

'''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
		__snake_case :	int      						=			'platform'
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class lowerCamelCase :



			'''simple docstring'''






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


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





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

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


			def lowercase__						(      self       :						Tuple )			->		int:





									'''simple docstring'''
									A__     :   List[str]      =ids_tensor([self.batch_size, self.seq_length - 1]		,			self.vocab_size ).clip(3		,			self.vocab_size )
									A__     :   Optional[int]      =np.expand_dims(np.array([self.eos_token_id] * self.batch_size )		,			1 )
									A__     :   Optional[Any]      =np.concatenate([input_ids, eos_tensor]		,			axis=1 )

									A__     :   Optional[int]      =ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size )

									A__     :   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		,			)
									A__     :   str      =prepare_pegasus_inputs_dict(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase )
									return config, inputs_dict


			def lowercase__						(      self       :						Any		,			lowerCAmelCase_       :						List[Any]		,			lowerCAmelCase_       :						Union[str, Any]		,			lowerCAmelCase_       :						Union[str, Any] )			->		List[Any]:





									'''simple docstring'''
									A__     :   int      =20
									A__     :   Tuple      =model_class_name(__UpperCAmelCase )

									A__     :   Optional[Any]      =model.encode(inputs_dict["""input_ids"""] )

									A__    ,						A__     :   Dict      =(
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									A__     :   str      =model.init_cache(decoder_input_ids.shape[0]		,			__UpperCAmelCase		,			__UpperCAmelCase )
									A__     :   Any      =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,			dtype="""i4""" )

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

									A__     :   Any      =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,			dtype="""i4""" )
									A__     :   List[str]      =model.decode(
									    decoder_input_ids[:, -1:]		,			__UpperCAmelCase		,			decoder_attention_mask=__UpperCAmelCase		,			past_key_values=outputs_cache.past_key_values		,			decoder_position_ids=__UpperCAmelCase		,			)

									A__     :   Tuple      =model.decode(__UpperCAmelCase		,			__UpperCAmelCase )

									A__     :   Any      =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 lowercase__						(      self       :						Dict		,			lowerCAmelCase_       :						Union[str, Any]		,			lowerCAmelCase_       :						Optional[Any]		,			lowerCAmelCase_       :						Tuple )			->		str:





									'''simple docstring'''
									A__     :   Optional[Any]      =20
									A__     :   str      =model_class_name(__UpperCAmelCase )

									A__     :   str      =model.encode(inputs_dict["""input_ids"""] )

									A__    ,						A__     :   Dict      =(
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

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

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

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

									A__     :   Dict      =model.decode(__UpperCAmelCase		,			__UpperCAmelCase		,			decoder_attention_mask=__UpperCAmelCase )

									A__     :   Optional[Any]      =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     :		List[Any],		__snake_case     :		Optional[int],		__snake_case     :		int,		__snake_case     :		str=None,		__snake_case     :		int=None,		)       ->							int:






						"""simple docstring"""





						if attention_mask is None:
												A__     :   List[Any]      =np.not_equal(__snake_case,		config.pad_token_id       ).astype(np.inta       )
						if decoder_attention_mask is None:
												A__     :   List[Any]      =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 lowerCamelCase (  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 lowercase__						(      self       :						str )			->		List[Any]:





									'''simple docstring'''
									A__     :   Dict      =FlaxPegasusModelTester(self )
									A__     :   str      =ConfigTester(self		,			config_class=__UpperCAmelCase )


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





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


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





									'''simple docstring'''
									A__    ,						A__     :   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(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase )


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





									'''simple docstring'''
									A__    ,						A__     :   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(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase )


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





									'''simple docstring'''
									A__    ,						A__     :   int      =self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__ ):
																					A__     :   Union[str, Any]      =self._prepare_for_class(__UpperCAmelCase		,			__UpperCAmelCase )
																					A__     :   int      =model_class(__UpperCAmelCase )

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

																					with self.subTest("""JIT Enabled""" ):
																											A__     :   List[Any]      =encode_jitted(**__UpperCAmelCase ).to_tuple()

																					with self.subTest("""JIT Disabled""" ):
																											with jax.disable_jit():
																																	A__     :   Optional[Any]      =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 lowercase__						(      self       :						List[Any] )			->		Optional[Any]:





									'''simple docstring'''
									A__    ,						A__     :   Tuple      =self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__ ):
																					A__     :   Optional[Any]      =model_class(__UpperCAmelCase )
																					A__     :   str      =model.encode(inputs_dict["""input_ids"""]		,			inputs_dict["""attention_mask"""] )

