


"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class       A__						:


     """simple docstring"""
     snake_case__						:       str				    =BlenderbotConfig
     snake_case__						:       Optional[int]				    ={}
     snake_case__						:       Optional[int]				    ='''gelu'''

     def __init__(						self:    List[Any]  ,		__a:    Optional[Any]  ,		__a:    List[str]=13  ,		__a:    Any=7  ,		__a:    List[str]=True  ,		__a:    List[Any]=False  ,		__a:    Union[str, Any]=99  ,		__a:    Union[str, Any]=32  ,		__a:    int=2  ,		__a:    Dict=4  ,		__a:    int=37  ,		__a:    Union[str, Any]=0.1  ,		__a:    Any=0.1  ,		__a:    Optional[int]=20  ,		__a:    Tuple=2  ,		__a:    int=1  ,		__a:    str=0  ,		)->     Dict:
           lowerCamelCase       :       int   =						parent
           lowerCamelCase       :       Union[str, Any]   =						batch_size
           lowerCamelCase       :       Dict   =						seq_length
           lowerCamelCase       :       Union[str, Any]   =						is_training
           lowerCamelCase       :       Dict   =						use_labels
           lowerCamelCase       :       Any   =						vocab_size
           lowerCamelCase       :       int   =						hidden_size
           lowerCamelCase       :       Dict   =						num_hidden_layers
           lowerCamelCase       :       Optional[int]   =						num_attention_heads
           lowerCamelCase       :       Any   =						intermediate_size

           lowerCamelCase       :       List[Any]   =						hidden_dropout_prob
           lowerCamelCase       :       List[str]   =						attention_probs_dropout_prob
           lowerCamelCase       :       Dict   =						max_position_embeddings
           lowerCamelCase       :       Optional[Any]   =						eos_token_id
           lowerCamelCase       :       str   =						pad_token_id
           lowerCamelCase       :       List[Any]   =						bos_token_id

     def 		a__    (						self:    str						)->     List[Any]:
           lowerCamelCase       :       List[Any]   =						ids_tensor([self.batch_size, self.seq_length - 1]  ,		self.vocab_size						)
           lowerCamelCase       :       List[Any]   =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)  ,		1						)
           lowerCamelCase       :       Optional[Any]   =						tf.concat([input_ids, eos_tensor]  ,		axis=1						)

           lowerCamelCase       :       Union[str, Any]   =						ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size						)

           lowerCamelCase       :       Any   =						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  ,		**self.config_updates  ,		)
           lowerCamelCase       :       Dict   =						prepare_blenderbot_inputs_dict(lowerCAmelCase__  ,		lowerCAmelCase__  ,		lowerCAmelCase__						)
           return config, inputs_dict







     def 		a__    (						self:    int  ,		__a:    Tuple  ,		__a:    Any						)->     Tuple:
           lowerCamelCase       :       int   =						TFBlenderbotModel(config=lowerCAmelCase__						).get_decoder()
           lowerCamelCase       :       Optional[Any]   =						inputs_dict['input_ids']

           lowerCamelCase       :       str   =						input_ids[:1, :]
           lowerCamelCase       :       List[str]   =						inputs_dict['attention_mask'][:1, :]
           lowerCamelCase       :       Optional[Any]   =						inputs_dict['head_mask']
           lowerCamelCase       :       Dict   =						1

           # first forward pass
           lowerCamelCase       :       Optional[int]   =						model(lowerCAmelCase__  ,		attention_mask=lowerCAmelCase__  ,		head_mask=lowerCAmelCase__  ,		use_cache=lowerCAmelCase__						)

           lowerCamelCase       :       List[str]   =						outputs.to_tuple()

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

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

           lowerCamelCase       :       str   =						model(lowerCAmelCase__  ,		attention_mask=lowerCAmelCase__						)[0]
           lowerCamelCase       :       Union[str, 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
           lowerCamelCase       :       Tuple   =						int(ids_tensor((1,)  ,		output_from_past.shape[-1]						)						)
           lowerCamelCase       :       str   =						output_from_no_past[:, -3:, random_slice_idx]
           lowerCamelCase       :       Tuple   =						output_from_past[:, :, random_slice_idx]

