
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_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			SwinaSRConfig()

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

				return config





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

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

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

				return name





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

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

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

				return orig_state_dict





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			get_config(lowerCamelCase__    )
				lowerCamelCase_						=			SwinaSRForImageSuperResolution(lowerCamelCase__    )
				model.eval()

				lowerCamelCase_						=			torch.hub.load_state_dict_from_url(lowerCamelCase__	, map_location="cpu"    )
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			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
				lowerCamelCase_						=			"https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
				lowerCamelCase_						=			Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__    ).raw    ).convert("RGB"    )
				lowerCamelCase_						=			SwinaSRImageProcessor()
				# pixel_values = processor(image, return_tensors="pt").pixel_values

				lowerCamelCase_						=			1_2_6 if "Jpeg" in checkpoint_url else 2_5_6
				lowerCamelCase_						=			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]    ),
				    ]    )
				lowerCamelCase_						=			transforms(lowerCamelCase__    ).unsqueeze(0    )

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

				lowerCamelCase_						=			model(lowerCamelCase__    )

				# assert values
				if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
								lowerCamelCase_						=			torch.Size([1, 3, 5_1_2, 5_1_2]    )
								lowerCamelCase_						=			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:
								lowerCamelCase_						=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
								lowerCamelCase_						=			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
								lowerCamelCase_						=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
								lowerCamelCase_						=			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:
								lowerCamelCase_						=			torch.Size([1, 3, 5_1_2, 5_1_2]    )
								lowerCamelCase_						=			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:
								lowerCamelCase_						=			torch.Size([1, 3, 1_0_2_4, 1_0_2_4]    )
								lowerCamelCase_						=			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]	, lowerCamelCase__	, atol=1e-3    )
				print("Looks ok!"    )

				lowerCamelCase_						=			{
				    "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_						=			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__":
			__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 copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:Dict             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      ["input_features", "is_longer"]






    def __init__(       self,		lowerCamelCase__=64,		lowerCamelCase__=4_8000,		lowerCamelCase__=480,		lowerCamelCase__=10,		lowerCamelCase__=1024,		lowerCamelCase__=0.0,		lowerCamelCase__=False,		lowerCamelCase__ = 0,		lowerCamelCase__ = 1_4000,		lowerCamelCase__ = None,		lowerCamelCase__ = "fusion",		lowerCamelCase__ = "repeatpad",		**lowerCamelCase__,		):
        super().__init__(
            feature_size=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		padding_value=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        A :       Tuple          =   top_db
        A :       Dict          =   truncation
        A :       int          =   padding
        A :       Optional[Any]          =   fft_window_size
        A :       Optional[Any]          =   (fft_window_size >> 1) + 1
        A :       List[Any]          =   hop_length
        A :       Tuple          =   max_length_s
        A :       str          =   max_length_s * sampling_rate
        A :       List[str]          =   sampling_rate
        A :       Dict          =   frequency_min
        A :       str          =   frequency_max
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm=lowerCamelCase__,		mel_scale="""htk""",		)
        A :       Union[str, Any]          =   mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins,		num_mel_filters=lowerCamelCase__,		min_frequency=lowerCamelCase__,		max_frequency=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		norm="""slaney""",		mel_scale="""slaney""",		)






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   copy.deepcopy(self.__dict__		)
        A :       Tuple          =   self.__class__.__name__
        if "mel_filters" in output:
            del output["mel_filters"]
        if "mel_filters_slaney" in output:
            del output["mel_filters_slaney"]
        return output






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        A :       Optional[Any]          =   spectrogram(
            lowerCamelCase__,		window_function(self.fft_window_size,		"""hann"""		),		frame_length=self.fft_window_size,		hop_length=self.hop_length,		power=2.0,		mel_filters=lowerCamelCase__,		log_mel="""dB""",		)
        return log_mel_spectrogram.T






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   np.array_split(list(range(0,		total_frames - chunk_frames + 1		)		),		3		)
        if len(ranges[1]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Any          =   [0]
        if len(ranges[2]		) == 0:
            # if the audio is too short, we just use the first chunk
            A :       Union[str, Any]          =   [0]
        # randomly choose index for each part
        A :       str          =   np.random.choice(ranges[0]		)
        A :       str          =   np.random.choice(ranges[1]		)
        A :       Tuple          =   np.random.choice(ranges[2]		)

        A :       List[str]          =   mel[idx_front : idx_front + chunk_frames, :]
        A :       Union[str, Any]          =   mel[idx_middle : idx_middle + chunk_frames, :]
        A :       Any          =   mel[idx_back : idx_back + chunk_frames, :]

