import bsa
import requests





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				return {
				    "name": soup.ha.a.text,
				    "genre": soup.find('''span'''							,      class_='''genre'''							).text.strip(),
				    "rating": soup.strong.text,
				    "page_link": F"https://www.imdb.com{soup.a.get('href'							)}",
				}





def     UpperCAmelCase						(   lowercase = 5							):







				"""simple docstring"""



				__lowercase			      =							int(float(lowercase							)							)
				if num_movies < 1:
								return ()
				__lowercase			      =							(
				    '''https://www.imdb.com/search/title?title_type='''
				    F"feature&sort=num_votes,desc&count={num_movies}"
				)
				__lowercase			      =							bsa.BeautifulSoup(requests.get(lowercase							).content							,      '''html.parser'''							)
				return tuple(
				    get_movie_data_from_soup(lowercase							)
				    for movie in source.find_all('''div'''							,      class_='''lister-item mode-advanced'''							)							)


if __name__ == "__main__":
		import json

		__a  :					Tuple          =  int(input("""How many movies would you like to see? """))
		print(
		    """, """.join(
		        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
		    )
		)
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__a  :					int          =  logging.get_logger(__name__)

__a  :					Optional[Any]          =  {
    """microsoft/layoutlm-base-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"""
    ),
    """microsoft/layoutlm-large-uncased""": (
        """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"""
    ),
}




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							vocab_size
							__lowercase			      =							hidden_size
							__lowercase			      =							num_hidden_layers
							__lowercase			      =							num_attention_heads
							__lowercase			      =							hidden_act
							__lowercase			      =							intermediate_size
							__lowercase			      =							hidden_dropout_prob
							__lowercase			      =							attention_probs_dropout_prob
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							type_vocab_size
							__lowercase			      =							initializer_range
							__lowercase			      =							layer_norm_eps
							__lowercase			      =							position_embedding_type
							__lowercase			      =							use_cache
							__lowercase			      =							max_ad_position_embeddings




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
							        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
							        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
							    ]						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							super().generate_dummy_inputs(
							    lowerCAmelCase__				,       batch_size=lowerCAmelCase__				,       seq_length=lowerCAmelCase__				,       is_pair=lowerCAmelCase__				,       framework=lowerCAmelCase__						)

							# Generate a dummy bbox
							__lowercase			      =							[48, 84, 73, 1_28]

							if not framework == TensorType.PYTORCH:
											raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''						)

							if not is_torch_available():
											raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''						)
							import torch

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict