


"""simple docstring"""


from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFTransfoXLForSequenceClassification,
       TFTransfoXLLMHeadModel,
       TFTransfoXLModel,
   )

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     )      ->							str:
    lowercase__       :       List[str]				=						parent
    lowercase__       :       str				=						13
    lowercase__       :       List[str]				=						7
    lowercase__       :       str				=						30
    lowercase__       :       Optional[Any]				=						self.seq_length + self.mem_len
    lowercase__       :       List[str]				=						15
    lowercase__       :       Optional[Any]				=						True
    lowercase__       :       Any				=						True
    lowercase__       :       Dict				=						99
    lowercase__       :       Dict				=						[10, 50, 80]
    lowercase__       :       Any				=						32
    lowercase__       :       Optional[Any]				=						32
    lowercase__       :       int				=						4
    lowercase__       :       Optional[Any]				=						8
    lowercase__       :       Optional[int]				=						128
    lowercase__       :       Dict				=						2
    lowercase__       :       int				=						2
    lowercase__       :       List[str]				=						None
    lowercase__       :       List[str]				=						1
    lowercase__       :       Dict				=						0
    lowercase__       :       List[Any]				=						3
    lowercase__       :       List[str]				=						self.vocab_size - 1
    lowercase__       :       Dict				=						0.01


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[int]				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)
    lowercase__       :       str				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

    lowercase__       :       Dict				=						None
    if self.use_labels:
     lowercase__       :       Dict				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

    lowercase__       :       Union[str, Any]				=						TransfoXLConfig(
        vocab_size=self.vocab_size ,     mem_len=self.mem_len ,     clamp_len=self.clamp_len ,     cutoffs=self.cutoffs ,     d_model=self.hidden_size ,     d_embed=self.d_embed ,     n_head=self.num_attention_heads ,     d_head=self.d_head ,     d_inner=self.d_inner ,     div_val=self.div_val ,     n_layer=self.num_hidden_layers ,     eos_token_id=self.eos_token_id ,     pad_token_id=self.vocab_size - 1 ,     init_range=self.init_range ,     num_labels=self.num_labels ,     )

    return (config, input_ids_a, input_ids_a, lm_labels)


   def 					UpperCAmelCase__(						self						)      ->							int:
    random.seed(self.seed						)
    tf.random.set_seed(self.seed						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							List[str]:
    lowercase__       :       List[str]				=						TFTransfoXLModel(lowerCamelCase__						)

    lowercase__	,       lowercase__       :       int				=						model(lowerCamelCase__						).to_tuple()

    lowercase__       :       Union[str, Any]				=						{"""input_ids""": input_ids_a, """mems""": mems_a}

    lowercase__	,       lowercase__       :       List[str]				=						model(lowerCamelCase__						).to_tuple()

    self.parent.assertEqual(hidden_states_a.shape ,     (self.batch_size, self.seq_length, self.hidden_size)						)
    self.parent.assertEqual(hidden_states_a.shape ,     (self.batch_size, self.seq_length, self.hidden_size)						)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a] ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,     )
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a] ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,     )


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Any				=						TFTransfoXLLMHeadModel(lowerCamelCase__						)

    lowercase__	,       lowercase__       :       Any				=						model(lowerCamelCase__						).to_tuple()

    lowercase__       :       Union[str, Any]				=						{"""input_ids""": input_ids_a, """labels""": lm_labels}
    lowercase__	,       lowercase__       :       Any				=						model(lowerCamelCase__						).to_tuple()

    lowercase__	,       lowercase__       :       Any				=						model([input_ids_a, mems_a]						).to_tuple()

    lowercase__       :       List[str]				=						{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

    lowercase__	,       lowercase__       :       Tuple				=						model(lowerCamelCase__						).to_tuple()

    self.parent.assertEqual(lm_logits_a.shape ,     (self.batch_size, self.seq_length, self.vocab_size)						)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a] ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,     )

    self.parent.assertEqual(lm_logits_a.shape ,     (self.batch_size, self.seq_length, self.vocab_size)						)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a] ,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,     )


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[Any]:
    lowercase__       :       Optional[Any]				=						TFTransfoXLForSequenceClassification(lowerCamelCase__						)
    lowercase__       :       List[str]				=						model(lowerCamelCase__						)
    self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.num_labels)						)







