




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFPegasusForConditionalGeneration, TFPegasusModel





@require_tf
class 	lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       =   PegasusConfig
 lowercase_       =   {}
 lowercase_       =   "gelu"



 def __init__(							self  :		Tuple    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Optional[Any]=13    ,    _lowerCAmelCase  :		str=7    ,    _lowerCAmelCase  :		Optional[Any]=True    ,    _lowerCAmelCase  :		Tuple=False    ,    _lowerCAmelCase  :		Any=99    ,    _lowerCAmelCase  :		List[str]=32    ,    _lowerCAmelCase  :		Any=2    ,    _lowerCAmelCase  :		Optional[int]=4    ,    _lowerCAmelCase  :		str=37    ,    _lowerCAmelCase  :		str=0.1    ,    _lowerCAmelCase  :		Any=0.1    ,    _lowerCAmelCase  :		Dict=40    ,    _lowerCAmelCase  :		Optional[Any]=2    ,    _lowerCAmelCase  :		List[str]=1    ,    _lowerCAmelCase  :		Optional[Any]=0    ,    ):
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      seq_length
       SCREAMING_SNAKE_CASE_     						=      is_training
       SCREAMING_SNAKE_CASE_     						=      use_labels
       SCREAMING_SNAKE_CASE_     						=      vocab_size
       SCREAMING_SNAKE_CASE_     						=      hidden_size
       SCREAMING_SNAKE_CASE_     						=      num_hidden_layers
       SCREAMING_SNAKE_CASE_     						=      num_attention_heads
       SCREAMING_SNAKE_CASE_     						=      intermediate_size

       SCREAMING_SNAKE_CASE_     						=      hidden_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_     						=      max_position_embeddings
       SCREAMING_SNAKE_CASE_     						=      eos_token_id
       SCREAMING_SNAKE_CASE_     						=      pad_token_id
       SCREAMING_SNAKE_CASE_     						=      bos_token_id



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      ids_tensor([self.batch_size, self.seq_length - 1]    ,    self.vocab_size						)
       SCREAMING_SNAKE_CASE_     						=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)    ,    1						)
       SCREAMING_SNAKE_CASE_     						=      tf.concat([input_ids, eos_tensor]    ,    axis=1						)

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

       SCREAMING_SNAKE_CASE_     						=      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    ,    )
       SCREAMING_SNAKE_CASE_     						=      prepare_pegasus_inputs_dict(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase						)
       return config, inputs_dict




 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      TFPegasusModel(config=_lowerCAmelCase						).get_decoder()
       SCREAMING_SNAKE_CASE_     						=      inputs_dict['input_ids']

       SCREAMING_SNAKE_CASE_     						=      input_ids[:1, :]
       SCREAMING_SNAKE_CASE_     						=      inputs_dict['attention_mask'][:1, :]
       SCREAMING_SNAKE_CASE_     						=      inputs_dict['head_mask']
       SCREAMING_SNAKE_CASE_     						=      1

       # first forward pass
       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    attention_mask=_lowerCAmelCase    ,    head_mask=_lowerCAmelCase    ,    use_cache=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE_     						=      ids_tensor((self.batch_size, 3)    ,    config.vocab_size						)
       SCREAMING_SNAKE_CASE_     						=      tf.cast(ids_tensor((self.batch_size, 3)    ,    2						)    ,    tf.inta						)

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

       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    attention_mask=_lowerCAmelCase						)[0]
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      int(ids_tensor((1,)    ,    output_from_past.shape[-1]						)						)
       SCREAMING_SNAKE_CASE_     						=      output_from_no_past[:, -3:, random_slice_idx]
       SCREAMING_SNAKE_CASE_     						=      output_from_past[:, :, random_slice_idx]

