





"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import LEDConfig, 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 TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class 						__snake_case   :





 """simple docstring"""


 _lowerCamelCase								=	LEDConfig
 _lowerCamelCase								=	{}
 _lowerCamelCase								=	"""gelu"""

 def __init__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase=13   ,		__lowerCamelCase=7   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False   ,		__lowerCamelCase=99   ,		__lowerCamelCase=32   ,		__lowerCamelCase=2   ,		__lowerCamelCase=4   ,		__lowerCamelCase=37   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=20   ,		__lowerCamelCase=2   ,		__lowerCamelCase=1   ,		__lowerCamelCase=0   ,		__lowerCamelCase=4   ,		):



     '''simple docstring'''




     __A       :		Optional[int]							=	parent
     __A       :		Optional[int]							=	batch_size
     __A       :		Optional[Any]							=	seq_length
     __A       :		Tuple							=	is_training
     __A       :		Optional[Any]							=	use_labels
     __A       :		Tuple							=	vocab_size
     __A       :		Optional[Any]							=	hidden_size
     __A       :		List[Any]							=	num_hidden_layers
     __A       :		List[Any]							=	num_attention_heads
     __A       :		List[str]							=	intermediate_size

     __A       :		Dict							=	hidden_dropout_prob
     __A       :		List[Any]							=	attention_probs_dropout_prob
     __A       :		Tuple							=	max_position_embeddings
     __A       :		Union[str, Any]							=	eos_token_id
     __A       :		List[str]							=	pad_token_id
     __A       :		Dict							=	bos_token_id
     __A       :		List[str]							=	attention_window

     # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
     # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
     # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
     # because its local attention only attends to `self.attention_window` and one before and one after
     __A       :		Any							=	self.attention_window + 2

     # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
     # the `test_attention_outputs` and `test_hidden_states_output` tests
     __A       :		str							=	(
         self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
     )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	ids_tensor([self.batch_size, self.seq_length - 1]   ,		self.vocab_size    )
     __A       :		Any							=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )   ,		1    )
     __A       :		List[Any]							=	tf.concat([input_ids, eos_tensor]   ,		axis=1    )

     __A       :		Dict							=	ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size    )

     __A       :		Tuple							=	self.config_cls(
         vocab_size=self.vocab_size   ,		d_model=self.hidden_size   ,		encoder_layers=self.num_hidden_layers   ,		decoder_layers=self.num_hidden_layers   ,		encoder_attention_heads=self.num_attention_heads   ,		decoder_attention_heads=self.num_attention_heads   ,		encoder_ffn_dim=self.intermediate_size   ,		decoder_ffn_dim=self.intermediate_size   ,		dropout=self.hidden_dropout_prob   ,		attention_dropout=self.attention_probs_dropout_prob   ,		max_position_embeddings=self.max_position_embeddings   ,		eos_token_ids=[2]   ,		bos_token_id=self.bos_token_id   ,		pad_token_id=self.pad_token_id   ,		decoder_start_token_id=self.pad_token_id   ,		attention_window=self.attention_window   ,		**self.config_updates   ,		)
     __A       :		Any							=	prepare_led_inputs_dict(__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Dict							=	tf.concat(
         [tf.zeros_like(__lowerCamelCase    )[:, :-1], tf.ones_like(__lowerCamelCase    )[:, -1:]]   ,		axis=-1   ,		)
     __A       :		Tuple							=	global_attention_mask
     return config, inputs_dict







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[str]							=	TFLEDModel(config=__lowerCamelCase    ).get_decoder()
     __A       :		List[Any]							=	inputs_dict['''input_ids''']

     __A       :		List[str]							=	input_ids[:1, :]
     __A       :		List[str]							=	inputs_dict['''attention_mask'''][:1, :]
     __A       :		Optional[Any]							=	1

     # first forward pass
     __A       :		Union[str, Any]							=	model(__lowerCamelCase   ,		attention_mask=__lowerCamelCase   ,		use_cache=__lowerCamelCase    )

     __A					,			__A       :		Optional[Any]							=	outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     __A       :		Optional[Any]							=	ids_tensor((self.batch_size, 3)   ,		config.vocab_size    )
     __A       :		Dict							=	tf.cast(ids_tensor((self.batch_size, 3)   ,		2    )   ,		tf.inta    )

