






'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A	:			List[Any]         =logging.get_logger(__name__)

_A	:			Optional[int]         =torch.device('''cpu''')





def  SCREAMING_SNAKE_CASE_       ()      ->       int:
							lowerCamelCase__					:	str         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__					:	str         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )
							return im





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Optional[int]:
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]      )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]      )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]      )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Any:
							lowerCamelCase__					:	List[str]         =       dct.pop(UpperCamelCase      )
							lowerCamelCase__					:	Union[str, Any]         =       val





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Any         =       []
							for k in state_dict.keys():
														lowerCamelCase__					:	List[Any]         =       k
														if ".pwconv" in k:
																					lowerCamelCase__					:	List[str]         =       k_new.replace(""".pwconv"""				,			""".point_wise_conv"""      )
														if ".dwconv" in k:
																					lowerCamelCase__					:	str         =       k_new.replace(""".dwconv"""				,			""".depth_wise_conv"""      )
														if ".Proj." in k:
																					lowerCamelCase__					:	str         =       k_new.replace(""".Proj."""				,			""".proj."""      )
														if "patch_embed" in k_new:
																					lowerCamelCase__					:	Any         =       k_new.replace("""patch_embed"""				,			"""swiftformer.patch_embed.patch_embedding"""      )
														if "network" in k_new:
																					lowerCamelCase__					:	Union[str, Any]         =       k_new.split("""."""      )
																					if ls[2].isdigit():
																												lowerCamelCase__					:	Optional[Any]         =       """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]      )
																					else:
																												lowerCamelCase__					:	Any         =       k_new.replace("""network"""				,			"""swiftformer.encoder.network"""      )
														rename_keys.append((k, k_new)      )
							return rename_keys





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Tuple:
							lowerCamelCase__					:	str         =       SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							lowerCamelCase__					:	Optional[int]         =       1000
							lowerCamelCase__					:	int         =       """huggingface/label-files"""
							lowerCamelCase__					:	Any         =       """imagenet-1k-id2label.json"""
							lowerCamelCase__					:	Optional[Any]         =       json.load(open(hf_hub_download(UpperCamelCase				,			UpperCamelCase				,			repo_type="""dataset"""      )				,			"""r"""      )      )
							lowerCamelCase__					:	Optional[Any]         =       {int(UpperCamelCase      ): v for k, v in idalabel.items()}
							lowerCamelCase__					:	Optional[Any]         =       idalabel
							lowerCamelCase__					:	Dict         =       {v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														lowerCamelCase__					:	str         =       [3, 3, 6, 4]
														lowerCamelCase__					:	Optional[Any]         =       [48, 56, 112, 220]

							elif swiftformer_name == "swiftformer_s":
														lowerCamelCase__					:	List[Any]         =       [3, 3, 9, 6]
														lowerCamelCase__					:	Optional[int]         =       [48, 64, 168, 224]

							elif swiftformer_name == "swiftformer_l1":
														lowerCamelCase__					:	int         =       [4, 3, 10, 5]
														lowerCamelCase__					:	Optional[int]         =       [48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														lowerCamelCase__					:	Optional[int]         =       [4, 4, 12, 6]
														lowerCamelCase__					:	str         =       [64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https"""      ):
																					lowerCamelCase__					:	Tuple         =       torch.hub.load_state_dict_from_url(UpperCamelCase				,			map_location="""cpu"""				,			check_hash=UpperCamelCase      )
														else:
																					lowerCamelCase__					:	List[str]         =       torch.load(UpperCamelCase				,			map_location="""cpu"""      )
							lowerCamelCase__					:	str         =       checkpoint

							lowerCamelCase__					:	List[str]         =       create_rename_keys(UpperCamelCase      )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )

							# load HuggingFace model
							lowerCamelCase__					:	List[str]         =       SwiftFormerForImageClassification(UpperCamelCase      ).eval()
							hf_model.load_state_dict(UpperCamelCase      )

							# prepare test inputs
							lowerCamelCase__					:	str         =       prepare_img()
							lowerCamelCase__					:	Optional[int]         =       ViTImageProcessor.from_pretrained("""preprocessor_config"""      )
							lowerCamelCase__					:	Union[str, Any]         =       processor(images=UpperCamelCase				,			return_tensors="""pt"""      )

							# compare outputs from both models
							lowerCamelCase__					:	Tuple         =       get_expected_output(UpperCamelCase      )
							lowerCamelCase__					:	str         =       hf_model(inputs["""pixel_values"""]      ).logits

							assert hf_logits.shape == torch.Size([1, 1000]      )
							assert torch.allclose(hf_logits[0, 0:5]				,			UpperCamelCase				,			atol=1E-3      )

							Path(UpperCamelCase      ).mkdir(exist_ok=UpperCamelCase      )
							print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''      )
							hf_model.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Union[str, Any]         =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--swiftformer_name''',
			    default='''swiftformer_xs''',
			    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
			    type=str,
			    help='''Name of the SwiftFormer model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default='''./converted_outputs/''',
			    type=str,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

			_A	:			int         =parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)









'''simple docstring'''







from __future__ import annotations

import requests

_A	:			str         =set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase = 1				,			UpperCamelCase = "new"				,			UpperCamelCase = None      )      ->       dict:
							lowerCamelCase__					:	Any         =       wanted_data or []
							if invalid_search_terms := ", ".join(sorted(set(UpperCamelCase      ) - valid_terms      )      ):
														lowerCamelCase__					:	str         =       f'''Invalid search term: {invalid_search_terms}'''
														raise ValueError(UpperCamelCase      )
							lowerCamelCase__					:	str         =       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

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

							lowerCamelCase__					:	Dict         =       {}
							for id_ in range(UpperCamelCase      ):
														lowerCamelCase__					:	Union[str, Any]         =       {
														    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''']))

