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  snake_case						(__lowercase						)	->	int:

						'''simple docstring'''



						_snake_case						:					Tuple    =   SwinaSRConfig()

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

						return config
def  snake_case						(__lowercase ,      __lowercase						)	->	List[Any]:

						'''simple docstring'''



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

						if name == "norm.weight":
												_snake_case						:					Optional[int]    =   "layernorm.weight"
						if name == "norm.bias":
												_snake_case						:					List[str]    =   "layernorm.bias"

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

						return name
def  snake_case						(__lowercase ,      __lowercase						)	->	List[Any]:

						'''simple docstring'''



						for key in orig_state_dict.copy().keys():
												_snake_case						:					Union[str, Any]    =   orig_state_dict.pop(__lowercase						)

												if "qkv" in key:
																		_snake_case						:					List[str]    =   key.split("."						)
																		_snake_case						:					int    =   int(key_split[1]						)
																		_snake_case						:					Optional[Any]    =   int(key_split[4]						)
																		_snake_case						:					List[Any]    =   config.embed_dim

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

						return orig_state_dict
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					Tuple    =   get_config(__lowercase						)
						_snake_case						:					str    =   SwinaSRForImageSuperResolution(__lowercase						)
						model.eval()

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

						_snake_case						:					Tuple    =   126 if "Jpeg" in checkpoint_url else 256
						_snake_case						:					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]						),
						    ]						)
						_snake_case						:					int    =   transforms(__lowercase						).unsqueeze(0						)

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

						_snake_case						:					Union[str, Any]    =   model(__lowercase						)

						# assert values
						if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
												_snake_case						:					Tuple    =   torch.Size([1, 3, 512, 512]						)
												_snake_case						:					List[Any]    =   torch.tensor(
												    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]						)
						elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												_snake_case						:					Optional[Any]    =   torch.Size([1, 3, 1_024, 1_024]						)
												_snake_case						:					int    =   torch.tensor(
												    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]						)
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												# TODO values didn't match exactly here
												_snake_case						:					List[str]    =   torch.Size([1, 3, 1_024, 1_024]						)
												_snake_case						:					Any    =   torch.tensor(
												    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]						)
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												_snake_case						:					Optional[int]    =   torch.Size([1, 3, 512, 512]						)
												_snake_case						:					Any    =   torch.tensor(
												    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]						)
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												_snake_case						:					Union[str, Any]    =   torch.Size([1, 3, 1_024, 1_024]						)
												_snake_case						:					Tuple    =   torch.tensor(
												    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]						)

						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!"						)

						_snake_case						:					Optional[int]    =   {
						    "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						:					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__":
				__SCREAMING_SNAKE_CASE						:   List[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.')

				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			parser.parse_args()
				convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
from __future__ import annotations

import requests

__SCREAMING_SNAKE_CASE						:   Tuple         =			set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)
def  snake_case						(__lowercase ,      __lowercase = 1 ,      __lowercase = "new" ,      __lowercase = None						)	->	dict:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(__lowercase						) - valid_terms						)						):
												_snake_case						:					List[str]    =   F"""Invalid search term: {invalid_search_terms}"""
												raise ValueError(__lowercase						)
						_snake_case						:					Any    =   requests.get(
						    F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}""" ,      headers={"User-agent": "A random string"} ,      )
						if response.status_code == 429:
												raise requests.HTTPError

						_snake_case						:					Optional[int]    =   response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(__lowercase						)}

						_snake_case						:					Union[str, Any]    =   {}
						for id_ in range(__lowercase						):
												_snake_case						:					Dict    =   {
												    item: data["data"]["children"][id_]["data"][item] for item in wanted_data
												}
						return data_dict


if __name__ == "__main__":
				# If you get Error 429, that means you are rate limited.Try after some time
				print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))