






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
     UpperCAmelCase__				   =		"platform"
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class 		lowercase_					:






      '''simple docstring'''




      __snake_case       =  PegasusConfig
      __snake_case       =  {}
      __snake_case       =  '''gelu'''







      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						Dict=13 ,					__UpperCAmelCase      :						Any=7 ,					__UpperCAmelCase      :						List[Any]=True ,					__UpperCAmelCase      :						List[str]=False ,					__UpperCAmelCase      :						Optional[int]=99 ,					__UpperCAmelCase      :						str=32 ,					__UpperCAmelCase      :						str=5 ,					__UpperCAmelCase      :						List[str]=4 ,					__UpperCAmelCase      :						List[Any]=37 ,					__UpperCAmelCase      :						Optional[int]=0.1 ,					__UpperCAmelCase      :						Tuple=0.1 ,					__UpperCAmelCase      :						str=20 ,					__UpperCAmelCase      :						List[str]=2 ,					__UpperCAmelCase      :						List[Any]=1 ,					__UpperCAmelCase      :						List[Any]=0 ,					)    ->Union[str, Any]:




       """simple docstring"""


       a		  =      parent
       a		  =      batch_size
       a		  =      seq_length
       a		  =      is_training
       a		  =      use_labels
       a		  =      vocab_size
       a		  =      hidden_size
       a		  =      num_hidden_layers
       a		  =      num_attention_heads
       a		  =      intermediate_size

       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      max_position_embeddings
       a		  =      eos_token_id
       a		  =      pad_token_id
       a		  =      bos_token_id







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->int:




       """simple docstring"""


       a		  =      ids_tensor([self.batch_size, self.seq_length - 1] ,					self.vocab_size  ).clip(3 ,					self.vocab_size  )
       a		  =      np.expand_dims(np.array([self.eos_token_id] * self.batch_size  ) ,					1  )
       a		  =      np.concatenate([input_ids, eos_tensor] ,					axis=1  )

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

       a		  =      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		  =      prepare_pegasus_inputs_dict(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       return config, inputs_dict







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




       """simple docstring"""


       a		  =      20
       a		  =      model_class_name(__UpperCAmelCase  )

       a		  =      model.encode(inputs_dict['''input_ids''']  )

       a  ,     a		  =      (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       a		  =      model.init_cache(decoder_input_ids.shape[0] ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,					dtype='''i4'''  )

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

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

       a		  =      model.decode(__UpperCAmelCase ,					__UpperCAmelCase  )

       a		  =      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      :						Dict ,					__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						Tuple  )    ->str:




       """simple docstring"""


       a		  =      20
       a		  =      model_class_name(__UpperCAmelCase  )

       a		  =      model.encode(inputs_dict['''input_ids''']  )

       a  ,     a		  =      (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       a		  =      jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
           ] ,					axis=-1 ,					)

       a		  =      model.init_cache(decoder_input_ids.shape[0] ,					__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :] ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,					)

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

       a		  =      model.decode(__UpperCAmelCase ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase  )

       a		  =      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 						_a    (      a :List[Any]	,							a :Optional[int]	,							a :int	,							a :str=None	,							a :int=None	,							)							->			int:
 if attention_mask is None:
  a		  =      np.not_equal(a	,							config.pad_token_id ).astype(np.inta )
 if decoder_attention_mask is None:
  a		  =      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 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




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







      def       __lowerCAmelCase					(      self      :						str  )    ->List[Any]:




       """simple docstring"""


       a		  =      FlaxPegasusModelTester(self  )
       a		  =      ConfigTester(self ,					config_class=__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[int]:




       """simple docstring"""


       self.config_tester.run_common_tests()







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->int:




       """simple docstring"""


       a  ,     a		  =      self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )







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




       """simple docstring"""


       a  ,     a		  =      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(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )







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




       """simple docstring"""


       a  ,     a		  =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
         a		  =      self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase  )
         a		  =      model_class(__UpperCAmelCase  )

         @jax.jit
         def encode_jitted(__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						str=None ,					**__UpperCAmelCase      :						List[Any]  ):
          return model.encode(input_ids=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase  )

         with self.subTest('''JIT Enabled'''  ):
          a		  =      encode_jitted(**__UpperCAmelCase  ).to_tuple()

         with self.subTest('''JIT Disabled'''  ):
          with jax.disable_jit():
           a		  =      encode_jitted(**__UpperCAmelCase  ).to_tuple()

