






import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       SwinConfig()
     __lowerCAmelCase             =       swin_name.split("_"							)

     __lowerCAmelCase             =       name_split[1]
     __lowerCAmelCase             =       int(name_split[4]							)
     __lowerCAmelCase             =       int(name_split[3][-1]							)

     if model_size == "tiny":
          __lowerCAmelCase             =       96
          __lowerCAmelCase             =       (2, 2, 6, 2)
          __lowerCAmelCase             =       (3, 6, 12, 24)
     elif model_size == "small":
          __lowerCAmelCase             =       96
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (3, 6, 12, 24)
     elif model_size == "base":
          __lowerCAmelCase             =       128
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (4, 8, 16, 32)
     else:
          __lowerCAmelCase             =       192
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (6, 12, 24, 48)

     if "in22k" in swin_name:
          __lowerCAmelCase             =       2_1841
     else:
          __lowerCAmelCase             =       1000
          __lowerCAmelCase             =       "huggingface/label-files"
          __lowerCAmelCase             =       "imagenet-1k-id2label.json"
          __lowerCAmelCase             =       json.load(open(hf_hub_download(__snake_case       ,			__snake_case       ,			repo_type="dataset"							)       ,			"r"							)							)
          __lowerCAmelCase             =       {int(__snake_case							): v for k, v in idalabel.items()}
          __lowerCAmelCase             =       idalabel
          __lowerCAmelCase             =       {v: k for k, v in idalabel.items()}

     __lowerCAmelCase             =       img_size
     __lowerCAmelCase             =       num_classes
     __lowerCAmelCase             =       embed_dim
     __lowerCAmelCase             =       depths
     __lowerCAmelCase             =       num_heads
     __lowerCAmelCase             =       window_size

     return config





def    __lowerCAmelCase   (    __snake_case							):
     if "patch_embed.proj" 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.norm"							)
     if "layers" in name:
          __lowerCAmelCase             =       "encoder." + name
     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 name == "norm.weight":
          __lowerCAmelCase             =       "layernorm.weight"
     if name == "norm.bias":
          __lowerCAmelCase             =       "layernorm.bias"

     if "head" in name:
          __lowerCAmelCase             =       name.replace("head"       ,			"classifier"							)
     else:
          __lowerCAmelCase             =       "swin." + name

     return name





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     for key in orig_state_dict.copy().keys():
          __lowerCAmelCase             =       orig_state_dict.pop(__snake_case							)

          if "mask" in key:
               continue
          elif "qkv" in key:
               __lowerCAmelCase             =       key.split("."							)
               __lowerCAmelCase             =       int(key_split[1]							)
               __lowerCAmelCase             =       int(key_split[3]							)
               __lowerCAmelCase             =       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               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:
                    ]
          else:
               __lowerCAmelCase             =       val

     return orig_state_dict





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       timm.create_model(__snake_case       ,			pretrained=__snake_case							)
     timm_model.eval()

     __lowerCAmelCase             =       get_swin_config(__snake_case							)
     __lowerCAmelCase             =       SwinForImageClassification(__snake_case							)
     model.eval()

     __lowerCAmelCase             =       convert_state_dict(timm_model.state_dict()       ,			__snake_case							)
     model.load_state_dict(__snake_case							)

     __lowerCAmelCase             =       "http://images.cocodataset.org/val2017/000000039769.jpg"

     __lowerCAmelCase             =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"       ,			"-"							)							)							)
     __lowerCAmelCase             =       Image.open(requests.get(__snake_case       ,			stream=__snake_case							).raw							)
     __lowerCAmelCase             =       image_processor(images=__snake_case       ,			return_tensors="pt"							)

     __lowerCAmelCase             =       timm_model(inputs["pixel_values"]							)
     __lowerCAmelCase             =       model(**__snake_case							).logits

     assert torch.allclose(__snake_case       ,			__snake_case       ,			atol=1E-3							)

     print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
     model.save_pretrained(__snake_case							)

     print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
     image_processor.save_pretrained(__snake_case							)


if __name__ == "__main__":
 lowerCamelCase      :       Any    =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--swin_name''',
     default='''swin_tiny_patch4_window7_224''',
     type=str,
     help='''Name of the Swin timm model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 lowerCamelCase      :       List[str]    =						parser.parse_args()
 convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)














from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def    __lowerCAmelCase   (    __snake_case = ""							):
     __lowerCAmelCase             =       url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
     __lowerCAmelCase             =       BeautifulSoup(requests.get(__snake_case							).text       ,			"html.parser"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			attrs="titleColumn"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			class_="ratingColumn imdbRating"							)
     return {
         title.a.text: float(rating.strong.text							)
         for title, rating in zip(__snake_case       ,			__snake_case							)
     }





def    __lowerCAmelCase   (    __snake_case = "IMDb_Top_250_Movies.csv"							):
     __lowerCAmelCase             =       get_imdb_top_aaa_movies()
     with open(__snake_case       ,			"w"       ,			newline=""							) as out_file:
          __lowerCAmelCase             =       csv.writer(__snake_case							)
          writer.writerow(["Movie title", "IMDb rating"]							)
          for title, rating in movies.items():
               writer.writerow([title, rating]							)


if __name__ == "__main__":
 write_movies()






