


'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, TensorType

__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}
class      lowercase			(							lowercase__      ):

		"""simple docstring"""

		_a     :   str									=       'imagegpt'
		_a     :   Dict									=       ['past_key_values']
		_a     :   Dict									=       {
		    'hidden_size': 'n_embd',
		    'max_position_embeddings': 'n_positions',
		    'num_attention_heads': 'n_head',
		    'num_hidden_layers': 'n_layer',
		}




		def __init__(	self					,     UpperCamelCase_=512 + 1					,     UpperCamelCase_=32 * 32					,     UpperCamelCase_=512					,     UpperCamelCase_=24					,     UpperCamelCase_=8					,     UpperCamelCase_=None					,     UpperCamelCase_="quick_gelu"					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.1					,     UpperCamelCase_=0.1					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=0.02					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=False					,     UpperCamelCase_=False					,     UpperCamelCase_=False					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				vocab_size
							UpperCamelCase__     :Optional[Any]							      =				n_positions
							UpperCamelCase__     :List[str]							      =				n_embd
							UpperCamelCase__     :List[str]							      =				n_layer
							UpperCamelCase__     :Dict							      =				n_head
							UpperCamelCase__     :List[str]							      =				n_inner
							UpperCamelCase__     :List[str]							      =				activation_function
							UpperCamelCase__     :Optional[int]							      =				resid_pdrop
							UpperCamelCase__     :Tuple							      =				embd_pdrop
							UpperCamelCase__     :Dict							      =				attn_pdrop
							UpperCamelCase__     :List[Any]							      =				layer_norm_epsilon
							UpperCamelCase__     :Optional[Any]							      =				initializer_range
							UpperCamelCase__     :Tuple							      =				scale_attn_weights
							UpperCamelCase__     :Optional[int]							      =				use_cache
							UpperCamelCase__     :Union[str, Any]							      =				scale_attn_by_inverse_layer_idx
							UpperCamelCase__     :int							      =				reorder_and_upcast_attn
							UpperCamelCase__     :Dict							      =				tie_word_embeddings

							super().__init__(tie_word_embeddings=UpperCamelCase_					,     **UpperCamelCase_							)



class      lowercase			(							lowercase__      ):

		"""simple docstring"""

		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = -1					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     UpperCamelCase_ = 3					,     UpperCamelCase_ = 32					,     UpperCamelCase_ = 32					,     ):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self._generate_dummy_images(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				dict(preprocessor(images=UpperCamelCase_					,     return_tensors=UpperCamelCase_							)							)

							return inputs



'''simple docstring'''
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()