






'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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 GPTaTokenizer, TFOPTForCausalLM, TFOPTModel







def 			A__							(      _a  :					Optional[Any]		,  _a  :					Optional[Any]		,  _a  :					int=None		,  _a  :					Optional[int]=None	):





 '''simple docstring'''







 if attention_mask is None:
  snake_case__  :    Optional[int]							       =tf.cast(tf.math.not_equal(_a		,  config.pad_token_id	)		,  tf.inta	)
 return {"input_ids": input_ids, "attention_mask": attention_mask}







@require_tf
class  _lowercase   :
      _a							:						str										=							OPTConfig
      _a							:						Optional[Any]										=							{}
      _a							:						str										=							'gelu'
      def __init__(     self				,			a				,			a=1_3				,			a=7				,			a=True				,			a=False				,			a=9_9				,			a=1_6				,			a=2				,			a=4				,			a=4				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=2_0				,			a=2				,			a=1				,			a=0				,			a=1_6				,			a=1_6				,			):
       snake_case__  :    Union[str, Any]							       =parent
       snake_case__  :    int							       =batch_size
       snake_case__  :    int							       =seq_length
       snake_case__  :    str							       =is_training
       snake_case__  :    int							       =use_labels
       snake_case__  :    List[str]							       =vocab_size
       snake_case__  :    Dict							       =hidden_size
       snake_case__  :    int							       =num_hidden_layers
       snake_case__  :    str							       =num_attention_heads
       snake_case__  :    Optional[Any]							       =intermediate_size
       snake_case__  :    List[str]							       =hidden_act
       snake_case__  :    Dict							       =hidden_dropout_prob
       snake_case__  :    str							       =attention_probs_dropout_prob
       snake_case__  :    Optional[Any]							       =max_position_embeddings
       snake_case__  :    Optional[Any]							       =eos_token_id
       snake_case__  :    Any							       =pad_token_id
       snake_case__  :    List[str]							       =bos_token_id
       snake_case__  :    str							       =embed_dim
       snake_case__  :    Union[str, Any]							       =word_embed_proj_dim
       snake_case__  :    int							       =False
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =ids_tensor([self.batch_size, self.seq_length - 1]				,			self.vocab_size   )
       snake_case__  :    Union[str, Any]							       =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )				,			1   )
       snake_case__  :    List[str]							       =tf.concat([input_ids, eos_tensor]				,			axis=1   )

       snake_case__  :    str							       =self.config_cls(
           vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			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_id=self.eos_token_id				,			bos_token_id=self.bos_token_id				,			pad_token_id=self.pad_token_id				,			embed_dim=self.embed_dim				,			word_embed_proj_dim=self.word_embed_proj_dim				,			is_encoder_decoder=snake_case__				,			**self.config_updates				,			)
       snake_case__  :    Optional[Any]							       =prepare_opt_inputs_dict(snake_case__				,			snake_case__   )
       return config, inputs_dict



      def 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Union[str, Any]							       =TFOPTModel(config=snake_case__   )
       snake_case__  :    Any							       =inputs_dict["input_ids"]

       snake_case__  :    Optional[int]							       =input_ids[:1, :]
       snake_case__  :    Dict							       =inputs_dict["attention_mask"][:1, :]
       snake_case__  :    int							       =1

       # first forward pass
       snake_case__  :    int							       =model(snake_case__				,			attention_mask=snake_case__				,			use_cache=snake_case__   )

       snake_case__  :    str							       =outputs.to_tuple()

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

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

       snake_case__  :    List[str]							       =model(snake_case__				,			attention_mask=snake_case__   )[0]
       snake_case__  :    Union[str, Any]							       =model(snake_case__				,			attention_mask=snake_case__				,			past_key_values=snake_case__   )[0]

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

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

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







@require_tf
class  _lowercase   (   SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__						,				unittest.TestCase						):
      _a							:						List[str]										=							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
      _a							:						Tuple										=							(TFOPTForCausalLM,) if is_tf_available() else ()
      _a							:						Optional[Any]										=							(
          {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
      )
      _a							:						int										=							False
      _a							:						Dict										=							False
      _a							:						Optional[int]										=							False
      _a							:						Dict										=							10
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =TFOPTModelTester(self   )
       snake_case__  :    Optional[Any]							       =ConfigTester(self				,			config_class=snake_case__   )
      def 						lowercase__					(     self   ):
       self.config_tester.run_common_tests()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*snake_case__   )



      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =self.model_tester.prepare_config_and_inputs_for_common()

       def _get_word_embedding_weight(a				,			a   ):
        if hasattr(snake_case__				,			"""weight"""   ):
         return embedding_layer.weight
        else:
         # Here we build the word embeddings weights if not exists.
         # And then we retry to get the attribute once built.
         model.build()
         if hasattr(snake_case__				,			"""weight"""   ):
          return embedding_layer.weight
         else:
          return None

       for model_class in self.all_model_classes:
        for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
         # build the embeddings
         snake_case__  :    List[Any]							       =model_class(config=snake_case__   )
         snake_case__  :    Optional[int]							       =_get_word_embedding_weight(snake_case__				,			model.get_input_embeddings()   )
         snake_case__  :    Union[str, Any]							       =_get_word_embedding_weight(snake_case__				,			model.get_output_embeddings()   )