        A :       Any          =   torch.tensor(mel[None, None, :]		)
        A :       Union[str, Any]          =   torch.nn.functional.interpolate(
            lowerCamelCase__,		size=[chunk_frames, 64],		mode="""bilinear""",		align_corners=lowerCamelCase__		)
        A :       List[Any]          =   mel_shrink[0][0].numpy()
        A :       Any          =   np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],		axis=0		)
        return mel_fusion






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
                A :       List[Any]          =   True
                # random crop to max_length (for compatibility) -> this should be handled by self.pad
                A :       int          =   len(lowerCamelCase__		) - max_length
                A :       Optional[int]          =   np.random.randint(0,		overflow + 1		)
                A :       List[Any]          =   waveform[idx : idx + max_length]
                A :       Optional[Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]
            elif truncation == "fusion":
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       List[str]          =   max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                A :       Optional[Any]          =   mel.shape[0]
                if chunk_frames == total_frames:
                    # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                    # In this case, we just use the whole audio.
                    A :       str          =   np.stack([mel, mel, mel, mel],		axis=0		)
                    A :       Union[str, Any]          =   False
                else:
                    A :       Dict          =   self._random_mel_fusion(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
                    A :       Tuple          =   True
            else:
                raise NotImplementedError(f'''data_truncating {truncation} not implemented'''		)

        else:
            A :       int          =   False
            # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
            if waveform.shape[0] < max_length:
                if padding == "repeat":
                    A :       Optional[int]          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Union[str, Any]          =   np.stack(np.tile(lowerCamelCase__,		n_repeat + 1		)		)[:max_length]
                if padding == "repeatpad":
                    A :       Dict          =   int(max_length / len(lowerCamelCase__		)		)
                    A :       Dict          =   np.stack(np.tile(lowerCamelCase__,		lowerCamelCase__		)		)
                A :       Any          =   np.pad(lowerCamelCase__,		(0, max_length - waveform.shape[0]),		mode="""constant""",		constant_values=0		)

            if truncation == "fusion":
                A :       Union[str, Any]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters		)
                A :       Tuple          =   np.stack([input_mel, input_mel, input_mel, input_mel],		axis=0		)
            else:
                A :       List[str]          =   self._np_extract_fbank_features(lowerCamelCase__,		self.mel_filters_slaney		)[None, :]

        return input_mel, longer






    def __call__(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Any          =   truncation if truncation is not None else self.truncation
        A :       Union[str, Any]          =   padding if padding else self.padding

        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                    f''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''		)
        else:
            logger.warning(
                """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                """Failing to do so can result in silent errors that might be hard to debug."""		)

        A :       str          =   isinstance(lowerCamelCase__,		np.ndarray		) and len(raw_speech.shape		) > 1
        if is_batched_numpy and len(raw_speech.shape		) > 2:
            raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''		)
        A :       Any          =   is_batched_numpy or (
            isinstance(lowerCamelCase__,		(list, tuple)		) and (isinstance(raw_speech[0],		(np.ndarray, tuple, list)		))
        )

        if is_batched:
            A :       Optional[int]          =   [np.asarray(lowerCamelCase__,		dtype=np.floataa		) for speech in raw_speech]
        elif not is_batched and not isinstance(lowerCamelCase__,		np.ndarray		):
            A :       int          =   np.asarray(lowerCamelCase__,		dtype=np.floataa		)
        elif isinstance(lowerCamelCase__,		np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
            A :       int          =   raw_speech.astype(np.floataa		)

        # always return batch
        if not is_batched:
            A :       List[Any]          =   [np.asarray(lowerCamelCase__		)]

        # convert to mel spectrogram, truncate and pad if needed.
        A :       Tuple          =   [
            self._get_input_mel(lowerCamelCase__,		max_length if max_length else self.nb_max_samples,		lowerCamelCase__,		lowerCamelCase__		)
            for waveform in raw_speech
        ]

        A :       Optional[Any]          =   []
        A :       Optional[Any]          =   []
        for mel, longer in padded_inputs:
            input_mel.append(lowerCamelCase__		)
            is_longer.append(lowerCamelCase__		)

        if truncation == "fusion" and sum(lowerCamelCase__		) == 0:
            # if no audio is longer than 10s, then randomly select one audio to be longer
            A :       Tuple          =   np.random.randint(0,		len(lowerCamelCase__		)		)
            A :       str          =   True

        if isinstance(input_mel[0],		lowerCamelCase__		):
            A :       int          =   [np.asarray(lowerCamelCase__,		dtype=np.floataa		) for feature in input_mel]

        # is_longer is a list of bool
        A :       Optional[int]          =   [[longer] for longer in is_longer]

        A :       Union[str, Any]          =   {"""input_features""": input_mel, """is_longer""": is_longer}
        A :       Tuple          =   BatchFeature(lowerCamelCase__		)

        if return_tensors is not None:
            A :       List[str]          =   input_features.convert_to_tensors(lowerCamelCase__		)

        return input_features