																					A__     :   Dict      ={
																					    """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_       :						Tuple		,			lowerCAmelCase_       :						Tuple		,			lowerCAmelCase_       :						Tuple ):
																											return model.decode(
																											    decoder_input_ids=__UpperCAmelCase		,			decoder_attention_mask=__UpperCAmelCase		,			encoder_outputs=__UpperCAmelCase		,			)

																					with self.subTest("""JIT Enabled""" ):
																											A__     :   int      =decode_jitted(**__UpperCAmelCase ).to_tuple()

																					with self.subTest("""JIT Disabled""" ):
																											with jax.disable_jit():
																																	A__     :   Optional[Any]      =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 lowercase__						(      self       :						str )			->		List[str]:





									'''simple docstring'''


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



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





									'''simple docstring'''
									A__     :   Tuple      =FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""" )
									A__     :   Union[str, Any]      =PegasusTokenizer.from_pretrained("""google/pegasus-xsum""" )

									A__     :   List[Any]      =[
									    """ 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__     :   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.""",
									]

									A__     :   Optional[int]      =tokenizer(__UpperCAmelCase		,			return_tensors="""np"""		,			truncation=__UpperCAmelCase		,			max_length=5_12		,			padding=__UpperCAmelCase )
									A__     :   str      =model.generate(**__UpperCAmelCase		,			num_beams=2 ).sequences
									A__     :   List[str]      =tokenizer.batch_decode(__UpperCAmelCase		,			skip_special_tokens=__UpperCAmelCase )
									assert tgt_text == decoded









from __future__ import annotations

import time

import numpy as np

UpperCAmelCase__				   =		[8, 5, 9, 7]
UpperCAmelCase__				   =		[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCAmelCase__				   =		[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class 		lowercase_					:






      '''simple docstring'''




      def __init__(      self      :						Optional[int] ,					__UpperCAmelCase      :						list[int] ,					__UpperCAmelCase      :						list[list[int]] ,					__UpperCAmelCase      :						list[list[int]] ,					)    ->None:




       """simple docstring"""


       a		  =      claim_vector
       a		  =      allocated_resources_table
       a		  =      maximum_claim_table







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




       """simple docstring"""


       return [
           sum(p_item[i] for p_item in self.__allocated_resources_table  )
           for i in range(len(self.__allocated_resources_table[0]  )  )
       ]







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




       """simple docstring"""


       return np.array(self.__claim_vector  ) - np.array(
           self.__processes_resource_summation()  )







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




       """simple docstring"""


       return [
           list(np.array(self.__maximum_claim_table[i]  ) - np.array(__UpperCAmelCase  )  )
           for i, allocated_resource in enumerate(self.__allocated_resources_table  )
       ]







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->dict[int, list[int]]:




       """simple docstring"""


       return {self.__need().index(__UpperCAmelCase  ): i for i in self.__need()}







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					**__UpperCAmelCase      :						Any  )    ->None:




       """simple docstring"""


       a		  =      self.__need()
       a		  =      self.__allocated_resources_table
       a		  =      self.__available_resources()
       a		  =      self.__need_index_manager()
       for kw, val in kwargs.items():
        if kw and val is True:
         self.__pretty_data()
       print('''_''' * 50 + '''\n'''  )
       while need_list:
        a		  =      False
        for each_need in need_list:
         a		  =      True
         for index, need in enumerate(__UpperCAmelCase  ):
          if need > available_resources[index]:
           a		  =      False
           break
         if execution:
          a		  =      True
          # get the original index of the process from ind_ctrl db
          for original_need_index, need_clone in need_index_manager.items():
           if each_need == need_clone:
            a		  =      original_need_index
          print(F"""Process {process_number + 1} is executing."""  )
          # remove the process run from stack
          need_list.remove(__UpperCAmelCase  )
          # update available/freed resources stack
          a		  =      np.array(__UpperCAmelCase  ) + np.array(
              alloc_resources_table[process_number]  )
          print(
              '''Updated available resource stack for processes: '''
              + ''' '''.join([str(__UpperCAmelCase  ) for x in available_resources]  )  )
          break
        if safe:
         print('''The process is in a safe state.\n'''  )
        else:
         print('''System in unsafe state. Aborting...\n'''  )
         break







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




       """simple docstring"""


       print(''' ''' * 9 + '''Allocated Resource Table'''  )
       for item in self.__allocated_resources_table:
        print(
            F"""P{self.__allocated_resources_table.index(__UpperCAmelCase  ) + 1}"""
            + ''' '''.join(F"""{it:>8}""" for it in item  )
            + '''\n'''  )
       print(''' ''' * 9 + '''System Resource Table'''  )
       for item in self.__maximum_claim_table:
        print(
            F"""P{self.__maximum_claim_table.index(__UpperCAmelCase  ) + 1}"""
            + ''' '''.join(F"""{it:>8}""" for it in item  )
            + '''\n'''  )
       print(
           '''Current Usage by Active Processes: '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__claim_vector  )  )
       print(
           '''Initial Available Resources:       '''
           + ''' '''.join(str(__UpperCAmelCase  ) for x in self.__available_resources()  )  )
       time.sleep(1  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


