
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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				SwinaSRConfig()

    if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        snake_case__						=				4
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        snake_case__						=				4
        snake_case__						=				48
        snake_case__						=				"pixelshuffle_aux"
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        snake_case__						=				[6, 6, 6, 6]
        snake_case__						=				60
        snake_case__						=				[6, 6, 6, 6]
        snake_case__						=				"pixelshuffledirect"
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        snake_case__						=				4
        snake_case__						=				"nearest+conv"
    elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
        snake_case__						=				1
        snake_case__						=				1
        snake_case__						=				126
        snake_case__						=				7
        snake_case__						=				255.0
        snake_case__						=				""

    return config





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

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

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

    return name





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    for key in orig_state_dict.copy().keys():
        snake_case__						=				orig_state_dict.pop(__lowerCAmelCase							)

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

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

    return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				get_config(__lowerCAmelCase							)
    snake_case__						=				SwinaSRForImageSuperResolution(__lowerCAmelCase							)
    model.eval()

    snake_case__						=				torch.hub.load_state_dict_from_url(__lowerCAmelCase    ,		map_location="cpu"							)
    snake_case__						=				convert_state_dict(__lowerCAmelCase    ,		__lowerCAmelCase							)
    snake_case__						,					snake_case__						=				model.load_state_dict(__lowerCAmelCase    ,		strict=__lowerCAmelCase							)

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

    snake_case__						=				126 if "Jpeg" in checkpoint_url else 256
    snake_case__						=				Compose(
        [
            Resize((image_size, image_size)							),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]    ,		std=[0.229, 0.224, 0.225]							),
        ]							)
    snake_case__						=				transforms(__lowerCAmelCase							).unsqueeze(0							)

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

    snake_case__						=				model(__lowerCAmelCase							)

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        snake_case__						=				torch.Size([1, 3, 512, 512]							)
        snake_case__						=				torch.tensor(
            [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]							)
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        snake_case__						=				torch.Size([1, 3, 1_024, 1_024]							)
        snake_case__						=				torch.tensor(
            [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]							)
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        snake_case__						=				torch.Size([1, 3, 1_024, 1_024]							)
        snake_case__						=				torch.tensor(
            [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]							)
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        snake_case__						=				torch.Size([1, 3, 512, 512]							)
        snake_case__						=				torch.tensor(
            [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]							)
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        snake_case__						=				torch.Size([1, 3, 1_024, 1_024]							)
        snake_case__						=				torch.tensor(
            [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]							)

    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]    ,		__lowerCAmelCase    ,		atol=1E-3							)
    print("Looks ok!"							)

    snake_case__						=				{
        "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"
        ),
    }
    snake_case__						=				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(__lowerCAmelCase							)
        print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
        processor.save_pretrained(__lowerCAmelCase							)

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


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

     __magic_name__            =  parser.parse_args()
     convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''sentencepiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__magic_name__            =  {
    '''google/rembert''': 256,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

          snake_case__						=				do_lower_case
          snake_case__						=				remove_space
          snake_case__						=				keep_accents
          snake_case__						=				vocab_file

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):

          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model." )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          snake_case__						=				[self.sep_token_id]
          snake_case__						=				[self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(lowerCamelCase ):
              logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase ) )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



