"""simple docstring"""
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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "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 					lowerCAmelCase_      (     __snake_case   ):



    """simple docstring"""
    _lowerCAmelCase       :      Optional[int]     					=  "bart"
    _lowerCAmelCase       :      Any     					=  ["past_key_values"]
    _lowerCAmelCase       :      str     					=  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

    def __init__(			self						,      lowerCAmelCase=5_02_65						,      lowerCAmelCase=10_24						,      lowerCAmelCase=12						,      lowerCAmelCase=40_96						,      lowerCAmelCase=16						,      lowerCAmelCase=12						,      lowerCAmelCase=40_96						,      lowerCAmelCase=16						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=10_24						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.02						,      lowerCAmelCase=0.0						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase=3						,      lowerCAmelCase=1						,      lowerCAmelCase=0						,      lowerCAmelCase=2						,      lowerCAmelCase=True						,      lowerCAmelCase=2						,      lowerCAmelCase=2						,      **lowerCAmelCase						,      ):






          """simple docstring"""

          snake_case        =     vocab_size
          snake_case        =     max_position_embeddings
          snake_case        =     d_model
          snake_case        =     encoder_ffn_dim
          snake_case        =     encoder_layers
          snake_case        =     encoder_attention_heads
          snake_case        =     decoder_ffn_dim
          snake_case        =     decoder_layers
          snake_case        =     decoder_attention_heads
          snake_case        =     dropout
          snake_case        =     attention_dropout
          snake_case        =     activation_dropout
          snake_case        =     activation_function
          snake_case        =     init_std
          snake_case        =     encoder_layerdrop
          snake_case        =     decoder_layerdrop
          snake_case        =     classifier_dropout
          snake_case        =     use_cache
          snake_case        =     encoder_layers
          snake_case        =     scale_embedding  # scale factor will be sqrt(d_model) if True

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

          # ensure backward compatibility for BART CNN models
          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'						,      lowerCamelCase_      ):
                snake_case        =     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 					lowerCAmelCase_      (     __snake_case   ):



    """simple docstring"""

    @property
    def 						snake_case	(			self      ):






          """simple docstring"""

          if self.task in ["default", "seq2seq-lm"]:
                snake_case        =     OrderedDict(
                    [
                        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ]      )

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

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






          """simple docstring"""

          if self.task in ["default", "seq2seq-lm"]:
                snake_case        =     super().outputs
          else:
                snake_case        =     super(lowerCamelCase_						,      self      ).outputs
                if self.use_past:
                      snake_case        =     self.num_layers
                      for i in range(lowerCamelCase_      ):
                            snake_case        =     {0: """batch""", 2: """past_sequence + sequence"""}
                            snake_case        =     {0: """batch""", 2: """past_sequence + sequence"""}
          return common_outputs

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






          """simple docstring"""

          snake_case        =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_      )

          # Generate decoder inputs
          snake_case        =     seq_length if not self.use_past else 1
          snake_case        =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_      )
          snake_case        =     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
          snake_case        =     dict(**lowerCamelCase_						,      **lowerCamelCase_      )

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

                snake_case        =     torch.cat(
                    [common_inputs['decoder_attention_mask'], torch.ones(lowerCamelCase_						,      lowerCamelCase_      )]						,      dim=1      )

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

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

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






          """simple docstring"""

          snake_case        =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_      )

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

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

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






          """simple docstring"""

          snake_case        =     compute_effective_axis_dimension(
              lowerCamelCase_						,      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
          snake_case        =     tokenizer.num_special_tokens_to_add(lowerCamelCase_      )
          snake_case        =     compute_effective_axis_dimension(
              lowerCamelCase_						,      fixed_dimension=OnnxConfig.default_fixed_sequence						,      num_token_to_add=lowerCamelCase_      )

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

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = -1						,      lowerCAmelCase = -1						,      lowerCAmelCase = False						,      lowerCAmelCase = None						,      ):






          """simple docstring"""

          if self.task in ["default", "seq2seq-lm"]:
                snake_case        =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                    lowerCamelCase_						,      batch_size=lowerCamelCase_						,      seq_length=lowerCamelCase_						,      is_pair=lowerCamelCase_						,      framework=lowerCamelCase_      )

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

          return common_inputs

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          if self.task in ["default", "seq2seq-lm"]:
                snake_case        =     super()._flatten_past_key_values_(lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_      )
          else:
                snake_case        =     super(lowerCamelCase_						,      self      )._flatten_past_key_values_(
                    lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_      )



"""simple docstring"""
import requests
from bsa import BeautifulSoup





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str = "https://www.worldometers.info/coronavirus"   )   ->			dict:





						"""simple docstring"""






						snake_case        =     BeautifulSoup(requests.get(_UpperCamelCase   ).text	,     'html.parser'   )
						snake_case        =     soup.findAll('h1'   )
						snake_case        =     soup.findAll('div'	,     {'class': 'maincounter-number'}   )
						keys += soup.findAll('span'	,     {'class': 'panel-title'}   )
						values += soup.findAll('div'	,     {'class': 'number-table-main'}   )
						return {key.text.strip(): value.text.strip() for key, value in zip(_UpperCamelCase	,     _UpperCamelCase   )}


if __name__ == "__main__":
					print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
					for key, value in world_covidaa_stats().items():
										print(f"""{key}\n{value}\n""")


