


"""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__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


		'''simple docstring'''







		lowerCAmelCase					:					List[Any]								=							SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				lowerCAmelCase					:					Optional[int]								=							4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				lowerCAmelCase					:					Any								=							4
				lowerCAmelCase					:					Tuple								=							4_8
				lowerCAmelCase					:					Any								=							"""pixelshuffle_aux"""
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				lowerCAmelCase					:					Any								=							[6, 6, 6, 6]
				lowerCAmelCase					:					Tuple								=							6_0
				lowerCAmelCase					:					int								=							[6, 6, 6, 6]
				lowerCAmelCase					:					List[Any]								=							"""pixelshuffledirect"""
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				lowerCAmelCase					:					Optional[Any]								=							4
				lowerCAmelCase					:					Optional[Any]								=							"""nearest+conv"""
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				lowerCAmelCase					:					Tuple								=							1
				lowerCAmelCase					:					Optional[int]								=							1
				lowerCAmelCase					:					List[Any]								=							1_2_6
				lowerCAmelCase					:					Optional[Any]								=							7
				lowerCAmelCase					:					List[Any]								=							255.0
				lowerCAmelCase					:					Any								=							""""""

		return config
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Dict    ):


		'''simple docstring'''







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

		if name == "norm.weight":
				lowerCAmelCase					:					Optional[Any]								=							"""layernorm.weight"""
		if name == "norm.bias":
				lowerCAmelCase					:					str								=							"""layernorm.bias"""

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

		return name
def  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   str    ):


		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				lowerCAmelCase					:					Tuple								=							orig_state_dict.pop(lowercase__    )

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

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

		return orig_state_dict
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ):


		'''simple docstring'''







		lowerCAmelCase					:					List[str]								=							get_config(lowercase__    )
		lowerCAmelCase					:					Any								=							SwinaSRForImageSuperResolution(lowercase__    )
		model.eval()

		lowerCAmelCase					:					List[str]								=							torch.hub.load_state_dict_from_url(lowercase__    ,				map_location="cpu"    )
		lowerCAmelCase					:					List[Any]								=							convert_state_dict(lowercase__    ,				lowercase__    )
		lowerCAmelCase					:					str								=							model.load_state_dict(lowercase__    ,				strict=lowercase__    )

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

		lowerCAmelCase					:					Any								=							1_2_6 if """Jpeg""" in checkpoint_url else 2_5_6
		lowerCAmelCase					:					Optional[Any]								=							Compose(
		    [
		        Resize((image_size, image_size)    ),
		        ToTensor(),
		        Normalize(mean=[0.485, 0.456, 0.406]    ,				std=[0.229, 0.224, 0.225]    ),
		    ]    )
		lowerCAmelCase					:					int								=							transforms(lowercase__    ).unsqueeze(0    )

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

		lowerCAmelCase					:					int								=							model(lowercase__    )

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				lowerCAmelCase					:					Optional[int]								=							torch.Size([1, 3, 5_1_2, 5_1_2]    )
				lowerCAmelCase					:					Optional[Any]								=							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:
				lowerCAmelCase					:					Optional[Any]								=							torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
				lowerCAmelCase					:					List[Any]								=							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
				lowerCAmelCase					:					List[str]								=							torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
				lowerCAmelCase					:					str								=							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:
				lowerCAmelCase					:					Tuple								=							torch.Size([1, 3, 5_1_2, 5_1_2]    )
				lowerCAmelCase					:					List[Any]								=							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:
				lowerCAmelCase					:					List[str]								=							torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
				lowerCAmelCase					:					Union[str, Any]								=							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]    ,				lowercase__    ,				atol=1E-3    )
		print("Looks ok!"    )

		lowerCAmelCase					:					Tuple								=							{
		    """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"""
		    ),
		}
		lowerCAmelCase					:					Dict								=							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(lowercase__    )
				print(f"""Saving image processor to {pytorch_dump_folder_path}"""    )
				processor.save_pretrained(lowercase__    )

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


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

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









"""simple docstring"""







from ...configuration_utils import PretrainedConfig


lowerCAmelCase__				    =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Any       ="tapas"

       def __init__(						self		,					snake_case__=30_522		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=1_024		,					snake_case__=[3, 256, 256, 2, 256, 256, 10]		,					snake_case__=0.02		,					snake_case__=1e-12		,					snake_case__=0		,					snake_case__=10.0		,					snake_case__=0		,					snake_case__=1.0		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=False		,					snake_case__="ratio"		,					snake_case__=None		,					snake_case__=None		,					snake_case__=64		,					snake_case__=32		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=None		,					snake_case__=None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(pad_token_id=snake_case__		,					**snake_case__						)

         # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
         lowerCAmelCase					:					List[Any]								=							vocab_size
         lowerCAmelCase					:					List[str]								=							hidden_size
         lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
         lowerCAmelCase					:					List[str]								=							num_attention_heads
         lowerCAmelCase					:					Optional[Any]								=							hidden_act
         lowerCAmelCase					:					List[Any]								=							intermediate_size
         lowerCAmelCase					:					Optional[Any]								=							hidden_dropout_prob
         lowerCAmelCase					:					str								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							max_position_embeddings
         lowerCAmelCase					:					Dict								=							type_vocab_sizes
         lowerCAmelCase					:					Union[str, Any]								=							initializer_range
         lowerCAmelCase					:					str								=							layer_norm_eps

         # Fine-tuning task hyperparameters
         lowerCAmelCase					:					Dict								=							positive_label_weight
         lowerCAmelCase					:					Union[str, Any]								=							num_aggregation_labels
         lowerCAmelCase					:					Optional[Any]								=							aggregation_loss_weight
         lowerCAmelCase					:					List[Any]								=							use_answer_as_supervision
         lowerCAmelCase					:					Dict								=							answer_loss_importance
         lowerCAmelCase					:					List[Any]								=							use_normalized_answer_loss
         lowerCAmelCase					:					List[str]								=							huber_loss_delta
         lowerCAmelCase					:					Optional[int]								=							temperature
         lowerCAmelCase					:					Optional[int]								=							aggregation_temperature
         lowerCAmelCase					:					Any								=							use_gumbel_for_cells
         lowerCAmelCase					:					Union[str, Any]								=							use_gumbel_for_aggregation
         lowerCAmelCase					:					Union[str, Any]								=							average_approximation_function
         lowerCAmelCase					:					int								=							cell_selection_preference
         lowerCAmelCase					:					Dict								=							answer_loss_cutoff
         lowerCAmelCase					:					Optional[int]								=							max_num_rows
         lowerCAmelCase					:					Union[str, Any]								=							max_num_columns
         lowerCAmelCase					:					Any								=							average_logits_per_cell
         lowerCAmelCase					:					List[Any]								=							select_one_column
         lowerCAmelCase					:					Tuple								=							allow_empty_column_selection
         lowerCAmelCase					:					str								=							init_cell_selection_weights_to_zero
         lowerCAmelCase					:					List[Any]								=							reset_position_index_per_cell
         lowerCAmelCase					:					Optional[Any]								=							disable_per_token_loss

         # Aggregation hyperparameters
         lowerCAmelCase					:					List[str]								=							aggregation_labels
         lowerCAmelCase					:					List[str]								=							no_aggregation_label_index

         if isinstance(self.aggregation_labels		,					snake_case__						):
           lowerCAmelCase					:					Union[str, Any]								=							{int(snake_case__						): v for k, v in aggregation_labels.items()}





