




"""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 __lowercase:




					'''simple docstring'''


					__a						:						Tuple     		= PegasusConfig
					__a						:						List[str]     		= {}
					__a						:						Tuple     		= '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						,	):
								__lowerCamelCase					:	str	       =				parent
								__lowerCamelCase					:	Tuple	       =				batch_size
								__lowerCamelCase					:	Any	       =				seq_length
								__lowerCamelCase					:	Dict	       =				is_training
								__lowerCamelCase					:	Optional[int]	       =				use_labels
								__lowerCamelCase					:	List[Any]	       =				vocab_size
								__lowerCamelCase					:	List[Any]	       =				hidden_size
								__lowerCamelCase					:	int	       =				num_hidden_layers
								__lowerCamelCase					:	Optional[Any]	       =				num_attention_heads
								__lowerCamelCase					:	List[Any]	       =				intermediate_size

								__lowerCamelCase					:	str	       =				hidden_dropout_prob
								__lowerCamelCase					:	Optional[Any]	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	List[Any]	       =				max_position_embeddings
								__lowerCamelCase					:	Tuple	       =				eos_token_id
								__lowerCamelCase					:	Optional[int]	       =				pad_token_id
								__lowerCamelCase					:	Optional[int]	       =				bos_token_id
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				ids_tensor([self.batch_size, self.seq_length - 1]						,	self.vocab_size     ).clip(3						,	self.vocab_size     )
								__lowerCamelCase					:	List[Any]	       =				np.expand_dims(np.array([self.eos_token_id] * self.batch_size     )						,	1     )
								__lowerCamelCase					:	int	       =				np.concatenate([input_ids, eos_tensor]						,	axis=1     )

								__lowerCamelCase					:	List[Any]	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

								__lowerCamelCase					:	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						,	)
								__lowerCamelCase					:	Union[str, Any]	       =				prepare_pegasus_inputs_dict(__a						,	__a						,	__a     )
								return config, inputs_dict
					def     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[int]	       =				20
								__lowerCamelCase					:	List[str]	       =				model_class_name(__a     )

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

								__lowerCamelCase       ,      __lowerCamelCase					:	str	       =				(
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								__lowerCamelCase					:	int	       =				model.init_cache(decoder_input_ids.shape[0]						,	__a						,	__a     )
								__lowerCamelCase					:	Any	       =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,	dtype='i4'     )

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

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

								__lowerCamelCase					:	str	       =				model.decode(__a						,	__a     )

								__lowerCamelCase					:	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     snake_case_				(    self						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Any	       =				20
								__lowerCamelCase					:	int	       =				model_class_name(__a     )

								__lowerCamelCase					:	int	       =				model.encode(inputs_dict['input_ids']     )

								__lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				(
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

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

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

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

								__lowerCamelCase					:	str	       =				model.decode(__a						,	__a						,	decoder_attention_mask=__a     )

								__lowerCamelCase					:	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 						UpperCAmelCase				(			A__:    str							,				A__:    Tuple							,				A__:    Optional[int]							,				A__:    List[Any]=None							,				A__:    int=None							,				)  ->     Optional[int]:
			if attention_mask is None:
						__lowerCamelCase					:	int	       =				np.not_equal(A__							,				config.pad_token_id     ).astype(np.inta     )
			if decoder_attention_mask is None:
						__lowerCamelCase					:	List[str]	       =				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'''


					__a						:						int     		= (
					    (
					        FlaxPegasusForConditionalGeneration,
					        FlaxPegasusModel,
					    )
					    if is_flax_available()
					    else ()
					)
					__a						:						str     		= (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
					__a						:						Optional[int]     		= True
					__a						:						Optional[Any]     		= False
					__a						:						Optional[Any]     		= False
					__a						:						Dict     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				FlaxPegasusModelTester(self     )
								__lowerCamelCase					:	Any	       =				ConfigTester(self						,	config_class=__a     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				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     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	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(__a						,	__a						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	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__     ):
														__lowerCamelCase					:	Optional[Any]	       =				self._prepare_for_class(__a						,	__a     )
														__lowerCamelCase					:	int	       =				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'     ):
																	__lowerCamelCase					:	Optional[int]	       =				encode_jitted(**__a     ).to_tuple()

														with self.subTest('JIT Disabled'     ):
																	with jax.disable_jit():
																				__lowerCamelCase					:	Tuple	       =				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     snake_case_				(    self     ):
								__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				self.model_tester.prepare_config_and_inputs_for_common()

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

														__lowerCamelCase					:	Optional[Any]	       =				{
														    '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'     ):
																	__lowerCamelCase					:	List[Any]	       =				decode_jitted(**__a     ).to_tuple()

														with self.subTest('JIT Disabled'     ):
																	with jax.disable_jit():
																				__lowerCamelCase					:	List[Any]	       =				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     snake_case_				(    self     ):
								for model_class_name in self.all_model_classes:
											__lowerCamelCase					:	Optional[Any]	       =				model_class_name.from_pretrained('google/pegasus-large'						,	from_pt=__a     )
											__lowerCamelCase					:	Any	       =				np.ones((1, 1)     )
											__lowerCamelCase					:	Any	       =				model(__a     )
											self.assertIsNotNone(__a     )





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'     )
								__lowerCamelCase					:	List[Any]	       =				PegasusTokenizer.from_pretrained('google/pegasus-xsum'     )

								__lowerCamelCase					:	Optional[int]	       =				[
								    ' 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!" ',
								]

								__lowerCamelCase					:	List[Any]	       =				[
								    '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.',
								]

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









"""simple docstring"""
import pytest

import datasets


# Import fixture modules as plugins
a_				:    Tuple										=     ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Optional[Any]     )  ->     List[str]:
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ['integration', 'unit']     ):
									continue
						item.add_marker(pytest.mark.unit     )





def 						UpperCAmelCase				(			A__:    int     )  ->     Optional[int]:
			config.addinivalue_line('markers'							,				'torchaudio_latest: mark test to run with torchaudio>=0.12'     )





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    List[Any]     )  ->     Any:
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			__lowerCamelCase					:	str	       =				tmp_path_factory.getbasetemp() / 'cache'
			__lowerCamelCase					:	Dict	       =				test_hf_cache_home / 'datasets'
			__lowerCamelCase					:	int	       =				test_hf_cache_home / 'metrics'
			__lowerCamelCase					:	List[str]	       =				test_hf_cache_home / 'modules'
			monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'							,				str(A__     )     )
			__lowerCamelCase					:	List[Any]	       =				test_hf_datasets_cache / 'downloads'
			monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'							,				str(A__     )     )
			__lowerCamelCase					:	Optional[int]	       =				test_hf_datasets_cache / 'downloads' / 'extracted'
			monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'							,				str(A__     )     )





@pytest.fixture(autouse=A__							,				scope='session'     )
def 						UpperCAmelCase				(			)  ->     Any:
			datasets.disable_progress_bar()





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     Dict:
			# don't take tests into account when counting downloads
			monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'							,				A__     )






@pytest.fixture
def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     Optional[Any]:
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'							,				A__     )



