

"""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
				_lowercase   :	Tuple												=						'platform'
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class      _UpperCAmelCase   :
	a__		: List[Any]					=					PegasusConfig
	a__		: int					=					{}
	a__		: Dict					=					'gelu'



	def __init__(  self     :       Any			,    _lowercase     :       Tuple			,    _lowercase     :       str=13			,    _lowercase     :       List[Any]=7			,    _lowercase     :       List[Any]=True			,    _lowercase     :       str=False			,    _lowercase     :       List[Any]=99			,    _lowercase     :       Optional[Any]=32			,    _lowercase     :       Tuple=5			,    _lowercase     :       str=4			,    _lowercase     :       Tuple=37			,    _lowercase     :       str=0.1			,    _lowercase     :       Union[str, Any]=0.1			,    _lowercase     :       List[Any]=20			,    _lowercase     :       str=2			,    _lowercase     :       Tuple=1			,    _lowercase     :       Tuple=0			,    ):
		__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        a  (  self     :       Optional[Any]						):
		__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(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase						)
		return config, inputs_dict



	def        a  (  self     :       Union[str, Any]			,    _lowercase     :       Optional[Any]			,    _lowercase     :       Any			,    _lowercase     :       int						):
		__UpperCAmelCase      		= 20
		__UpperCAmelCase      		= model_class_name(_UpperCAmelCase						)

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

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

		__UpperCAmelCase      		= model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase						)
		__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]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )

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

		__UpperCAmelCase      		= model.decode(_UpperCAmelCase			,    _UpperCAmelCase						)

		__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        a  (  self     :       Optional[int]			,    _lowercase     :       Tuple			,    _lowercase     :       Dict			,    _lowercase     :       List[Any]						):
		__UpperCAmelCase      		= 20
		__UpperCAmelCase      		= model_class_name(_UpperCAmelCase						)

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

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

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

		__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       lowercase__						(       snake_case_   :Tuple			,		snake_case_   :Optional[Any]			,		snake_case_   :Tuple			,		snake_case_   :Dict=None			,		snake_case_   :List[str]=None			,		):
	if attention_mask is None:
		__UpperCAmelCase      		= np.not_equal(lowerCAmelCase_			,		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   (							_UpperCamelCase		,    unittest.TestCase       ):
	a__		: List[str]					=					(
	    (
	        FlaxPegasusForConditionalGeneration,
	        FlaxPegasusModel,
	    )
	    if is_flax_available()
	    else ()
	)
	a__		: Any					=					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
	a__		: List[Any]					=					True
	a__		: List[str]					=					False
	a__		: List[Any]					=					False
	a__		: List[str]					=					False



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= FlaxPegasusModelTester(self						)
		__UpperCAmelCase      		= ConfigTester(self			,    config_class=_UpperCAmelCase						)



	def        a  (  self     :       List[str]						):
		self.config_tester.run_common_tests()



	def        a  (  self     :       str						):
		__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(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase						)



	def        a  (  self     :       str						):
		__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(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase						)



	def        a  (  self     :       Any						):
		__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(_UpperCAmelCase			,    _UpperCAmelCase						)
				__UpperCAmelCase      		= model_class(_UpperCAmelCase						)

				@jax.jit
				def encode_jitted(_lowercase     :       int			,    _lowercase     :       int=None			,    **_lowercase     :       Tuple						):
					return model.encode(input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase						)

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

				with self.subTest('''JIT Disabled'''						):
					with jax.disable_jit():
						__UpperCAmelCase      		= 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        a  (  self     :       Optional[Any]						):
		__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(_UpperCAmelCase						)
				__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(_lowercase     :       int			,    _lowercase     :       List[str]			,    _lowercase     :       Tuple						):
					return model.decode(
					    decoder_input_ids=_UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    )

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

				with self.subTest('''JIT Disabled'''						):
					with jax.disable_jit():
						__UpperCAmelCase      		= 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        a  (  self     :       Any						):
		for model_class_name in self.all_model_classes:
			__UpperCAmelCase      		= model_class_name.from_pretrained('''google/pegasus-large'''			,    from_pt=_UpperCAmelCase						)
			__UpperCAmelCase      		= np.ones((1, 1)						)
			__UpperCAmelCase      		= model(_UpperCAmelCase						)
			self.assertIsNotNone(_UpperCAmelCase						)



	@slow
	def        a  (  self     :       Union[str, Any]						):
		__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(_UpperCAmelCase			,    return_tensors='''np'''			,    truncation=_UpperCAmelCase			,    max_length=5_12			,    padding=_UpperCAmelCase						)
		__UpperCAmelCase      		= model.generate(**_UpperCAmelCase			,    num_beams=2						).sequences
		__UpperCAmelCase      		= tokenizer.batch_decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase						)
		assert tgt_text == decoded





'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCAmelCase		     =  TypeVar('''T''')

class       __magic_name__		(       Generic[T]  ):







							def __init__(				self   :			int      ,_UpperCAmelCase   :			bool = True						):
									_a	: dict[T, list[T]]								=				{}  # dictionary of lists
									_a	: Tuple								=				directed







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			T      ,_UpperCAmelCase   :			T						):

									if not self.directed:  # For undirected graphs
											# if both source vertex and destination vertex are both present in the
											# adjacency list, add destination vertex to source vertex list of adjacent
											# vertices and add source vertex to destination vertex list of adjacent
											# vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination vertex
											# to source vertex list of adjacent vertices, then create a new vertex with
											# destination vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: Optional[Any]								=				[source_vertex]
											# if only destination vertex is present in adjacency list, add source vertex
											# to destination vertex list of adjacent vertices, then create a new vertex
											# with source vertex as key and assign a list containing the source vertex
											# as it's first adjacent vertex.
											elif destination_vertex in self.adj_list:
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
													_a	: Union[str, Any]								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and assign a list
											# containing the destination vertex as it's first adjacent vertex also
											# create a new vertex with destination vertex as key and assign a list
											# containing the source vertex as it's first adjacent vertex.
											else:
													_a	: Union[str, Any]								=				[destination_vertex]
													_a	: Optional[int]								=				[source_vertex]
									else:  # For directed graphs
											# if both source vertex and destination vertex are present in adjacency
											# list, add destination vertex to source vertex list of adjacent vertices.
											if source_vertex in self.adj_list and destination_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
											# if only source vertex is present in adjacency list, add destination
											# vertex to source vertex list of adjacent vertices and create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											elif source_vertex in self.adj_list:
													self.adj_list[source_vertex].append(_UpperCAmelCase						)
													_a	: int								=				[]
											# if only destination vertex is present in adjacency list, create a new
											# vertex with source vertex as key and assign a list containing destination
											# vertex as first adjacent vertex
											elif destination_vertex in self.adj_list:
													_a	: Tuple								=				[destination_vertex]
											# if both source vertex and destination vertex are not present in adjacency
											# list, create a new vertex with source vertex as key and a list containing
											# destination vertex as it's first adjacent vertex. Then create a new vertex
											# with destination vertex as key, which has no adjacent vertex
											else:
													_a	: Tuple								=				[destination_vertex]
													_a	: str								=				[]

									return self







							def __repr__(				self   :			int						):
									return pformat(self.adj_list						)




