




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        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
						_UpperCAmelCase      =			SwinaSRConfig()

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

						return config




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

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

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

						return name




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				List[Any]:
						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase      =			orig_state_dict.pop(snake_case       )

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

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

						return orig_state_dict




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			get_config(snake_case       )
						_UpperCAmelCase      =			SwinaSRForImageSuperResolution(snake_case       )
						model.eval()

						_UpperCAmelCase      =			torch.hub.load_state_dict_from_url(snake_case			,     map_location="""cpu"""       )
						_UpperCAmelCase      =			convert_state_dict(snake_case			,     snake_case       )
						_UpperCAmelCase    ,					_UpperCAmelCase      =			model.load_state_dict(snake_case			,     strict=snake_case       )

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

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

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

						_UpperCAmelCase      =			model(snake_case       )

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

						_UpperCAmelCase      =			{
						    """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"""
						    ),
						}
						_UpperCAmelCase      =			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(snake_case       )
												print(f"Saving image processor to {pytorch_dump_folder_path}"       )
												processor.save_pretrained(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													=			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 csv

import tweepy

# Twitter API credentials
a													=			""
a													=			""
a													=			""
a													=			""




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				None:
						# authorize twitter, initialize tweepy
						_UpperCAmelCase      =			tweepy.OAuthHandler(snake_case			,     snake_case       )
						auth.set_access_token(snake_case			,     snake_case       )
						_UpperCAmelCase      =			tweepy.API(snake_case       )

						# initialize a list to hold all the tweepy Tweets
						_UpperCAmelCase      =			[]

						# make initial request for most recent tweets (200 is the maximum allowed count)
						_UpperCAmelCase      =			api.user_timeline(screen_name=snake_case			,     count=2_0_0       )

						# save most recent tweets
						alltweets.extend(snake_case       )

						# save the id of the oldest tweet less one
						_UpperCAmelCase      =			alltweets[-1].id - 1

						# keep grabbing tweets until there are no tweets left to grab
						while len(snake_case       ) > 0:
												print(f"getting tweets before {oldest}"       )

												# all subsequent requests use the max_id param to prevent duplicates
												_UpperCAmelCase      =			api.user_timeline(
												    screen_name=snake_case			,     count=2_0_0			,     max_id=snake_case       )

												# save most recent tweets
												alltweets.extend(snake_case       )

												# update the id of the oldest tweet less one
												_UpperCAmelCase      =			alltweets[-1].id - 1

												print(f"...{len(snake_case       )} tweets downloaded so far"       )

						# transform the tweepy tweets into a 2D array that will populate the csv
						_UpperCAmelCase      =			[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

						# write the csv
						with open(f"new_{screen_name}_tweets.csv"			,     """w"""       ) as f:
												_UpperCAmelCase      =			csv.writer(snake_case       )
												writer.writerow(["""id""", """created_at""", """text"""]       )
												writer.writerows(snake_case       )


if __name__ == "__main__":
				# pass in the username of the account you want to download
				get_all_tweets("FirePing32")