





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )


class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def __init__(    self   :			List[str]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			List[str]=13      ,				UpperCAmelCase__   :			List[str]=7      ,				UpperCAmelCase__   :			Union[str, Any]=True      ,				UpperCAmelCase__   :			int=True      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Optional[int]=99      ,				UpperCAmelCase__   :			Any=32      ,				UpperCAmelCase__   :			Any=5      ,				UpperCAmelCase__   :			int=4      ,				UpperCAmelCase__   :			Optional[Any]=37      ,				UpperCAmelCase__   :			Dict="gelu"      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			int=512      ,				UpperCAmelCase__   :			Optional[Any]=16      ,				UpperCAmelCase__   :			List[Any]=2      ,				UpperCAmelCase__   :			Union[str, Any]=0.02      ,				UpperCAmelCase__   :			List[str]=4      ,				)		->Optional[int]:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					seq_length
           A__   				=					is_training
           A__   				=					use_attention_mask
           A__   				=					use_token_type_ids
           A__   				=					use_labels
           A__   				=					vocab_size
           A__   				=					hidden_size
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					intermediate_size
           A__   				=					hidden_act
           A__   				=					hidden_dropout_prob
           A__   				=					attention_probs_dropout_prob
           A__   				=					max_position_embeddings
           A__   				=					type_vocab_size
           A__   				=					type_sequence_label_size
           A__   				=					initializer_range
           A__   				=					num_choices


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[str]:





           '''simple docstring'''

           A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)

           A__   				=					None
           if self.use_attention_mask:
                  A__   				=					random_attention_mask([self.batch_size, self.seq_length])

           A__   				=					None
           if self.use_token_type_ids:
                  A__   				=					ids_tensor([self.batch_size, self.seq_length]      ,				self.type_vocab_size)

           A__   				=					RoFormerConfig(
               vocab_size=self.vocab_size      ,				hidden_size=self.hidden_size      ,				num_hidden_layers=self.num_hidden_layers      ,				num_attention_heads=self.num_attention_heads      ,				intermediate_size=self.intermediate_size      ,				hidden_act=self.hidden_act      ,				hidden_dropout_prob=self.hidden_dropout_prob      ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				type_vocab_size=self.type_vocab_size      ,				is_decoder=snake_case__      ,				initializer_range=self.initializer_range      ,				)

           return config, input_ids, token_type_ids, attention_mask





    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->int:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()
           A__				,							A__				,							A__				,							A__   				=					config_and_inputs
           A__   				=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict


@require_flax
class        UpperCamelCase_					(				__a	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   True

    UpperCAmelCase__													=   (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					FlaxRoFormerModelTester(self)





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Dict:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__   				=					model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''      ,				from_pt=snake_case__)
                  A__   				=					model(np.ones((1, 1)))
                  self.assertIsNotNone(snake_case__)


@require_flax
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''')
           A__   				=					jnp.array([[0, 1, 2, 3, 4, 5]])
           A__   				=					model(snake_case__)[0]

           A__   				=					50_000

           A__   				=					(1, 6, vocab_size)
           self.assertEqual(output.shape      ,				snake_case__)

           A__   				=					jnp.array(
               [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]])

           self.assertTrue(jnp.allclose(output[:, :3, :3]      ,				snake_case__      ,				atol=1e-4))








from collections import namedtuple

import requests
from lxml import html  # type: ignore

_lowerCamelCase			:	Optional[int]							 =     namedtuple("""covid_data""", """cases deaths recovered""")







def SCREAMING_SNAKE_CASE       ( lowercase_ = "https://www.worldometers.info/coronavirus/"		)     -> covid_data:



       """simple docstring"""

       A__   				=					'''//div[@class = "maincounter-number"]/span/text()'''
       return covid_data(*html.fromstring(requests.get(lowercase_		).content		).xpath(lowercase_		)		)


_lowerCamelCase			:	Optional[int]							 =     """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""
print(fmt.format(*covid_stats()))


