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

				from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class        _UpperCAmelCase:
		lowercase__         = PegasusConfig
		lowercase__         = {}
		lowercase__         = 'gelu'



		def __init__(   self							,						__a							,						__a=13							,						__a=7							,						__a=True							,						__a=False							,						__a=99							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a=0.1							,						__a=0.1							,						__a=20							,						__a=2							,						__a=1							,						__a=0							,						)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    seq_length
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    vocab_size
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size

							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    max_position_embeddings
							_UpperCamelCase									=    eos_token_id
							_UpperCamelCase									=    pad_token_id
							_UpperCamelCase									=    bos_token_id



		def 		UpperCAmelCase				(   self)			->			int:






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

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

							_UpperCamelCase									=    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							,						)
							_UpperCamelCase									=    prepare_pegasus_inputs_dict(__a							,						__a							,						__a)
							return config, inputs_dict



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    20
							_UpperCamelCase									=    model_class_name(__a)

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

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

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

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

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

							_UpperCamelCase									=    model.decode(__a							,						__a)

							_UpperCamelCase									=    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				(   self							,						__a							,						__a							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    20
							_UpperCamelCase									=    model_class_name(__a)

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

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

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

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

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

							_UpperCamelCase									=    model.decode(__a							,						__a							,						decoder_attention_mask=__a)

							_UpperCamelCase									=    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__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case=None,	__snake_case=None,	)				->						Union[str, Any]:


					"""simple docstring"""





					if attention_mask is None:
										_UpperCamelCase									=    np.not_equal(__snake_case,	config.pad_token_id      ).astype(np.inta      )
					if decoder_attention_mask is None:
										_UpperCamelCase									=    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(       lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (
		        FlaxPegasusForConditionalGeneration,
		        FlaxPegasusModel,
		    )
		    if is_flax_available()
		    else ()
		)
		lowercase__         = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
		lowercase__         = True
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    FlaxPegasusModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a)



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






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



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(__a							,						__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    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(__a							,						__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__):
																	_UpperCamelCase									=    self._prepare_for_class(__a							,						__a)
																	_UpperCamelCase									=    model_class(__a)

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

																	with self.subTest('''JIT Enabled'''):
																						_UpperCamelCase									=    encode_jitted(**__a).to_tuple()

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

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



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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

																	_UpperCamelCase									=    {
																	    '''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(__a							,						__a							,						__a):
																						return model.decode(
																						    decoder_input_ids=__a							,						decoder_attention_mask=__a							,						encoder_outputs=__a							,						)

																	with self.subTest('''JIT Enabled'''):
																						_UpperCamelCase									=    decode_jitted(**__a).to_tuple()

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

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



		@slow
		def 		UpperCAmelCase				(   self)			->			int:






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




		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
							_UpperCamelCase									=    PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')

							_UpperCamelCase									=    [
							    ''' 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!" ''',
							]

							_UpperCamelCase									=    [
							    '''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.''',
							]

							_UpperCamelCase									=    tokenizer(__a							,						return_tensors='''np'''							,						truncation=__a							,						max_length=5_12							,						padding=__a)
							_UpperCamelCase									=    model.generate(**__a							,						num_beams=2).sequences
							_UpperCamelCase									=    tokenizer.batch_decode(__a							,						skip_special_tokens=__a)
							assert tgt_text == decoded













import numpy as np


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      (0, 0)
        A_       :					Dict        =      None
        A_       :					Optional[Any]        =      0
        A_       :					Optional[Any]        =      0
        A_       :					List[Any]        =      0



     def __eq__(				self							:int      ,   snake_case							:str	):





        '''simple docstring'''




        return self.position == cell.position





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        print(self.position	)




class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Dict      ,   snake_case							:Optional[Any]=(5, 5)	):





        '''simple docstring'''




        A_       :					Optional[int]        =      np.zeros(snake_case	)
        A_       :					Tuple        =      world_size[0]
        A_       :					Optional[Any]        =      world_size[1]



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        print(self.w	)





     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:int	):





        '''simple docstring'''




        A_       :					Optional[int]        =      [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        A_       :					Union[str, Any]        =      cell.position[0]
        A_       :					int        =      cell.position[1]
        A_       :					Optional[int]        =      []
        for n in neughbour_cord:
           A_       :					List[str]        =      current_x + n[0]
           A_       :					Optional[Any]        =      current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
              A_       :					List[Any]        =      Cell()
              A_       :					Tuple        =      (x, y)
              A_       :					List[Any]        =      cell
              neighbours.append(snake_case	)
        return neighbours




def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			str						)			->    str:
   A_       :					Optional[Any]        =      []
   A_       :					Tuple        =      []
   _open.append(_lowerCAmelCase						)

   while _open:
      A_       :					List[Any]        =      np.argmin([n.f for n in _open]						)
      A_       :					Optional[Any]        =      _open[min_f]
      _closed.append(_open.pop(_lowerCAmelCase						)						)
      if current == goal:
         break
      for n in world.get_neigbours(_lowerCAmelCase						):
         for c in _closed:
            if c == n:
               continue
         A_       :					int        =      current.g + 1
         A_   ,    A_       :					List[str]        =      n.position
         A_   ,    A_       :					Any        =      goal.position
         A_       :					Optional[Any]        =      (ya - ya) ** 2 + (xa - xa) ** 2
         A_       :					Optional[Any]        =      n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(_lowerCAmelCase						)
   A_       :					Optional[int]        =      []
   while current.parent is not None:
      path.append(current.position						)
      A_       :					List[Any]        =      current.parent
   path.append(current.position						)
   return path[::-1]


if __name__ == "__main__":
 _lowerCAmelCase				:   Union[str, Any]						       =						Gridworld()
 # Start position and goal
 _lowerCAmelCase				:   Tuple						       =						Cell()
 _lowerCAmelCase				:   str						       =						(0, 0)
 _lowerCAmelCase				:   Union[str, Any]						       =						Cell()
 _lowerCAmelCase				:   List[str]						       =						(4, 4)
 print(F'''path from {start.position} to {goal.position}''')
 _lowerCAmelCase				:   Union[str, Any]						       =						astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  _lowerCAmelCase				:   Any						       =						1
 print(world.w)
