




"""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 						UpperCAmelCase				(			A__:    Optional[int]     )  ->     int:

			'''simple docstring'''





			__lowerCamelCase					:	int	       =				SwinaSRConfig()

			if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
						__lowerCamelCase					:	List[Any]	       =				4
			elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
						__lowerCamelCase					:	str	       =				4
						__lowerCamelCase					:	Union[str, Any]	       =				48
						__lowerCamelCase					:	Any	       =				'pixelshuffle_aux'
			elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
						__lowerCamelCase					:	Union[str, Any]	       =				[6, 6, 6, 6]
						__lowerCamelCase					:	Any	       =				60
						__lowerCamelCase					:	List[str]	       =				[6, 6, 6, 6]
						__lowerCamelCase					:	Optional[Any]	       =				'pixelshuffledirect'
			elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
						__lowerCamelCase					:	Dict	       =				4
						__lowerCamelCase					:	Union[str, Any]	       =				'nearest+conv'
			elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
						__lowerCamelCase					:	Any	       =				1
						__lowerCamelCase					:	Any	       =				1
						__lowerCamelCase					:	List[str]	       =				126
						__lowerCamelCase					:	Optional[Any]	       =				7
						__lowerCamelCase					:	List[str]	       =				255.0
						__lowerCamelCase					:	str	       =				''

			return config





def 						UpperCAmelCase				(			A__:    str							,				A__:    int     )  ->     Dict:

			'''simple docstring'''





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

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

			return name





def 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    List[str]     )  ->     Dict:

			'''simple docstring'''





			for key in orig_state_dict.copy().keys():
						__lowerCamelCase					:	List[Any]	       =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_     )

						if "qkv" in key:
									__lowerCamelCase					:	Optional[int]	       =				key.split('.'     )
									__lowerCamelCase					:	Optional[Any]	       =				int(key_split[1]     )
									__lowerCamelCase					:	Union[str, Any]	       =				int(key_split[4]     )
									__lowerCamelCase					:	str	       =				config.embed_dim

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

			return orig_state_dict





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    List[Any]							,				A__:    List[Any]     )  ->     int:

			'''simple docstring'''





			__lowerCamelCase					:	Dict	       =				get_config(SCREAMING_SNAKE_CASE_     )
			__lowerCamelCase					:	Dict	       =				SwinaSRForImageSuperResolution(SCREAMING_SNAKE_CASE_     )
			model.eval()

			__lowerCamelCase					:	Dict	       =				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_							,				map_location='cpu'     )
			__lowerCamelCase					:	str	       =				convert_state_dict(SCREAMING_SNAKE_CASE_							,				SCREAMING_SNAKE_CASE_     )
			__lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				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
			__lowerCamelCase					:	Tuple	       =				'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
			__lowerCamelCase					:	List[str]	       =				Image.open(requests.get(SCREAMING_SNAKE_CASE_							,				stream=SCREAMING_SNAKE_CASE_     ).raw     ).convert('RGB'     )
			__lowerCamelCase					:	str	       =				SwinaSRImageProcessor()
			# pixel_values = processor(image, return_tensors="pt").pixel_values

			__lowerCamelCase					:	str	       =				126 if 'Jpeg' in checkpoint_url else 256
			__lowerCamelCase					:	Union[str, Any]	       =				Compose(
			    [
			        Resize((image_size, image_size)     ),
			        ToTensor(),
			        Normalize(mean=[0.485, 0.456, 0.406]							,				std=[0.229, 0.224, 0.225]     ),
			    ]     )
			__lowerCamelCase					:	List[str]	       =				transforms(SCREAMING_SNAKE_CASE_     ).unsqueeze(0     )

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

			__lowerCamelCase					:	Optional[int]	       =				model(SCREAMING_SNAKE_CASE_     )

			# assert values
			if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
						__lowerCamelCase					:	Optional[int]	       =				torch.Size([1, 3, 512, 512]     )
						__lowerCamelCase					:	int	       =				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					:	Any	       =				torch.Size([1, 3, 1024, 1024]     )
						__lowerCamelCase					:	Optional[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					:	int	       =				torch.Size([1, 3, 1024, 1024]     )
						__lowerCamelCase					:	Dict	       =				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					:	str	       =				torch.Size([1, 3, 512, 512]     )
						__lowerCamelCase					:	str	       =				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					:	Tuple	       =				torch.Size([1, 3, 1024, 1024]     )
						__lowerCamelCase					:	Dict	       =				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]							,				SCREAMING_SNAKE_CASE_							,				atol=1E-3     )
			print('Looks ok!'     )

			__lowerCamelCase					:	str	       =				{
			    '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					:	Any	       =				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_				:    Optional[Any]										=     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_				:    Dict										=     parser.parse_args()
					convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= 'Speech2TextFeatureExtractor'
					__a						:						Optional[int]     		= 'Speech2TextTokenizer'
					def __init__(    self						,	__a						,	__a     ):
								super().__init__(__a						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False
					def __call__(    self						,	*__a						,	**__a     ):
								# For backward compatibility
								if self._in_target_context_manager:
											return self.current_processor(*__a						,	**__a     )

								if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
											__lowerCamelCase					:	str	       =				kwargs.pop('raw_speech'     )
								else:
											__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('audio'						,	__a     )
								__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('sampling_rate'						,	__a     )
								__lowerCamelCase					:	Optional[int]	       =				kwargs.pop('text'						,	__a     )
								if len(__a     ) > 0:
											__lowerCamelCase					:	Optional[int]	       =				args[0]
											__lowerCamelCase					:	str	       =				args[1:]

								if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'     )

								if audio is not None:
											__lowerCamelCase					:	Dict	       =				self.feature_extractor(__a						,	*__a						,	sampling_rate=__a						,	**__a     )
								if text is not None:
											__lowerCamelCase					:	Optional[int]	       =				self.tokenizer(__a						,	**__a     )

								if text is None:
											return inputs
								elif audio is None:
											return encodings
								else:
											__lowerCamelCase					:	Any	       =				encodings['input_ids']
											return inputs
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.batch_decode(*__a						,	**__a     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.decode(*__a						,	**__a     )





					@contextmanager
					def     snake_case_				(    self     ):
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'     )
								__lowerCamelCase					:	int	       =				True
								__lowerCamelCase					:	Union[str, Any]	       =				self.tokenizer
								yield
								__lowerCamelCase					:	Tuple	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False