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







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




       """simple docstring"""


       a  ,     a		  =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
         a		  =      model_class(__UpperCAmelCase  )
         a		  =      model.encode(inputs_dict['''input_ids'''] ,					inputs_dict['''attention_mask''']  )

         a		  =      {
             '''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(__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Tuple  ):
          return model.decode(
              decoder_input_ids=__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					encoder_outputs=__UpperCAmelCase ,					)

         with self.subTest('''JIT Enabled'''  ):
          a		  =      decode_jitted(**__UpperCAmelCase  ).to_tuple()

         with self.subTest('''JIT Disabled'''  ):
          with jax.disable_jit():
           a		  =      decode_jitted(**__UpperCAmelCase  ).to_tuple()

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







      @slow
      def       __lowerCAmelCase					(      self      :						str  )    ->List[str]:




       """simple docstring"""


       for model_class_name in self.all_model_classes:
        a		  =      model_class_name.from_pretrained('''google/pegasus-large''' ,					from_pt=__UpperCAmelCase  )
        a		  =      np.ones((1, 1)  )
        a		  =      model(__UpperCAmelCase  )
        self.assertIsNotNone(__UpperCAmelCase  )







      @slow
      def       __lowerCAmelCase					(      self      :						str  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''  )
       a		  =      PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''  )

       a		  =      [
           ''' 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!" ''',
       ]

       a		  =      [
           '''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.''',
       ]

       a		  =      tokenizer(__UpperCAmelCase ,					return_tensors='''np''' ,					truncation=__UpperCAmelCase ,					max_length=512 ,					padding=__UpperCAmelCase  )
       a		  =      model.generate(**__UpperCAmelCase ,					num_beams=2  ).sequences
       a		  =      tokenizer.batch_decode(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase  )
       assert tgt_text == decoded






class        _a							:

							def __init__(							self:	Any							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      ''''''
									lowercase__            =      ''''''
									lowercase__            =      []

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									if m == -1:
											return n + 1
									elif n == -1:
											return m + 1
									elif self.dp[m][n] > -1:
											return self.dp[m][n]
									else:
											if self.worda[m] == self.worda[n]:
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						n - 1							)
											else:
													lowercase__            =      self.__min_dist_top_down_dp(UpperCamelCase_    ,						n - 1							)
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						UpperCamelCase_							)
													lowercase__            =      self.__min_dist_top_down_dp(m - 1    ,						n - 1							)
													lowercase__            =      1 + min(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

											return self.dp[m][n]

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									lowercase__            =      worda
									lowercase__            =      worda
									lowercase__            =      [[-1 for _ in range(len(UpperCamelCase_							)							)] for _ in range(len(UpperCamelCase_							)							)]

									return self.__min_dist_top_down_dp(len(UpperCamelCase_							) - 1    ,						len(UpperCamelCase_							) - 1							)


							def       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									lowercase__            =      worda
									lowercase__            =      worda
									lowercase__            =      len(UpperCamelCase_							)
									lowercase__            =      len(UpperCamelCase_							)
									lowercase__            =      [[0 for _ in range(n + 1							)] for _ in range(m + 1							)]

									for i in range(m + 1							):
											for j in range(n + 1							):
													if i == 0:  # first string is empty
															lowercase__            =      j
													elif j == 0:  # second string is empty
															lowercase__            =      i
													elif worda[i - 1] == worda[j - 1]:  # last characters are equal
															lowercase__            =      self.dp[i - 1][j - 1]
													else:
															lowercase__            =      self.dp[i][j - 1]
															lowercase__            =      self.dp[i - 1][j]
															lowercase__            =      self.dp[i - 1][j - 1]
															lowercase__            =      1 + min(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)
									return self.dp[m][n]


if __name__ == "__main__":
			lowerCAmelCase       =							EditDistance()

			print('****************** Testing Edit Distance DP Algorithm ******************')
			print()

			lowerCAmelCase       =							input('Enter the first string: ').strip()
			lowerCAmelCase       =							input('Enter the second string: ').strip()

			print()
			print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
			print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
			print()
			print('*************** End of Testing Edit Distance DP Algorithm ***************')


