



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  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      Any         =					PegasusConfig
							UpperCamelCase__				:      Optional[int]         =					{}
							UpperCamelCase__				:      Union[str, Any]         =					"""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  ,  ):
											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 _a      (							self		):
											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(a_  ,  a_  ,  a_		)
											return config, inputs_dict


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		20
											a__				      =		model_class_name(a_		)

											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]  ,  a_  ,  a_		)
											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]  ,  a_  ,  decoder_attention_mask=a_  ,  past_key_values=a_  ,  decoder_position_ids=a_  ,  )

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

											a__				      =		model.decode(a_  ,  a_		)

											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      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		20
											a__				      =		model_class_name(a_		)

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

											a__				      =		model.decode(a_  ,  a_  ,  decoder_attention_mask=a_		)

											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     :				Union[str, Any] ,		__a     :				List[str] ,		__a     :				Tuple ,		__a     :				Dict=None ,		__a     :				Optional[Any]=None ,		):

				"""simple docstring"""
				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  __snake_case      (   SCREAMING_SNAKE_CASE			,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							UpperCamelCase__				:      Any         =					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							UpperCamelCase__				:      Any         =					True
							UpperCamelCase__				:      Dict         =					False
							UpperCamelCase__				:      List[str]         =					False
							UpperCamelCase__				:      str         =					False


							def _a      (							self		):
											a__				      =		FlaxPegasusModelTester(self		)
											a__				      =		ConfigTester(self  ,  config_class=a_		)


							def _a      (							self		):
											self.config_tester.run_common_tests()


							def _a      (							self		):
											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(a_  ,  a_  ,  a_		)


							def _a      (							self		):
											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(a_  ,  a_  ,  a_		)


							def _a      (							self		):
											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(a_  ,  a_		)
																			a__				      =		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"""		):
																							a__				      =		encode_jitted(**a_		).to_tuple()

																			with self.subTest("""JIT Disabled"""		):
																							with jax.disable_jit():
																											a__				      =		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 _a      (							self		):
											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(a_		)
																			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(a_  ,  a_  ,  a_		):
																							return model.decode(
																							    decoder_input_ids=a_  ,  decoder_attention_mask=a_  ,  encoder_outputs=a_  ,  )

																			with self.subTest("""JIT Enabled"""		):
																							a__				      =		decode_jitted(**a_		).to_tuple()

																			with self.subTest("""JIT Disabled"""		):
																							with jax.disable_jit():
																											a__				      =		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 _a      (							self		):
											for model_class_name in self.all_model_classes:
															a__				      =		model_class_name.from_pretrained("""google/pegasus-large"""  ,  from_pt=a_		)
															a__				      =		np.ones((1, 1)		)
															a__				      =		model(a_		)
															self.assertIsNotNone(a_		)







							@slow
							def _a      (							self		):
											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(a_  ,  return_tensors="""np"""  ,  truncation=a_  ,  max_length=512  ,  padding=a_		)
											a__				      =		model.generate(**a_  ,  num_beams=2		).sequences
											a__				      =		tokenizer.batch_decode(a_  ,  skip_special_tokens=a_		)
											assert tgt_text == decoded






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase							 =					{"""bert_for_seq_generation""": 512}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Any         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[str]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      Optional[int]         =					["""input_ids""", """attention_mask"""]


							def __init__(							self  ,  a_  ,  a_="<s>"  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<::::>"  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Add extra_ids to the special token list
											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  pad_token=a_  ,  sep_token=a_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **a_  ,  )

											a__				      =		vocab_file

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a      (							self		):
											return self.sp_model.get_piece_size()


							def _a      (							self		):
											a__				      =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(							self		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

											# for backward compatibility
											if not hasattr(self  ,  """sp_model_kwargs"""		):
															a__				      =		{}

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a      (							self  ,  a_		):
											return self.sp_model.encode(a_  ,  out_type=a_		)


							def _a      (							self  ,  a_		):
											return self.sp_model.piece_to_id(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.sp_model.IdToPiece(a_		)
											return token


							def _a      (							self  ,  a_		):
											a__				      =		[]
											a__				      =		""""""
											for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(a_		) + token
																			a__				      =		[]
															else:
																			current_sub_tokens.append(a_		)
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    a_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(a_		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file  ,  a_		)
											elif not os.path.isfile(self.vocab_file		):
															with open(a_  ,  """wb"""		) as fi:
																			a__				      =		self.sp_model.serialized_model_proto()
																			fi.write(a_		)

											return (out_vocab_file,)

