
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_    :			int    =	logging.get_logger(__name__)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=False  ,     lowerCamelCase=False    )	->						Any:
				UpperCamelCase_:				Tuple						=    """backbone.""" if is_semantic else """"""

				UpperCamelCase_:				str						=    []
				for i in range(config.num_hidden_layers    ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')    )
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')    )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')    )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')    )
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')    )
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')    )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')    )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')    )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')    )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')    )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F'''{prefix}cls_token''', """beit.embeddings.cls_token"""),
				        (F'''{prefix}patch_embed.proj.weight''', """beit.embeddings.patch_embeddings.projection.weight"""),
				        (F'''{prefix}patch_embed.proj.bias''', """beit.embeddings.patch_embeddings.projection.bias"""),
				        (F'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
				    ]    )

				if has_lm_head:
								# mask token + layernorm
								rename_keys.extend(
								    [
								        ("""mask_token""", """beit.embeddings.mask_token"""),
								        ("""norm.weight""", """layernorm.weight"""),
								        ("""norm.bias""", """layernorm.bias"""),
								    ]    )
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
								        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
								        ("""head.weight""", """classifier.weight"""),
								        ("""head.bias""", """classifier.bias"""),
								    ]    )

				return rename_keys







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=False  ,     lowerCamelCase=False    )	->						List[str]:
				for i in range(config.num_hidden_layers    ):
								UpperCamelCase_:				List[Any]						=    """backbone.""" if is_semantic else """"""
								# queries, keys and values
								UpperCamelCase_:				Any						=    state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''    )
								UpperCamelCase_:				List[str]						=    state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''    )
								UpperCamelCase_:				Optional[int]						=    state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''    )

								UpperCamelCase_:				Any						=    in_proj_weight[
								    : config.hidden_size, :
								]
								UpperCamelCase_:				Dict						=    q_bias
								UpperCamelCase_:				str						=    in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								UpperCamelCase_:				Any						=    in_proj_weight[
								    -config.hidden_size :, :
								]
								UpperCamelCase_:				Any						=    v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								UpperCamelCase_:				List[str]						=    state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''    )
								UpperCamelCase_:				Union[str, Any]						=    state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''    )

								UpperCamelCase_:				int						=    gamma_a
								UpperCamelCase_:				List[str]						=    gamma_a







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				UpperCamelCase_:				Optional[int]						=    dct.pop(__snake_case    )
				UpperCamelCase_:				Any						=    val







def 							A__       (    )	->						str:
				UpperCamelCase_:				List[str]						=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:				Any						=    Image.open(requests.get(__snake_case  ,     stream=__snake_case    ).raw    )
				return im







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=False    )	->						List[str]:
				UpperCamelCase_:				Any						=    False if """rvlcdip""" in checkpoint_url else True
				UpperCamelCase_:				Optional[Any]						=    BeitConfig(use_absolute_position_embeddings=__snake_case  ,     use_mask_token=__snake_case    )

				# size of the architecture
				if "large" in checkpoint_url or "dit-l" in checkpoint_url:
								UpperCamelCase_:				Tuple						=    10_24
								UpperCamelCase_:				Dict						=    40_96
								UpperCamelCase_:				str						=    24
								UpperCamelCase_:				Any						=    16

				# labels
				if "rvlcdip" in checkpoint_url:
								UpperCamelCase_:				Dict						=    16
								UpperCamelCase_:				Any						=    """huggingface/label-files"""
								UpperCamelCase_:				List[str]						=    """rvlcdip-id2label.json"""
								UpperCamelCase_:				List[str]						=    json.load(open(hf_hub_download(__snake_case  ,     __snake_case  ,     repo_type="""dataset"""    )  ,     """r"""    )    )
								UpperCamelCase_:				List[str]						=    {int(__snake_case    ): v for k, v in idalabel.items()}
								UpperCamelCase_:				int						=    idalabel
								UpperCamelCase_:				str						=    {v: k for k, v in idalabel.items()}

				# load state_dict of original model, remove and rename some keys
				UpperCamelCase_:				str						=    torch.hub.load_state_dict_from_url(__snake_case  ,     map_location="""cpu"""    )["""model"""]

				UpperCamelCase_:				List[Any]						=    create_rename_keys(__snake_case  ,     has_lm_head=__snake_case    )
				for src, dest in rename_keys:
								rename_key(__snake_case  ,     __snake_case  ,     __snake_case    )
				read_in_q_k_v(__snake_case  ,     __snake_case  ,     has_lm_head=__snake_case    )

				# load HuggingFace model
				UpperCamelCase_:				Tuple						=    BeitForMaskedImageModeling(__snake_case    ) if has_lm_head else BeitForImageClassification(__snake_case    )
				model.eval()
				model.load_state_dict(__snake_case    )

				# Check outputs on an image
				UpperCamelCase_:				Optional[Any]						=    BeitImageProcessor(
				    size=config.image_size  ,     resample=PILImageResampling.BILINEAR  ,     do_center_crop=__snake_case    )
				UpperCamelCase_:				List[Any]						=    prepare_img()

				UpperCamelCase_:				Union[str, Any]						=    image_processor(images=__snake_case  ,     return_tensors="""pt"""    )
				UpperCamelCase_:				Union[str, Any]						=    encoding["""pixel_values"""]

				UpperCamelCase_:				Tuple						=    model(__snake_case    )
				UpperCamelCase_:				Optional[Any]						=    outputs.logits

				# verify logits
				UpperCamelCase_:				int						=    [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
				assert logits.shape == torch.Size(__snake_case    ), "Shape of logits not as expected"

				Path(__snake_case    ).mkdir(exist_ok=__snake_case    )
				print(F'''Saving model 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 push_to_hub:
								if has_lm_head:
												UpperCamelCase_:				Optional[int]						=    """dit-base""" if """base""" in checkpoint_url else """dit-large"""
								else:
												UpperCamelCase_:				List[str]						=    """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
								image_processor.push_to_hub(
								    repo_path_or_name=Path(__snake_case  ,     __snake_case    )  ,     organization="""nielsr"""  ,     commit_message="""Add image processor"""  ,     use_temp_dir=__snake_case  ,     )
								model.push_to_hub(
								    repo_path_or_name=Path(__snake_case  ,     __snake_case    )  ,     organization="""nielsr"""  ,     commit_message="""Add model"""  ,     use_temp_dir=__snake_case  ,     )


if __name__ == "__main__":
							lowerCamelCase_    :			Any    =	argparse.ArgumentParser()

							parser.add_argument(
							    """--checkpoint_url""",
							    default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
							    type=str,
							    help="""URL to the original PyTorch checkpoint (.pth file).""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""",
							    action="""store_true""",
							)
							lowerCamelCase_    :			Union[str, Any]    =	parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)














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()