   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       str				=						self.prepare_config_and_inputs()
    ((lowercase__)	,       (lowercase__)	,       (lowercase__)	,       (lowercase__))       :       int				=						config_and_inputs
    lowercase__       :       int				=						{"""input_ids""": input_ids_a}
    return config, inputs_dict

@require_tf
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    (
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   _a   :       Any									=    () if is_tf_available() else ()
   _a   :       Tuple									=    (
       {
           '''feature-extraction''': TFTransfoXLModel,
           '''text-classification''': TFTransfoXLForSequenceClassification,
           '''text-generation''': TFTransfoXLLMHeadModel,
           '''zero-shot''': TFTransfoXLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
   _a   :       List[str]									=    False
   _a   :       Optional[Any]									=    False
   _a   :       int									=    False
   _a   :       int									=    False


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Optional[Any]:
    if pipeline_test_casse_name == "TextGenerationPipelineTests":
     # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
     # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
     # tokenizer.
     return True

    return False


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Optional[int]				=						TFTransfoXLModelTester(self						)
    lowercase__       :       Optional[int]				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     d_embed=37						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    self.config_tester.run_common_tests()


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    self.model_tester.set_seed()
    lowercase__       :       Optional[int]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_model(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    self.model_tester.set_seed()
    lowercase__       :       List[Any]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Union[str, Any]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__	,       lowercase__       :       int				=						self.model_tester.prepare_config_and_inputs_for_common()
    lowercase__       :       List[Any]				=						[TFTransfoXLForSequenceClassification]

    for model_class in self.all_model_classes:
     lowercase__       :       int				=						model_class(lowerCamelCase__						)
     assert isinstance(model.get_input_embeddings() ,     tf.keras.layers.Layer						)
     if model_class in list_other_models_with_output_ebd:
      lowercase__       :       str				=						model.get_output_embeddings()
      assert isinstance(lowerCamelCase__ ,     tf.keras.layers.Layer						)
      lowercase__       :       str				=						model.get_bias()
      assert name is None
     else:
      lowercase__       :       List[Any]				=						model.get_output_embeddings()
      assert x is None
      lowercase__       :       int				=						model.get_bias()
      assert name is None


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    # TODO JP: Make TransfoXL XLA compliant
    pass


   @slow
   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__       :       Optional[Any]				=						TFTransfoXLModel.from_pretrained(lowerCamelCase__						)
     self.assertIsNotNone(lowerCamelCase__						)







   @unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass

@require_tf
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @unittest.skip("""Skip test until #12651 is resolved."""						)
   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Any				=						TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""						)
    # fmt: off
    lowercase__       :       Any				=						tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] ,     dtype=tf.intaa						)  # noqa: E231
    # fmt: on
    #  In 1991 , the remains of Russian Tsar Nicholas II and his family
    #  ( except for Alexei and Maria ) are discovered .
    #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
    #  remainder of the story . 1883 Western Siberia ,
    #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
    #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
    #  father initially slaps him for making such an accusation , Rasputin watches as the
    #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
    #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
    #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

    # fmt: off
    lowercase__       :       List[Any]				=						[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
    # fmt: on
    #  In 1991, the remains of Russian Tsar Nicholas II and his family (
    #  except for Alexei and Maria ) are discovered. The voice of young son,
    #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
    #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
    #  and a group of men to perform magic. Rasputin has a vision and
    #  denounces one of the men as a horse thief. Although his father initially
    #  slaps him for making such an accusation, Rasputin watches as the man
    #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
    #  of the Virgin Mary, prompting him to become a priest.
    #  Rasputin quickly becomes famous, with people, even a bishop, begging for
    #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
    # Nicholas II and his family were discovered. The voice of <unk> young son,
    # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

    lowercase__       :       Any				=						model.generate(lowerCamelCase__ ,     max_length=200 ,     do_sample=lowerCamelCase__						)
    self.assertListEqual(output_ids[0].numpy().tolist() ,     lowerCamelCase__						)



"""simple docstring"""


import requests
from bsa import BeautifulSoup


def    _lowerCamelCase   (     lowerCamelCase__	:							str = "https://www.worldometers.info/coronavirus"  ):
 lowercase__       :       Optional[Any]				=						BeautifulSoup(requests.get(lowerCamelCase__  ).text       ,		"""html.parser"""  )
 lowercase__       :       Union[str, Any]				=						soup.findAll("""h1"""  )
 lowercase__       :       List[str]				=						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(lowerCamelCase__       ,		lowerCamelCase__  )}


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")