         # reshape the embeddings
         model.resize_token_embeddings(snake_case__   )
         snake_case__  :    List[str]							       =_get_word_embedding_weight(snake_case__				,			model.get_input_embeddings()   )
         snake_case__  :    List[Any]							       =_get_word_embedding_weight(snake_case__				,			model.get_output_embeddings()   )

         # check that the resized embeddings size matches the desired size.
         snake_case__  :    str							       =size if size is not None else config.vocab_size

         self.assertEqual(new_input_embeddings.shape[0]				,			snake_case__   )

         # check that weights remain the same after resizing
         snake_case__  :    Any							       =True
         for pa, pa in zip(old_input_embeddings.value()				,			new_input_embeddings.value()   ):
          if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
           snake_case__  :    Dict							       =False
         self.assertTrue(snake_case__   )

         if old_output_embeddings is not None and new_output_embeddings is not None:
          self.assertEqual(new_output_embeddings.shape[0]				,			snake_case__   )

          snake_case__  :    List[str]							       =True
          for pa, pa in zip(old_output_embeddings.value()				,			new_output_embeddings.value()   ):
           if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
            snake_case__  :    str							       =False
          self.assertTrue(snake_case__   )







def 			A__							(      _a  :					Union[str, Any]	):





 '''simple docstring'''







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







@require_tf
class  _lowercase   (   unittest.TestCase						):
      _a							:						Optional[Any]										=							99
      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =tf.ones((4, 1)				,			dtype=tf.intaa   ) * 2
       snake_case__  :    Dict							       =tf.concat([ids_tensor((4, 6)				,			self.vocab_size - 3   ) + 3, eos_column_vector]				,			axis=1   )
       snake_case__  :    str							       =input_ids.shape[0]
       snake_case__  :    Optional[int]							       =OPTConfig(
           vocab_size=self.vocab_size				,			hidden_size=2_4				,			num_hidden_layers=2				,			num_attention_heads=2				,			ffn_dim=3_2				,			max_position_embeddings=4_8				,			eos_token_id=2				,			pad_token_id=1				,			bos_token_id=0				,			)
       return config, input_ids, batch_size







@require_sentencepiece
@require_tf
class  _lowercase   (   unittest.TestCase						):
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =TFOPTModel.from_pretrained("""facebook/opt-350m"""   )
       snake_case__  :    str							       =_long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   )
       snake_case__  :    Tuple							       =tf.not_equal(snake_case__				,			model.config.pad_token_id   )
       with tf.GradientTape():
        snake_case__  :    Tuple							       =model(input_ids=snake_case__				,			attention_mask=snake_case__   ).last_hidden_state
       snake_case__  :    List[str]							       =(1, 1_1, 5_1_2)
       self.assertEqual(output.shape				,			snake_case__   )
       snake_case__  :    Dict							       =tf.constant(
           [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]   )
       self.assertTrue(np.allclose(output[:, :3, :3]				,			snake_case__				,			atol=4e-3   )   )

       snake_case__  :    int							       =tf.function(snake_case__				,			jit_compile=snake_case__   )
       snake_case__  :    List[str]							       =xla_generate(snake_case__				,			snake_case__   )[0]
       self.assertTrue(np.allclose(output[:, :3, :3]				,			snake_case__				,			atol=4e-2   )   )







@require_tf
@slow
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       super().setUp()
       snake_case__  :    List[str]							       ="facebook/opt-350m"



      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =TFOPTForCausalLM.from_pretrained(self.path_model   )
       snake_case__  :    str							       =GPTaTokenizer.from_pretrained(self.path_model   )

       snake_case__  :    Tuple							       =[
           "Today is a beautiful day and I want to",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]
       # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
       snake_case__  :    int							       =tokenizer(snake_case__				,			return_tensors="""tf"""				,			padding=snake_case__				,			add_special_tokens=snake_case__   )
       snake_case__  :    int							       =tf.math.reduce_mean(model(inputs.input_ids				,			attention_mask=inputs.attention_mask   )[0]				,			axis=-1   )
       snake_case__  :    int							       =tf.constant(
           [
               [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
               [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
               [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
               [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
           ]   )
       self.assertTrue(np.allclose(snake_case__				,			snake_case__				,			atol=1e-4   )   )

       snake_case__  :    int							       =tf.function(snake_case__				,			jit_compile=snake_case__   )
       snake_case__  :    Optional[Any]							       =tf.math.reduce_mean(xla_generate(inputs.input_ids				,			attention_mask=inputs.attention_mask   )[0]				,			axis=-1   )
       self.assertTrue(np.allclose(snake_case__				,			snake_case__				,			atol=1e-4   )   )