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




def 	snake_case		(  UpperCamelCase__							:    Dict      ,					UpperCamelCase__							:    str      ,					UpperCamelCase__							:    Union[str, Any]      ,					UpperCamelCase__							:    Any=None      ,					UpperCamelCase__							:    int=None      ,					UpperCamelCase__							:    Union[str, Any]=None      ,					UpperCamelCase__							:    Any=None      ,					UpperCamelCase__							:    str=None      ,					) ->   Union[str, Any]:
      if attention_mask is None:
            lowerCamelCase       :       Union[str, Any]   =						tf.cast(tf.math.not_equal(A__      ,					config.pad_token_id     )      ,					tf.inta     )
      if decoder_attention_mask is None:
            lowerCamelCase       :       Optional[int]   =						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:
            lowerCamelCase       :       Tuple   =						tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
      if decoder_head_mask is None:
            lowerCamelCase       :       Dict   =						tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      if cross_attn_head_mask is None:
            lowerCamelCase       :       Optional[int]   =						tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }


@require_tf
class       A__						(    __SCREAMING_SNAKE_CASE			,  __SCREAMING_SNAKE_CASE			,  unittest.TestCase):


     """simple docstring"""
     snake_case__						:       List[str]				    =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     snake_case__						:       int				    =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     snake_case__						:       Any				    =(
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     snake_case__						:       Any				    =True
     snake_case__						:       int				    =False
     snake_case__						:       Tuple				    =False

     def 		a__    (						self:    Optional[Any]						)->     List[Any]:
           lowerCamelCase       :       List[str]   =						TFBlenderbotModelTester(self						)
           lowerCamelCase       :       Dict   =						ConfigTester(self  ,		config_class=lowerCAmelCase__						)

     def 		a__    (						self:    List[str]						)->     Tuple:
           self.config_tester.run_common_tests()







     def 		a__    (						self:    Optional[int]						)->     Dict:
           lowerCamelCase       :       List[Any]   =						self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__						)


@require_tokenizers
@require_tf
class       A__						(    unittest.TestCase):


     """simple docstring"""
     snake_case__						:       List[Any]				    =['''My friends are cool but they eat too many carbs.''']
     snake_case__						:       Optional[int]				    ='''facebook/blenderbot-400M-distill'''

     @cached_property
     def 		a__    (						self:    Union[str, Any]						)->     Tuple:
           return BlenderbotTokenizer.from_pretrained(self.model_name						)

     @cached_property
     def 		a__    (						self:    Dict						)->     Dict:
           lowerCamelCase       :       Optional[int]   =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
           return model







     @slow
     def 		a__    (						self:    str						)->     Optional[int]:
           lowerCamelCase       :       List[Any]   =						self.tokenizer(self.src_text  ,		return_tensors="""tf"""						)
           lowerCamelCase       :       Optional[int]   =						self.model.generate(
               model_inputs.input_ids  ,		)
           lowerCamelCase       :       Optional[Any]   =						self.tokenizer.batch_decode(generated_ids.numpy()  ,		skip_special_tokens=lowerCAmelCase__						)[0]
           assert (
               generated_words
               == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
           )








import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[int]								='%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))

				print('Googling.....')

				lowerCAmelCase__			:      Optional[int]								=F"""https://www.google.com/search?q={query}&num=100"""

				lowerCAmelCase__			:      Union[str, Any]								=requests.get(
				    url,
				    headers={'User-Agent': str(UserAgent().random)},
				)

				try:
								lowerCAmelCase__			:      str								=(
								    BeautifulSoup(res.text, 'html.parser')
								    .find('div', attrs={'class': 'yuRUbf'})
								    .find('a')
								    .get('href')
								)

				except AttributeError:
								lowerCAmelCase__			:      Optional[Any]								=parse_qs(
								    BeautifulSoup(res.text, 'html.parser')
								    .find('div', attrs={'class': 'kCrYT'})
								    .find('a')
								    .get('href')
								)['url'][0]

				webbrowser.open(link)





