'''simple docstring'''

import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  __a:			int	       =       """platform"""
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   PegasusConfig
       SCREAMING_SNAKE_CASE 		=   {}
       SCREAMING_SNAKE_CASE 		=   "gelu"

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=13     ,       __lowerCAmelCase=7     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=99     ,       __lowerCAmelCase=32     ,       __lowerCAmelCase=5     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=37     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=20     ,       __lowerCAmelCase=2     ,       __lowerCAmelCase=1     ,       __lowerCAmelCase=0     ,       )  ->  List[Any]:
        lowercase__    :   List[str]						= parent
        lowercase__    :   List[str]						= batch_size
        lowercase__    :   List[str]						= seq_length
        lowercase__    :   Union[str, Any]						= is_training
        lowercase__    :   str						= use_labels
        lowercase__    :   Any						= vocab_size
        lowercase__    :   Optional[Any]						= hidden_size
        lowercase__    :   Any						= num_hidden_layers
        lowercase__    :   Union[str, Any]						= num_attention_heads
        lowercase__    :   Optional[int]						= intermediate_size

        lowercase__    :   Optional[Any]						= hidden_dropout_prob
        lowercase__    :   Union[str, Any]						= attention_probs_dropout_prob
        lowercase__    :   Any						= max_position_embeddings
        lowercase__    :   int						= eos_token_id
        lowercase__    :   List[str]						= pad_token_id
        lowercase__    :   Optional[int]						= bos_token_id

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   Optional[int]						= ids_tensor([self.batch_size, self.seq_length - 1]     ,       self.vocab_size			).clip(3     ,       self.vocab_size			)
        lowercase__    :   Optional[int]						= np.expand_dims(np.array([self.eos_token_id] * self.batch_size			)     ,       1			)
        lowercase__    :   str						= np.concatenate([input_ids, eos_tensor]     ,       axis=1			)

        lowercase__    :   Optional[Any]						= ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size			)

        lowercase__    :   List[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     ,       )
        lowercase__    :   int						= prepare_pegasus_inputs_dict(lowercase_     ,       lowercase_     ,       lowercase_			)
        return config, inputs_dict

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        lowercase__    :   Optional[Any]						= 20
        lowercase__    :   Tuple						= model_class_name(lowercase_			)

        lowercase__    :   Union[str, Any]						= model.encode(inputs_dict['''input_ids''']			)

        lowercase__			,							lowercase__    :   Dict						= (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        lowercase__    :   Union[str, Any]						= model.init_cache(decoder_input_ids.shape[0]     ,       lowercase_     ,       lowercase_			)
        lowercase__    :   Optional[int]						= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,       dtype='''i4'''			)

        lowercase__    :   Dict						= jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]     ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,       )
        lowercase__    :   Dict						= model.decode(
            decoder_input_ids[:, :-1]     ,       lowercase_     ,       decoder_attention_mask=lowercase_     ,       past_key_values=lowercase_     ,       decoder_position_ids=lowercase_     ,       )

        lowercase__    :   List[Any]						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,       dtype='''i4'''			)
        lowercase__    :   List[Any]						= model.decode(
            decoder_input_ids[:, -1:]     ,       lowercase_     ,       decoder_attention_mask=lowercase_     ,       past_key_values=outputs_cache.past_key_values     ,       decoder_position_ids=lowercase_     ,       )

        lowercase__    :   List[Any]						= model.decode(lowercase_     ,       lowercase_			)

        lowercase__    :   Tuple						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
        self.parent.assertTrue(diff < 1E-3     ,       msg=F"""Max diff is {diff}"""			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   Union[str, Any]						= 20
        lowercase__    :   Union[str, Any]						= model_class_name(lowercase_			)

        lowercase__    :   Optional[Any]						= model.encode(inputs_dict['''input_ids''']			)

        lowercase__			,							lowercase__    :   Optional[int]						= (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        lowercase__    :   List[Any]						= jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
            ]     ,       axis=-1     ,       )

        lowercase__    :   Union[str, Any]						= model.init_cache(decoder_input_ids.shape[0]     ,       lowercase_     ,       lowercase_			)
        lowercase__    :   List[Any]						= jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]     ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,       )

        lowercase__    :   int						= model.decode(
            decoder_input_ids[:, :-1]     ,       lowercase_     ,       decoder_attention_mask=lowercase_     ,       past_key_values=lowercase_     ,       decoder_position_ids=lowercase_     ,       )
        lowercase__    :   Tuple						= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,       dtype='''i4'''			)
        lowercase__    :   Optional[Any]						= model.decode(
            decoder_input_ids[:, -1:]     ,       lowercase_     ,       past_key_values=outputs_cache.past_key_values     ,       decoder_attention_mask=lowercase_     ,       decoder_position_ids=lowercase_     ,       )

        lowercase__    :   Dict						= model.decode(lowercase_     ,       lowercase_     ,       decoder_attention_mask=lowercase_			)

        lowercase__    :   List[str]						= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
        self.parent.assertTrue(diff < 1E-3     ,       msg=F"""Max diff is {diff}"""			)


