


import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "bart"
    UpperCAmelCase__						:		List[Any]      =  ["past_key_values"]
    UpperCAmelCase__						:		Optional[int]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50265					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=3					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   **A_					,   )  ->       Optional[int]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             num_labels=A_					,   pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase    							=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]    )

              if self.use_past:
                   __UpperCamelCase    							={0: 'batch'}
                   __UpperCamelCase    							={0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   __UpperCamelCase    							={0: 'batch', 1: 'decoder_sequence'}
                   __UpperCamelCase    							={0: 'batch', 1: 'decoder_sequence'}

              if self.use_past:
                   self.fill_with_past_key_values_(A_					,   direction='inputs'    )
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              __UpperCamelCase    							=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ]    )
              if self.use_past:
                   __UpperCamelCase							,      __UpperCamelCase    							=self.num_layers
                   for i in range(A_    ):
                        __UpperCamelCase    							={0: 'batch', 2: 'past_sequence + sequence'}
                        __UpperCamelCase    							={0: 'batch', 2: 'past_sequence + sequence'}
         else:
              __UpperCamelCase    							=OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                      ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                  ]    )

         return common_inputs






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase    							=super().outputs
         else:
              __UpperCamelCase    							=super(A_					,   self    ).outputs
              if self.use_past:
                   __UpperCamelCase							,      __UpperCamelCase    							=self.num_layers
                   for i in range(A_    ):
                        __UpperCamelCase    							={0: 'batch', 2: 'past_sequence + sequence'}
                        __UpperCamelCase    							={0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def _a					(			self					,   A_					,   A_ = -1					,   A_ = -1					,   A_ = False					,   A_ = None					,   )  ->       Mapping[str, Any]:
         __UpperCamelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A_					,   A_					,   A_					,   A_					,   A_    )

         # Generate decoder inputs
         __UpperCamelCase    							=seq_length if not self.use_past else 1
         __UpperCamelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A_					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							={f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         __UpperCamelCase    							=dict(**A_					,   **A_    )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'    )
              else:
                   import torch
              __UpperCamelCase							,      __UpperCamelCase    							=common_inputs['input_ids'].shape
              __UpperCamelCase    							=common_inputs['decoder_input_ids'].shape[1]
              __UpperCamelCase							,      __UpperCamelCase    							=self.num_attention_heads
              __UpperCamelCase    							=(
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              __UpperCamelCase    							=decoder_seq_length + 3
              __UpperCamelCase    							=(
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              __UpperCamelCase    							=torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(A_					,   A_    )]					,   dim=1    )

              __UpperCamelCase    							=[]
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              __UpperCamelCase							,      __UpperCamelCase    							=self.num_layers
              __UpperCamelCase    							=min(A_					,   A_    )
              __UpperCamelCase    							=max(A_					,   A_    ) - min_num_layers
              __UpperCamelCase    							='encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

              for _ in range(A_    ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(A_    ),
                           torch.zeros(A_    ),
                           torch.zeros(A_    ),
                           torch.zeros(A_    ),
                       )    )
              # TODO: test this.
              __UpperCamelCase    							=encoder_shape if remaining_side_name == 'encoder' else decoder_shape
              for _ in range(A_					,   A_    ):
                   common_inputs["past_key_values"].append((torch.zeros(A_    ), torch.zeros(A_    ))    )
         return common_inputs






    def _a					(			self					,   A_					,   A_ = -1					,   A_ = -1					,   A_ = False					,   A_ = None					,   )  ->       Mapping[str, Any]:
         __UpperCamelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             A_					,   A_					,   A_					,   A_					,   A_    )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'    )
              else:
                   import torch
              __UpperCamelCase							,      __UpperCamelCase    							=common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              __UpperCamelCase    							=seqlen + 2
              __UpperCamelCase							,      __UpperCamelCase    							=self.num_layers
              __UpperCamelCase							,      __UpperCamelCase    							=self.num_attention_heads
              __UpperCamelCase    							=(
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              __UpperCamelCase    							=common_inputs['attention_mask'].dtype
              __UpperCamelCase    							=torch.cat(
                  [common_inputs['attention_mask'], torch.ones(A_					,   A_					,   dtype=A_    )]					,   dim=1    )
              __UpperCamelCase    							=[
                  (torch.zeros(A_    ), torch.zeros(A_    )) for _ in range(A_    )
              ]
         return common_inputs






    def _a					(			self					,   A_					,   A_ = -1					,   A_ = -1					,   A_ = False					,   A_ = None					,   )  ->       Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         __UpperCamelCase    							=compute_effective_axis_dimension(
             A_					,   fixed_dimension=OnnxConfig.default_fixed_batch					,   num_token_to_add=0    )

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         __UpperCamelCase    							=tokenizer.num_special_tokens_to_add(A_    )
         __UpperCamelCase    							=compute_effective_axis_dimension(
             A_					,   fixed_dimension=OnnxConfig.default_fixed_sequence					,   num_token_to_add=A_    )

         # Generate dummy inputs according to compute batch and sequence
         __UpperCamelCase    							=[' '.join([tokenizer.unk_token]    ) * seq_length] * batch_size
         __UpperCamelCase    							=dict(tokenizer(A_					,   return_tensors=A_    )    )
         return common_inputs






    def _a					(			self					,   A_					,   A_ = -1					,   A_ = -1					,   A_ = False					,   A_ = None					,   )  ->       Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase    							=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  A_					,   batch_size=A_					,   seq_length=A_					,   is_pair=A_					,   framework=A_    )

         elif self.task == "causal-lm":
              __UpperCamelCase    							=self._generate_dummy_inputs_for_causal_lm(
                  A_					,   batch_size=A_					,   seq_length=A_					,   is_pair=A_					,   framework=A_    )
         else:
              __UpperCamelCase    							=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  A_					,   batch_size=A_					,   seq_length=A_					,   is_pair=A_					,   framework=A_    )

         return common_inputs






    def _a					(			self					,   A_					,   A_					,   A_					,   A_    )  ->       str:
         if self.task in ["default", "seq2seq-lm"]:
              __UpperCamelCase    							=super()._flatten_past_key_values_(A_					,   A_					,   A_					,   A_    )
         else:
              __UpperCamelCase    							=super(A_					,   self    )._flatten_past_key_values_(
                  A_					,   A_					,   A_					,   A_    )









from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str = ""				):
     __UpperCamelCase    							=url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
     __UpperCamelCase    							=BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__				).text			,     'html.parser'				)
     __UpperCamelCase    							=soup.find_all('td'			,     attrs='titleColumn'				)
     __UpperCamelCase    							=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"				):
     __UpperCamelCase    							=get_imdb_top_aaa_movies()
     with open(SCREAMING_SNAKE_CASE__			,     'w'			,     newline=''				) as out_file:
          __UpperCamelCase    							=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()





