


'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class A__       :
      __UpperCamelCase       :  Tuple	       =							BlenderbotSmallConfig
      __UpperCamelCase       :  int	       =							{}
      __UpperCamelCase       :  Union[str, Any]	       =							"gelu"






      def __init__( self			:Any     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:Any=1_3     ,						SCREAMING_SNAKE_CASE			:Optional[int]=7     ,						SCREAMING_SNAKE_CASE			:Any=True     ,						SCREAMING_SNAKE_CASE			:Any=False     ,						SCREAMING_SNAKE_CASE			:str=9_9     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=3_2     ,						SCREAMING_SNAKE_CASE			:Optional[int]=2     ,						SCREAMING_SNAKE_CASE			:List[Any]=4     ,						SCREAMING_SNAKE_CASE			:List[Any]=3_7     ,						SCREAMING_SNAKE_CASE			:List[Any]=0.1     ,						SCREAMING_SNAKE_CASE			:Optional[int]=0.1     ,						SCREAMING_SNAKE_CASE			:Optional[int]=2_0     ,						SCREAMING_SNAKE_CASE			:Optional[int]=2     ,						SCREAMING_SNAKE_CASE			:List[str]=1     ,						SCREAMING_SNAKE_CASE			:Tuple=0     ,						)       ->					str:




             '''simple docstring'''
             _a    : Optional[Any]     					=parent
             _a    : Dict     					=batch_size
             _a    : Any     					=seq_length
             _a    : List[Any]     					=is_training
             _a    : Any     					=use_labels
             _a    : str     					=vocab_size
             _a    : List[Any]     					=hidden_size
             _a    : Optional[Any]     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : str     					=intermediate_size

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






      def __UpperCAmelCase       ( self			:Optional[Any]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Any     					=ids_tensor([self.batch_size, self.seq_length - 1]     ,						self.vocab_size	)
             _a    : Union[str, Any]     					=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)     ,						1	)
             _a    : Any     					=tf.concat([input_ids, eos_tensor]     ,						axis=1	)

             _a    : Tuple     					=ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size	)

             _a    : Optional[int]     					=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     ,						)
             _a    : Union[str, Any]     					=prepare_blenderbot_small_inputs_dict(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
             return config, inputs_dict






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:Any	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Any     					=TFBlenderbotSmallModel(config=SCREAMING_SNAKE_CASE	).get_decoder()
             _a    : Optional[int]     					=inputs_dict["""input_ids"""]

             _a    : Optional[int]     					=input_ids[:1, :]
             _a    : List[str]     					=inputs_dict["""attention_mask"""][:1, :]
             _a    : Optional[int]     					=inputs_dict["""head_mask"""]
             _a    : Optional[Any]     					=1

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

             _a					,    _a    : List[Any]     					=outputs.to_tuple()

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

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

             _a    : str     					=model(SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE	)[0]
             _a    : Tuple     					=model(SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE     ,						past_key_values=SCREAMING_SNAKE_CASE	)[0]

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

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

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


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Dict    ,_UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	List[Any]=None    ,_UpperCAmelCase	:	Union[str, Any]=None    ,_UpperCAmelCase	:	Dict=None    ,_UpperCAmelCase	:	Tuple=None    ,_UpperCAmelCase	:	Optional[int]=None    ,)  ->			Tuple:
       if attention_mask is None:
              _a    : List[str]     					=tf.cast(tf.math.not_equal(_UpperCAmelCase    ,config.pad_token_id  )    ,tf.inta  )
       if decoder_attention_mask is None:
              _a    : Any     					=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    : int     					=tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
       if decoder_head_mask is None:
              _a    : Any     					=tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
       if cross_attn_head_mask is None:
              _a    : Any     					=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__       (						UpperCAmelCase__ ,     UpperCAmelCase__ ,     unittest.TestCase      ):
      __UpperCamelCase       :  str	       =							(
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      __UpperCamelCase       :  Any	       =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      __UpperCamelCase       :  int	       =							(
          {
              "conversational": TFBlenderbotSmallForConditionalGeneration,
              "feature-extraction": TFBlenderbotSmallModel,
              "summarization": TFBlenderbotSmallForConditionalGeneration,
              "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
              "translation": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      __UpperCamelCase       :  List[Any]	       =							True
      __UpperCamelCase       :  Optional[int]	       =							False
      __UpperCamelCase       :  List[Any]	       =							False






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




             '''simple docstring'''
             _a    : Tuple     					=TFBlenderbotSmallModelTester(self	)
             _a    : Optional[Any]     					=ConfigTester(self     ,						config_class=SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Any	)       ->					Tuple:




             '''simple docstring'''
             self.config_tester.run_common_tests()






      def __UpperCAmelCase       ( self			:Optional[Any]	)       ->					Tuple:




             '''simple docstring'''
             _a    : List[Any]     					=self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE	)







@require_tokenizers
@require_tf
class A__       (						unittest.TestCase      ):
      __UpperCamelCase       :  Union[str, Any]	       =							[
          "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
          "  i'm going to throw up.\nand why is that?"
      ]
      __UpperCamelCase       :  Union[str, Any]	       =							"facebook/blenderbot_small-90M"






      @cached_property
      def __UpperCAmelCase       ( self			:Union[str, Any]	)       ->					Any:




             '''simple docstring'''
             # use "old" tokenizer here because of bug when downloading new tokenizer
             return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)






      @cached_property
      def __UpperCAmelCase       ( self			:int	)       ->					int:




             '''simple docstring'''
             _a    : Tuple     					=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
             return model






      @slow
      def __UpperCAmelCase       ( self			:List[Any]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : Union[str, Any]     					=self.tokenizer(self.src_text     ,						return_tensors="""tf"""	)
             _a    : List[str]     					=self.model.generate(
                 model_inputs.input_ids     ,						attention_mask=model_inputs.attention_mask     ,						num_beams=2     ,						use_cache=SCREAMING_SNAKE_CASE     ,						)
             _a    : str     					=self.tokenizer.batch_decode(generated_ids.numpy()     ,						skip_special_tokens=SCREAMING_SNAKE_CASE	)[0]
             assert generated_words in (
                 "i don't know. i just feel like i'm going to throw up. it's not fun.",
                 "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
                 "i'm not sure. i just feel like i've been in a bad situation.",
             )






'''simple docstring'''







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   A__:    Union[str, Any]					    =       input('''Enter image url: ''').strip()
   print(F"Downloading image from {url} ...")
   A__:    Tuple					    =       BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   A__:    Union[str, Any]					    =       soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   A__:    List[Any]					    =       requests.get(image_url).content
   A__:    List[str]					    =       F"{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"Done. Image saved to disk as {file_name}.")