@require_tf
@slow
class  _lowercase   (   unittest.TestCase						):
      @property
      def 						lowercase__					(     self   ):
       return [
           "Today is a beautiful day and I want",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       ="facebook/opt-125m"

       snake_case__  :    List[str]							       =[
           "Today is a beautiful day and I want to",
           "In the city of New York, the city",
           "Paris is the capital of France and the capital",
           "Computers and mobile phones have taken over the",
       ]

       snake_case__  :    Union[str, Any]							       =[]
       snake_case__  :    Optional[Any]							       =GPTaTokenizer.from_pretrained(snake_case__   )
       snake_case__  :    Any							       =TFOPTForCausalLM.from_pretrained(snake_case__   )

       for prompt in self.prompts:
        snake_case__  :    Optional[int]							       =tokenizer(snake_case__				,			return_tensors="""tf"""   ).input_ids

        snake_case__  :    List[str]							       =model.generate(snake_case__				,			max_length=1_0   )

        snake_case__  :    Union[str, Any]							       =tokenizer.batch_decode(snake_case__				,			skip_special_tokens=snake_case__   )
        predicted_outputs += generated_string

       self.assertListEqual(snake_case__				,			snake_case__   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="facebook/opt-350m"

       snake_case__  :    Optional[Any]							       =GPTaTokenizer.from_pretrained(snake_case__   )
       snake_case__  :    Optional[int]							       =TFOPTForCausalLM.from_pretrained(snake_case__   )

       snake_case__  :    List[str]							       ="left"

       # use different length sentences to test batching
       snake_case__  :    Dict							       =[
           "Hello, my dog is a little",
           "Today, I",
       ]

       snake_case__  :    Optional[int]							       =tokenizer(snake_case__				,			return_tensors="""tf"""				,			padding=snake_case__   )
       snake_case__  :    int							       =inputs["input_ids"]

       snake_case__  :    int							       =model.generate(input_ids=snake_case__				,			attention_mask=inputs["""attention_mask"""]   )

       snake_case__  :    Dict							       =tokenizer(sentences[0]				,			return_tensors="""tf"""   ).input_ids
       snake_case__  :    Optional[Any]							       =model.generate(input_ids=snake_case__   )

       snake_case__  :    Tuple							       =inputs_non_padded.shape[-1] - tf.math.reduce_sum(
           tf.cast(inputs["""attention_mask"""][-1]				,			tf.intaa   )   )
       snake_case__  :    Optional[Any]							       =tokenizer(sentences[1]				,			return_tensors="""tf"""   ).input_ids
       snake_case__  :    Union[str, Any]							       =model.generate(input_ids=snake_case__				,			max_length=model.config.max_length - num_paddings   )

       snake_case__  :    Tuple							       =tokenizer.batch_decode(snake_case__				,			skip_special_tokens=snake_case__   )
       snake_case__  :    Optional[Any]							       =tokenizer.decode(output_non_padded[0]				,			skip_special_tokens=snake_case__   )
       snake_case__  :    int							       =tokenizer.decode(output_padded[0]				,			skip_special_tokens=snake_case__   )

       snake_case__  :    List[Any]							       =[
           "Hello, my dog is a little bit of a dork.\nI'm a little bit",
           "Today, I was in the middle of a conversation with a friend about the",
       ]
       self.assertListEqual(snake_case__				,			snake_case__   )
       self.assertListEqual(snake_case__				,			[non_padded_sentence, padded_sentence]   )



      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="facebook/opt-350m"

       snake_case__  :    Optional[int]							       =[
           "Today is a beautiful day and I want to",
           "In the city of San Francisco, the city",
           "Paris is the capital of France and the capital",
           "Computers and mobile phones have taken over the",
       ]

       snake_case__  :    Dict							       =[]
       snake_case__  :    Optional[int]							       =GPTaTokenizer.from_pretrained(snake_case__   )
       snake_case__  :    int							       =TFOPTForCausalLM.from_pretrained(snake_case__   )

       for prompt in self.prompts:
        snake_case__  :    Dict							       =tokenizer(snake_case__				,			return_tensors="""tf"""   ).input_ids

        snake_case__  :    Any							       =model.generate(snake_case__				,			max_length=1_0   )

        snake_case__  :    Tuple							       =tokenizer.batch_decode(snake_case__				,			skip_special_tokens=snake_case__   )
        predicted_outputs += generated_string

       self.assertListEqual(snake_case__				,			snake_case__   )













from collections import namedtuple

import requests
from lxml import html  # type: ignore

__lowerCamelCase	:		Any										=   namedtuple("""covid_data""", """cases deaths recovered""")







def 			A__							(      _a  :					str = "https://www.worldometers.info/coronavirus/"	):





 '''simple docstring'''







 snake_case__  :    Any							       ="""//div[@class = \"maincounter-number\"]/span/text()"""
 return covid_data(*html.fromstring(requests.get(_a	).content	).xpath(_a	)	)


__lowerCamelCase	:		str										=   """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()))





