCase__										=     BertTokenizer.from_pretrained(args.bert			)

       UpperCAmelCase__										=     prepare_ref(__A ,						__A ,						__A			)

       with open(args.save_path ,						"w" ,						encoding="utf-8"			) as f:
              UpperCAmelCase__										=     [json.dumps(__A			) + "\n" for ref in ref_ids]
              f.writelines(__A			)


if __name__ == "__main__":
    A      = argparse.ArgumentParser(description="prepare_chinese_ref")
    parser.add_argument(
        "--file_name",
        type=str,
        default="./resources/chinese-demo.txt",
        help="file need process, same as training data in lm",
    )
    parser.add_argument(
        "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
    )
    parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
    parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

    A      = parser.parse_args()
    main(args)