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


def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     str=None      ,      __UpperCAmelCase						:     str=None      ,      __UpperCAmelCase						:     Any=None      ,      __UpperCAmelCase						:     Union[str, Any]=None      ,      __UpperCAmelCase						:     int=None      ,      ) ->			Union[str, Any]:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_     						=      tf.cast(tf.math.not_equal(__UpperCAmelCase      ,      config.pad_token_id							)      ,      tf.inta							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_     						=      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:
            SCREAMING_SNAKE_CASE_     						=      tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_     						=      tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_     						=      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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
 lowercase_       =   (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
 lowercase_       =   (
     {
         "conversational": TFPegasusForConditionalGeneration,
         "feature-extraction": TFPegasusModel,
         "summarization": TFPegasusForConditionalGeneration,
         "text2text-generation": TFPegasusForConditionalGeneration,
         "translation": TFPegasusForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 lowercase_       =   True
 lowercase_       =   False
 lowercase_       =   False



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      TFPegasusModelTester(self						)
       SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       self.config_tester.run_common_tests()




 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase						)





@require_sentencepiece
@require_tokenizers
@require_tf
class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   [
     " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
     " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
 ]
 lowercase_       =   [
     "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
     " reduce the risk of wildfires.",
     "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
 ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
 lowercase_       =   "google/pegasus-xsum"



 @cached_property
 def 				lowerCAmelCase_  (							self  :		Any						):
       return AutoTokenizer.from_pretrained(self.model_name						)



 @cached_property
 def 				lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_     						=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
       return model



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    **_lowerCAmelCase  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_     						=      self.translate_src_text(**_lowerCAmelCase						)
       assert self.expected_text == generated_words



 def 				lowerCAmelCase_  (							self  :		int    ,    **_lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.tokenizer(self.src_text    ,    **_lowerCAmelCase    ,    padding=_lowerCAmelCase    ,    return_tensors='tf'						)
       SCREAMING_SNAKE_CASE_     						=      self.model.generate(
           model_inputs.input_ids    ,    attention_mask=model_inputs.attention_mask    ,    num_beams=2    ,    use_cache=_lowerCAmelCase    ,    )
       SCREAMING_SNAKE_CASE_     						=      self.tokenizer.batch_decode(generated_ids.numpy()    ,    skip_special_tokens=_lowerCAmelCase						)
       return generated_words




 @slow
 def 				lowerCAmelCase_  (							self  :		Any						):
       self._assert_generated_batch_equal_expected()





from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str = "isbn/0140328726"							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      olid.strip().strip('/'							)  # Remove leading/trailing whitespace & slashes
      if new_olid.count('/'							) != 1:
            SCREAMING_SNAKE_CASE_     						=      f"{olid} is not a valid Open Library olid"
            raise ValueError(__UpperCAmelCase							)
      return requests.get(f"https://openlibrary.org/{new_olid}.json"							).json()

def  UpperCAmelCase_							(				__UpperCAmelCase						:     dict							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      {
          'title': 'Title',
          'publish_date': 'Publish date',
          'authors': 'Authors',
          'number_of_pages': 'Number of pages:',
          'first_sentence': 'First sentence',
          'isbn_10': 'ISBN (10)',
          'isbn_13': 'ISBN (13)',
      }
      SCREAMING_SNAKE_CASE_     						=      {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      SCREAMING_SNAKE_CASE_     						=      [
          get_openlibrary_data(author['key']							)['name'] for author in data['Authors']
      ]
      SCREAMING_SNAKE_CASE_     						=      data['First sentence']['value']
      for key, value in data.items():
            if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      ', '.join(__UpperCAmelCase							)
      return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   while True:
      lowerCamelCase__			:       Optional[Any]         =							input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
      if isbn.lower() in ("", "q", "quit", "exit", "stop"):
         break

      if len(isbn) not in (10, 13) or not isbn.isdigit():
         print(f'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
         continue

      print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')

      try:
         lowerCamelCase__			:       Union[str, Any]         =							summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
         print('\n'.join(f'''{key}: {value}''' for key, value in book_summary.items()))
      except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
         print(f'''Sorry, there are no results for ISBN: {isbn}.''')