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=None		,					UpperCAmelCase=None		,					):

 if attention_mask is None:
  lowercase__    :   int						= np.not_equal(a__		,					config.pad_token_id					).astype(np.inta					)
 if decoder_attention_mask is None:
  lowercase__    :   Dict						= np.concatenate(
      [
          np.ones(decoder_input_ids[:, :1].shape		,					dtype=np.inta					),
          np.not_equal(decoder_input_ids[:, 1:]		,					config.pad_token_id					).astype(np.inta					),
      ]		,					axis=-1		,					)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
 }





@require_flax
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   (
           (
               FlaxPegasusForConditionalGeneration,
               FlaxPegasusModel,
           )
           if is_flax_available()
           else ()
       )
       SCREAMING_SNAKE_CASE 		=   (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[Any]						= FlaxPegasusModelTester(self			)
        lowercase__    :   List[Any]						= ConfigTester(self     ,       config_class=lowercase_			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        self.config_tester.run_common_tests()

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__			,							lowercase__    :   Tuple						= self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(lowercase_     ,       lowercase_     ,       lowercase_			)

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__			,							lowercase__    :   List[str]						= self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_     ,       lowercase_     ,       lowercase_			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   Optional[Any]						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
          lowercase__    :   Optional[Any]						= self._prepare_for_class(lowercase_     ,       lowercase_			)
          lowercase__    :   Optional[Any]						= model_class(lowercase_			)

          @jax.jit
          def encode_jitted(__lowerCAmelCase     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase			):
           return model.encode(input_ids=lowercase_     ,       attention_mask=lowercase_			)

          with self.subTest('''JIT Enabled'''			):
           lowercase__    :   Union[str, Any]						= encode_jitted(**lowercase_			).to_tuple()

          with self.subTest('''JIT Disabled'''			):
           with jax.disable_jit():
            lowercase__    :   Optional[Any]						= encode_jitted(**lowercase_			).to_tuple()

          self.assertEqual(len(lowercase_			)     ,       len(lowercase_			)			)
          for jitted_output, output in zip(lowercase_     ,       lowercase_			):
           self.assertEqual(jitted_output.shape     ,       output.shape			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__			,							lowercase__    :   Dict						= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__			):
          lowercase__    :   Any						= model_class(lowercase_			)
          lowercase__    :   int						= model.encode(inputs_dict['''input_ids''']     ,       inputs_dict['''attention_mask''']			)

          lowercase__    :   List[str]						= {
              '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
              '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
              '''encoder_outputs''': encoder_outputs,
          }

          @jax.jit
          def decode_jitted(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			):
           return model.decode(
               decoder_input_ids=lowercase_     ,       decoder_attention_mask=lowercase_     ,       encoder_outputs=lowercase_     ,       )

          with self.subTest('''JIT Enabled'''			):
           lowercase__    :   Any						= decode_jitted(**lowercase_			).to_tuple()

          with self.subTest('''JIT Disabled'''			):
           with jax.disable_jit():
            lowercase__    :   Optional[Any]						= decode_jitted(**lowercase_			).to_tuple()

          self.assertEqual(len(lowercase_			)     ,       len(lowercase_			)			)
          for jitted_output, output in zip(lowercase_     ,       lowercase_			):
           self.assertEqual(jitted_output.shape     ,       output.shape			)

       @slow
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:







        for model_class_name in self.all_model_classes:
         lowercase__    :   Optional[Any]						= model_class_name.from_pretrained('''google/pegasus-large'''     ,       from_pt=lowercase_			)
         lowercase__    :   Any						= np.ones((1, 1)			)
         lowercase__    :   int						= model(lowercase_			)
         self.assertIsNotNone(lowercase_			)


       @slow
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   int						= FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''			)
        lowercase__    :   Dict						= PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''			)

        lowercase__    :   Tuple						= [
            ''' 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__    :   Dict						= [
            '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
            '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
        ]

        lowercase__    :   Optional[Any]						= tokenizer(lowercase_     ,       return_tensors='''np'''     ,       truncation=lowercase_     ,       max_length=512     ,       padding=lowercase_			)
        lowercase__    :   Union[str, Any]						= model.generate(**lowercase_     ,       num_beams=2			).sequences
        lowercase__    :   List[str]						= tokenizer.batch_decode(lowercase_     ,       skip_special_tokens=lowercase_			)
        assert tgt_text == decoded





'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase					):
 if not all(x.isalpha() for x in string					):
  raise ValueError('''String must only contain alphabetic characters.'''					)

 lowercase__    :   Tuple						= sorted(string.lower()					)
 return len(UpperCAmelCase					) == len(set(UpperCAmelCase					)					)


if __name__ == "__main__":
  __a:			Union[str, Any]	       =       input("""Enter a string """).strip()

  __a:			Tuple	       =       is_isogram(input_str)
  print(F'{input_str} is {"an" if isogram else "not an"} isogram.')

