





from typing import Mapping

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


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

_A					:				str				=			{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class 					__snake_case	(  __a    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	'''umt5'''
    lowerCamelCase__    :		List[Any]				=	['''past_key_values''']



    def __init__(       self   ,				A_=25_01_12   ,				A_=5_12   ,				A_=64   ,				A_=10_24   ,				A_=8   ,				A_=None   ,				A_=6   ,				A_=32   ,				A_=1_28   ,				A_=0.1   ,				A_=1E-6   ,				A_=1.0   ,				A_="gated-gelu"   ,				A_=True   ,				A_=True   ,				A_="T5Tokenizer"   ,				A_=True   ,				A_=0   ,				A_=1   ,				A_=0   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(
            is_encoder_decoder=_lowercase   ,				tokenizer_class=_lowercase   ,				tie_word_embeddings=_lowercase   ,				pad_token_id=_lowercase   ,				eos_token_id=_lowercase   ,				decoder_start_token_id=_lowercase   ,				**_lowercase   ,				)
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			d_model
        SCREAMING_SNAKE_CASE__											=			d_kv
        SCREAMING_SNAKE_CASE__											=			d_ff
        SCREAMING_SNAKE_CASE__											=			num_layers
        SCREAMING_SNAKE_CASE__											=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        SCREAMING_SNAKE_CASE__											=			num_heads
        SCREAMING_SNAKE_CASE__											=			relative_attention_num_buckets
        SCREAMING_SNAKE_CASE__											=			relative_attention_max_distance
        SCREAMING_SNAKE_CASE__											=			dropout_rate
        SCREAMING_SNAKE_CASE__											=			layer_norm_epsilon
        SCREAMING_SNAKE_CASE__											=			initializer_factor
        SCREAMING_SNAKE_CASE__											=			feed_forward_proj
        SCREAMING_SNAKE_CASE__											=			use_cache

        SCREAMING_SNAKE_CASE__											=			self.feed_forward_proj.split('''-'''     )
        SCREAMING_SNAKE_CASE__											=			act_info[-1]
        SCREAMING_SNAKE_CASE__											=			act_info[0] == '''gated'''

        if len(_lowercase     ) > 1 and act_info[0] != "gated" or len(_lowercase     ) > 2:
            raise ValueError(
                f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                '''\'gated-gelu\' or \'relu\''''     )

        if feed_forward_proj == "gated-gelu":
            SCREAMING_SNAKE_CASE__											=			'''gelu_new'''



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.d_model



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.num_heads




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.num_layers





class 					__snake_case	(  __a    ):


    '''simple docstring'''







    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{
            '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
            '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
        }
        if self.use_past:
            SCREAMING_SNAKE_CASE__											=			'''past_encoder_sequence + sequence'''
            SCREAMING_SNAKE_CASE__											=			{0: '''batch'''}
            SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
            SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''decoder_sequence'''}
            SCREAMING_SNAKE_CASE__											=			{0: '''batch''', 1: '''decoder_sequence'''}

        if self.use_past:
            self.fill_with_past_key_values_(_lowercase   ,				direction='''inputs'''     )

        return common_inputs



    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 13




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 5E-4

from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







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




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







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


if __name__ == "__main__":
  write_movies()