"""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(					a							):
							__a     				=  SwinaSRConfig()

							if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														__a     				=  4
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														__a     				=  4
														__a     				=  4_8
														__a     				=  '''pixelshuffle_aux'''
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														__a     				=  [6, 6, 6, 6]
														__a     				=  6_0
														__a     				=  [6, 6, 6, 6]
														__a     				=  '''pixelshuffledirect'''
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														__a     				=  4
														__a     				=  '''nearest+conv'''
							elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
														__a     				=  1
														__a     				=  1
														__a     				=  1_2_6
														__a     				=  7
														__a     				=  2_55.0
														__a     				=  ''''''

							return config



def 					_lowerCamelCase(					a ,  a							):



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

							if name == "norm.weight":
														__a     				=  '''layernorm.weight'''
							if name == "norm.bias":
														__a     				=  '''layernorm.bias'''

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

							return name



def 					_lowerCamelCase(					a ,  a							):



							for key in orig_state_dict.copy().keys():
														__a     				=  orig_state_dict.pop(a__							)

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

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

							return orig_state_dict



def 					_lowerCamelCase(					a ,  a ,  a							):
							__a     				=  get_config(a__							)
							__a     				=  SwinaSRForImageSuperResolution(a__							)
							model.eval()

							__a     				=  torch.hub.load_state_dict_from_url(a__ ,  map_location="cpu"							)
							__a     				=  convert_state_dict(a__ ,  a__							)
							__a     				=  model.load_state_dict(a__ ,  strict=a__							)

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

							__a     				=  1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
							__a     				=  Compose(
							    [
							        Resize((image_size, image_size)							),
							        ToTensor(),
							        Normalize(mean=[0.4_85, 0.4_56, 0.4_06] ,  std=[0.2_29, 0.2_24, 0.2_25]							),
							    ]							)
							__a     				=  transforms(a__							).unsqueeze(0							)

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

							__a     				=  model(a__							)

							# assert values
							if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
														__a     				=  torch.Size([1, 3, 5_1_2, 5_1_2]							)
														__a     				=  torch.tensor(
														    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]							)
							elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														__a     				=  torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
														__a     				=  torch.tensor(
														    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]							)
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														# TODO values didn't match exactly here
														__a     				=  torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
														__a     				=  torch.tensor(
														    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]							)
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														__a     				=  torch.Size([1, 3, 5_1_2, 5_1_2]							)
														__a     				=  torch.tensor(
														    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]							)
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														__a     				=  torch.Size([1, 3, 1_0_2_4, 1_0_2_4]							)
														__a     				=  torch.tensor(
														    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]							)

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

							__a     				=  {
							    '''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'''
							    ),
							}
							__a     				=  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(a__							)
														print(F"Saving image processor to {pytorch_dump_folder_path}"							)
														processor.save_pretrained(a__							)

							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__:Optional[int]			=	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__:int			=	parser.parse_args()
			convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_camembert import CamembertTokenizer
else:
   SCREAMING_SNAKE_CASE__:List[Any]			=	None


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Tuple			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE__:str			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		CamembertTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"]  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						additional_special_tokens=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [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 + sep + token_ids_a + sep		) * [0]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  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,)
