






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

	from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 					UpperCamelCase_ :



	'''simple docstring'''




	lowerCAmelCase						=					PegasusConfig
	lowerCAmelCase						=					{}
	lowerCAmelCase						=					'''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			,     )			->       str:
								snake_case_           =				parent
								snake_case_           =				batch_size
								snake_case_           =				seq_length
								snake_case_           =				is_training
								snake_case_           =				use_labels
								snake_case_           =				vocab_size
								snake_case_           =				hidden_size
								snake_case_           =				num_hidden_layers
								snake_case_           =				num_attention_heads
								snake_case_           =				intermediate_size

								snake_case_           =				hidden_dropout_prob
								snake_case_           =				attention_probs_dropout_prob
								snake_case_           =				max_position_embeddings
								snake_case_           =				eos_token_id
								snake_case_           =				pad_token_id
								snake_case_           =				bos_token_id




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size   ).clip(3			,     self.vocab_size   )
								snake_case_           =				np.expand_dims(np.array([self.eos_token_id] * self.batch_size   )			,     1   )
								snake_case_           =				np.concatenate([input_ids, eos_tensor]			,     axis=1   )

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

								snake_case_           =				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			,     )
								snake_case_           =				prepare_pegasus_inputs_dict(a			,     a			,     a   )
								return config, inputs_dict




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       Optional[int]:
								snake_case_           =				20
								snake_case_           =				model_class_name(a   )

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

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

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

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

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

								snake_case_           =				model.decode(a			,     a   )

								snake_case_           =				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   )			->       List[str]:
								snake_case_           =				20
								snake_case_           =				model_class_name(a   )

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

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

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

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

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

								snake_case_           =				model.decode(a			,     a			,     decoder_attention_mask=a   )

								snake_case_           =				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_				,	a_				,	a_				,	a_=None				,	a_=None				,	):
							if attention_mask is None:
														snake_case_           =				np.not_equal(a_				,	config.pad_token_id).astype(np.inta)
							if decoder_attention_mask is None:
														snake_case_           =				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_ (     snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					(
	    (
	        FlaxPegasusForConditionalGeneration,
	        FlaxPegasusModel,
	    )
	    if is_flax_available()
	    else ()
	)
	lowerCAmelCase						=					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
	lowerCAmelCase						=					True
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False
	lowerCAmelCase						=					False




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								snake_case_           =				FlaxPegasusModelTester(self   )
								snake_case_           =				ConfigTester(self			,     config_class=a   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								self.config_tester.run_common_tests()




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_     ,						snake_case_           =				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[int]:
								snake_case_     ,						snake_case_           =				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   )			->       str:
								snake_case_     ,						snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__   ):
																						snake_case_           =				self._prepare_for_class(a			,     a   )
																						snake_case_           =				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'   ):
																													snake_case_           =				encode_jitted(**a   ).to_tuple()

																						with self.subTest('JIT Disabled'   ):
																													with jax.disable_jit():
																																				snake_case_           =				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   )			->       Union[str, Any]:
								snake_case_     ,						snake_case_           =				self.model_tester.prepare_config_and_inputs_for_common()

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

																						snake_case_           =				{
																						    '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'   ):
																													snake_case_           =				decode_jitted(**a   ).to_tuple()

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






	@slow
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'   )
								snake_case_           =				PegasusTokenizer.from_pretrained('google/pegasus-xsum'   )

								snake_case_           =				[
								    ' 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!" ',
								]

								snake_case_           =				[
								    '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.',
								]

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













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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_rembert import RemBertTokenizer
else:
	lowercase    				=   None

lowercase    				=   logging.get_logger(__name__)
lowercase    				=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

lowercase    				=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

lowercase    				=   {
    "google/rembert": 256,
}

lowercase    				=   "▁"




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					VOCAB_FILES_NAMES
	lowerCAmelCase						=					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase						=					RemBertTokenizer




	def __init__(			self			,     a=None			,     a=None			,     a=True			,     a=True			,     a=False			,     a="[CLS]"			,     a="[SEP]"			,     a="<unk>"			,     a="[SEP]"			,     a="<pad>"			,     a="[CLS]"			,     a="[MASK]"			,     **a			,     )			->       Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_           =				AddedToken(a			,     lstrip=a			,     rstrip=a   ) if isinstance(a			,     a   ) else mask_token

								super().__init__(
								    a			,     tokenizer_file=a			,     do_lower_case=a			,     remove_space=a			,     keep_accents=a			,     bos_token=a			,     eos_token=a			,     unk_token=a			,     sep_token=a			,     pad_token=a			,     cls_token=a			,     mask_token=a			,     **a			,     )

								snake_case_           =				do_lower_case
								snake_case_           =				remove_space
								snake_case_           =				keep_accents
								snake_case_           =				vocab_file
								snake_case_           =				False if not self.vocab_file else True




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[self.cls_token_id]
								if token_ids_a is None:
															return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep




	def 				_UpperCamelCase     (			self			,     a			,     a = None			,     a = False   )			->       List[int]:

								if already_has_special_tokens:
															if token_ids_a is not None:
																						raise ValueError(
																						    'You should not supply a second sequence if the provided sequence of '
																						    'ids is already formatted with special tokens for the model.'   )
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
															return [1] + ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]
								return [1] + ([0] * len(a   )) + [1]




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       Tuple[str]:
								if not os.path.isdir(a   ):
															logger.error('Vocabulary path ({}) should be a directory'.format(a   )   )
															return
								snake_case_           =				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   ):
															copyfile(self.vocab_file			,     a   )

								return (out_vocab_file,)





