
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :		Optional[int]       			=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :		Any       			=							{
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}






class        __lowercase		(       a_							):
					__magic_name__    :				Tuple  						=						'''pix2struct_text_model'''
					__magic_name__    :				Union[str, Any]  						=						['''past_key_values''']
					__magic_name__    :				Dict  						=						{
					    '''hidden_size''': '''hidden_size''',
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self							,							a__=5_0_2_4_4							,							a__=7_6_8							,							a__=6_4							,							a__=2_0_4_8							,							a__=1_2							,							a__=1_2							,							a__=3_2							,							a__=1_2_8							,							a__=0.1							,							a__=1E-6							,							a__=1.0							,							a__="gelu_new"							,							a__=0							,							a__=False							,							a__=0							,							a__=1							,							a__=False							,							a__=True							,							**a__							,							)		->				Optional[int]:





									'''simple docstring'''
									A_													=	vocab_size
									A_													=	hidden_size
									A_													=	d_kv
									A_													=	d_ff
									A_													=	num_layers
									A_													=	num_heads
									A_													=	relative_attention_num_buckets
									A_													=	relative_attention_max_distance
									A_													=	dropout_rate
									A_													=	layer_norm_epsilon
									A_													=	initializer_factor
									A_													=	use_cache

									A_													=	eos_token_id
									A_													=	decoder_start_token_id

									# for backwards compatibility
									A_													=	dense_act_fn

									super().__init__(
									    pad_token_id=lowerCAmelCase_							,							eos_token_id=lowerCAmelCase_							,							decoder_start_token_id=lowerCAmelCase_							,							tie_word_embeddings=lowerCAmelCase_							,							is_decoder=lowerCAmelCase_							,							**lowerCAmelCase_							,							)


					@classmethod
					def 							lowerCAmelCase_     (						cls							,							a__							,							**a__    )		->				"PretrainedConfig":





									'''simple docstring'''
									cls._set_token_in_kwargs(lowerCAmelCase_    )

									A_						,       A_													=	cls.get_config_dict(lowerCAmelCase_							,							**lowerCAmelCase_    )

									# get the text config dict if we are loading from Pix2StructConfig
									if config_dict.get('''model_type'''    ) == "pix2struct":
													A_													=	config_dict['''text_config''']

									if "model_type" in config_dict and hasattr(cls							,							'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
													    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."    )

									return cls.from_dict(lowerCAmelCase_							,							**lowerCAmelCase_    )






class        __lowercase		(       a_							):
					__magic_name__    :				List[str]  						=						'''pix2struct_vision_model'''


					def __init__(						self							,							a__=7_6_8							,							a__=7_6_8							,							a__=2_0_4_8							,							a__=6_4							,							a__=1_2							,							a__=1_2							,							a__="gelu_new"							,							a__=1E-6							,							a__=0.0							,							a__=0.0							,							a__=1E-10							,							a__=1.0							,							a__=4_0_9_6							,							a__=3_2							,							a__=1_2_8							,							**a__							,							)		->				Union[str, Any]:





									'''simple docstring'''
									super().__init__(**lowerCAmelCase_    )

									A_													=	hidden_size
									A_													=	patch_embed_hidden_size
									A_													=	d_ff
									A_													=	dropout_rate
									A_													=	num_hidden_layers
									A_													=	num_attention_heads
									A_													=	initializer_range
									A_													=	initializer_factor
									A_													=	attention_dropout
									A_													=	layer_norm_eps
									A_													=	dense_act_fn
									A_													=	seq_len
									A_													=	relative_attention_num_buckets
									A_													=	relative_attention_max_distance
									A_													=	d_kv


					@classmethod
					def 							lowerCAmelCase_     (						cls							,							a__							,							**a__    )		->				"PretrainedConfig":





									'''simple docstring'''
									cls._set_token_in_kwargs(lowerCAmelCase_    )

									A_						,       A_													=	cls.get_config_dict(lowerCAmelCase_							,							**lowerCAmelCase_    )

									# get the vision config dict if we are loading from Pix2StructConfig
									if config_dict.get('''model_type'''    ) == "pix2struct":
													A_													=	config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls							,							'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
													    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."    )

									return cls.from_dict(lowerCAmelCase_							,							**lowerCAmelCase_    )






class        __lowercase		(       a_							):
					__magic_name__    :				int  						=						'''pix2struct'''
					__magic_name__    :				List[str]  						=						True


					def __init__(						self							,							a__=None							,							a__=None							,							a__=1.0							,							a__=0.02							,							a__=False							,							a__=False							,							a__=True							,							**a__							,							)		->				str:





									'''simple docstring'''
									super().__init__(tie_word_embeddings=lowerCAmelCase_							,							is_encoder_decoder=lowerCAmelCase_							,							**lowerCAmelCase_    )

									if text_config is None:
													A_													=	{}
													logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''    )

									if vision_config is None:
													A_													=	{}
													logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''    )

									A_													=	PixaStructTextConfig(**lowerCAmelCase_    )
									A_													=	PixaStructVisionConfig(**lowerCAmelCase_    )

									A_													=	self.text_config.decoder_start_token_id
									A_													=	self.text_config.pad_token_id
									A_													=	self.text_config.eos_token_id

									A_													=	initializer_factor
									A_													=	initializer_range

									A_													=	self.initializer_range
									A_													=	self.initializer_range

									A_													=	is_vqa


					@classmethod
					def 							lowerCAmelCase_     (						cls							,							a__							,							a__							,							**a__    )		->				Tuple:





									'''simple docstring'''
									return cls(text_config=text_config.to_dict()							,							vision_config=vision_config.to_dict()							,							**lowerCAmelCase_    )


					def 							lowerCAmelCase_     (						self    )		->				Optional[Any]:





									'''simple docstring'''
									A_													=	copy.deepcopy(self.__dict__    )
									A_													=	self.text_config.to_dict()
									A_													=	self.vision_config.to_dict()
									A_													=	self.__class__.model_type
									return output



'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "IMDb_Top_250_Movies.csv" ):






			"""simple docstring"""




			__A=	get_imdb_top_aaa_movies()
			with open(_SCREAMING_SNAKE_CASE,'w',newline='' ) as out_file:
						__A=	csv.writer(_SCREAMING_SNAKE_CASE )
						writer.writerow(['Movie title', 'IMDb rating'] )
						for title, rating in movies.items():
									writer.writerow([title, rating] )


if __name__ == "__main__":
					write_movies()