     # append to next input_ids and
     __A       :		Union[str, Any]							=	tf.concat([input_ids, next_tokens]   ,		axis=-1    )
     __A       :		Any							=	tf.concat([attention_mask, next_attn_mask]   ,		axis=-1    )

     __A       :		List[Any]							=	model(__lowerCamelCase   ,		attention_mask=__lowerCamelCase    )[0]
     __A       :		Any							=	model(__lowerCamelCase   ,		attention_mask=__lowerCamelCase   ,		past_key_values=__lowerCamelCase    )[0]

     self.parent.assertEqual(next_tokens.shape[1]   ,		output_from_past.shape[1]    )

     # select random slice
     __A       :		Optional[int]							=	int(ids_tensor((1,)   ,		output_from_past.shape[-1]    )    )
     __A       :		Optional[Any]							=	output_from_no_past[:, -3:, random_slice_idx]
     __A       :		Tuple							=	output_from_past[:, :, random_slice_idx]

     # test that outputs are equal for slice
     tf.debugging.assert_near(__lowerCamelCase   ,		__lowerCamelCase   ,		rtol=1e-3    )







def 						__lowercase	(  snake_case_    :    List[str]							,snake_case_    :    str							,snake_case_    :    List[str]							,snake_case_    :    Tuple=None							,snake_case_    :    Optional[Any]=None							,snake_case_    :    List[str]=None							,snake_case_    :    Tuple=None							,)  ->List[Any]:



    '''simple docstring'''







    if attention_mask is None:
        __A       :		Union[str, Any]							=	tf.cast(tf.math.not_equal(snake_case_							,config.pad_token_id		)							,tf.inta		)
    if decoder_attention_mask is None:
        __A       :		Tuple							=	tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape							,dtype=tf.inta		),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,config.pad_token_id		)							,tf.inta		),
            ]							,axis=-1							,)
    if head_mask is None:
        __A       :		Union[str, Any]							=	tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
    if decoder_head_mask is None:
        __A       :		List[str]							=	tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "decoder_input_ids": decoder_input_ids,
        "decoder_attention_mask": decoder_attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
    }






@require_tf
class 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 _lowerCamelCase								=	(TFLEDForConditionalGeneration,) if is_tf_available() else ()
 _lowerCamelCase								=	(
     {
         """conversational""": TFLEDForConditionalGeneration,
         """feature-extraction""": TFLEDModel,
         """summarization""": TFLEDForConditionalGeneration,
         """text2text-generation""": TFLEDForConditionalGeneration,
         """translation""": TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _lowerCamelCase								=	True
 _lowerCamelCase								=	False
 _lowerCamelCase								=	False
 _lowerCamelCase								=	False

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	TFLEDModelTester(self    )
     __A       :		Optional[int]							=	ConfigTester(self   ,		config_class=__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A					,			__A       :		str							=	self.model_tester.prepare_config_and_inputs_for_common()
     __A       :		Optional[int]							=	tf.zeros_like(inputs_dict['''attention_mask''']    )
     __A       :		Dict							=	2
     __A       :		Dict							=	tf.where(
         tf.range(self.model_tester.seq_length    )[None, :] < num_global_attn_indices   ,		1   ,		inputs_dict['''global_attention_mask''']   ,		)

     __A       :		Any							=	True
     __A       :		Any							=	self.model_tester.seq_length
     __A       :		Dict							=	self.model_tester.encoder_seq_length

     def check_decoder_attentions_output(__lowerCamelCase    ):
         __A       :		int							=	outputs.decoder_attentions
         self.assertEqual(len(__lowerCamelCase    )   ,		self.model_tester.num_hidden_layers    )
         self.assertListEqual(
             list(decoder_attentions[0].shape[-3:]    )   ,		[self.model_tester.num_attention_heads, seq_length, seq_length]   ,		)

     def check_encoder_attentions_output(__lowerCamelCase    ):
         __A       :		Optional[int]							=	[t.numpy() for t in outputs.encoder_attentions]
         __A       :		Optional[int]							=	[t.numpy() for t in outputs.encoder_global_attentions]
         self.assertEqual(len(__lowerCamelCase    )   ,		self.model_tester.num_hidden_layers    )
         self.assertEqual(len(__lowerCamelCase    )   ,		self.model_tester.num_hidden_layers    )
         self.assertListEqual(
             list(attentions[0].shape[-3:]    )   ,		[self.model_tester.num_attention_heads, seq_length, seq_length]   ,		)
         self.assertListEqual(
             list(global_attentions[0].shape[-3:]    )   ,		[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]   ,		)

     for model_class in self.all_model_classes:
         __A       :		Tuple							=	True
         __A       :		Optional[Any]							=	False
         __A       :		Optional[Any]							=	False
         __A       :		Optional[int]							=	model_class(__lowerCamelCase    )
         __A       :		Optional[int]							=	model(self._prepare_for_class(__lowerCamelCase   ,		__lowerCamelCase    )    )
         __A       :		str							=	len(__lowerCamelCase    )
         self.assertEqual(config.output_hidden_states   ,		__lowerCamelCase    )
         check_encoder_attentions_output(__lowerCamelCase    )

         if self.is_encoder_decoder:
             __A       :		int							=	model_class(__lowerCamelCase    )
             __A       :		Any							=	model(self._prepare_for_class(__lowerCamelCase   ,		__lowerCamelCase    )    )
             self.assertEqual(config.output_hidden_states   ,		__lowerCamelCase    )
             check_decoder_attentions_output(__lowerCamelCase    )

         # Check that output attentions can also be changed via the config
         del inputs_dict["output_attentions"]
         __A       :		Dict							=	True
         __A       :		Union[str, Any]							=	model_class(__lowerCamelCase    )
         __A       :		Optional[Any]							=	model(self._prepare_for_class(__lowerCamelCase   ,		__lowerCamelCase    )    )
         self.assertEqual(config.output_hidden_states   ,		__lowerCamelCase    )
         check_encoder_attentions_output(__lowerCamelCase    )

         # Check attention is always last and order is fine
         __A       :		Tuple							=	True
         __A       :		int							=	True
         __A       :		Union[str, Any]							=	model_class(__lowerCamelCase    )
         __A       :		Dict							=	model(self._prepare_for_class(__lowerCamelCase   ,		__lowerCamelCase    )    )

         self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)   ,		len(__lowerCamelCase    )    )
         self.assertEqual(model.config.output_hidden_states   ,		__lowerCamelCase    )
         check_encoder_attentions_output(__lowerCamelCase    )

 @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''    )
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     pass







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     pass







def 						__lowercase	(  snake_case_    :    List[str]		)  ->List[str]:



    '''simple docstring'''







    return tf.constant(snake_case_							,dtype=tf.intaa		)


a_					=     1e-4






@slow
@require_tf
class 						__snake_case   (						unittest.TestCase	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''    ).led

     # change to intended input here
     __A       :		Optional[Any]							=	_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
     __A       :		Dict							=	_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
     __A       :		int							=	prepare_led_inputs_dict(model.config   ,		__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Tuple							=	model(**__lowerCamelCase    )[0]
     __A       :		List[str]							=	(1, 1024, 768)
     self.assertEqual(output.shape   ,		__lowerCamelCase    )
     # change to expected output here
     __A       :		Optional[int]							=	tf.convert_to_tensor(
         [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]   ,		)
     tf.debugging.assert_near(output[:, :3, :3]   ,		__lowerCamelCase   ,		atol=1e-3    )







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''    )

     # change to intended input here
     __A       :		int							=	_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
     __A       :		Dict							=	_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]    )
     __A       :		Union[str, Any]							=	prepare_led_inputs_dict(model.config   ,		__lowerCamelCase   ,		__lowerCamelCase    )
     __A       :		Dict							=	model(**__lowerCamelCase    )[0]
     __A       :		List[str]							=	(1, 1024, model.config.vocab_size)
     self.assertEqual(output.shape   ,		__lowerCamelCase    )
     # change to expected output here
     __A       :		str							=	tf.convert_to_tensor(
         [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]   ,		)
     tf.debugging.assert_near(output[:, :3, :3]   ,		__lowerCamelCase   ,		atol=1e-3   ,		rtol=1e-3    )











"""simple docstring"""



from collections import namedtuple

import requests
from lxml import html  # type: ignore

a_					=     namedtuple("""covid_data""", """cases deaths recovered""")







def 						__lowercase	(  snake_case_    :    str = "https://www.worldometers.info/coronavirus/"		)  ->covid_data:



    '''simple docstring'''







    __A       :		List[str]							=	'''//div[@class = "maincounter-number"]/span/text()'''
    return covid_data(*html.fromstring(requests.get(snake_case_		).content		).xpath(snake_case_		)		)


a_					=     """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()))




