






"""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__	:    Any			 = 'platform'
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class        lowercase__      :
			_UpperCAmelCase		:int             =			PegasusConfig
			_UpperCAmelCase		:Optional[Any]             =			{}
			_UpperCAmelCase		:Tuple             =			"gelu"


			def __init__(				self					:  Optional[int]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Any=13       ,	snake_case__					:  int=7       ,	snake_case__					:  Optional[Any]=True       ,	snake_case__					:  str=False       ,	snake_case__					:  Dict=99       ,	snake_case__					:  Union[str, Any]=32       ,	snake_case__					:  List[Any]=5       ,	snake_case__					:  Optional[Any]=4       ,	snake_case__					:  Optional[int]=37       ,	snake_case__					:  Optional[int]=0.1       ,	snake_case__					:  List[Any]=0.1       ,	snake_case__					:  Union[str, Any]=20       ,	snake_case__					:  Tuple=2       ,	snake_case__					:  Union[str, Any]=1       ,	snake_case__					:  Any=0       ,	):
										lowerCamelCase_	:							Optional[int]									=parent
										lowerCamelCase_	:							Any									=batch_size
										lowerCamelCase_	:							Dict									=seq_length
										lowerCamelCase_	:							Dict									=is_training
										lowerCamelCase_	:							int									=use_labels
										lowerCamelCase_	:							Optional[int]									=vocab_size
										lowerCamelCase_	:							Dict									=hidden_size
										lowerCamelCase_	:							Optional[Any]									=num_hidden_layers
										lowerCamelCase_	:							Dict									=num_attention_heads
										lowerCamelCase_	:							Optional[Any]									=intermediate_size

										lowerCamelCase_	:							str									=hidden_dropout_prob
										lowerCamelCase_	:							int									=attention_probs_dropout_prob
										lowerCamelCase_	:							Tuple									=max_position_embeddings
										lowerCamelCase_	:							List[str]									=eos_token_id
										lowerCamelCase_	:							str									=pad_token_id
										lowerCamelCase_	:							List[str]									=bos_token_id


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Union[str, Any]									=ids_tensor([self.batch_size, self.seq_length - 1]       ,	self.vocab_size ).clip(3       ,	self.vocab_size )
										lowerCamelCase_	:							str									=np.expand_dims(np.array([self.eos_token_id] * self.batch_size )       ,	1 )
										lowerCamelCase_	:							str									=np.concatenate([input_ids, eos_tensor]       ,	axis=1 )

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

										lowerCamelCase_	:							List[str]									=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_	:							Optional[int]									=prepare_pegasus_inputs_dict(snake_case__       ,	snake_case__       ,	snake_case__ )
										return config, inputs_dict


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  List[Any]       ,	snake_case__					:  Tuple       ,	snake_case__					:  int ):
										lowerCamelCase_	:							Dict									=20
										lowerCamelCase_	:							Optional[Any]									=model_class_name(snake_case__ )

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

										lowerCamelCase_	:							Optional[Any]									=(
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										lowerCamelCase_	:							List[str]									=model.init_cache(decoder_input_ids.shape[0]       ,	snake_case__       ,	snake_case__ )
										lowerCamelCase_	:							Optional[int]									=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,	dtype="i4" )

										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_	:							List[str]									=model.decode(
										    decoder_input_ids[:, :-1]       ,	snake_case__       ,	decoder_attention_mask=snake_case__       ,	past_key_values=snake_case__       ,	decoder_position_ids=snake_case__       ,	)

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

										lowerCamelCase_	:							Tuple									=model.decode(snake_case__       ,	snake_case__ )

										lowerCamelCase_	:							List[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__						(				self					:  List[Any]       ,	snake_case__					:  str       ,	snake_case__					:  int       ,	snake_case__					:  Optional[int] ):
										lowerCamelCase_	:							str									=20
										lowerCamelCase_	:							str									=model_class_name(snake_case__ )

										lowerCamelCase_	:							Optional[int]									=model.encode(inputs_dict["input_ids"] )

										lowerCamelCase_	:							Optional[Any]									=(
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										lowerCamelCase_	:							List[str]									=jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
										    ]       ,	axis=-1       ,	)

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

										lowerCamelCase_	:							str									=model.decode(snake_case__       ,	snake_case__       ,	decoder_attention_mask=snake_case__ )

										lowerCamelCase_	:							Union[str, 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    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   List[str]    ,						lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   List[str]=None    ,						lowerCamelCase__     :   List[str]=None    ,						)    ->     Union[str, Any]:
							if attention_mask is None:
														lowerCamelCase_	:							int									=np.not_equal(lowerCamelCase__    ,						config.pad_token_id  ).astype(np.inta  )
							if decoder_attention_mask is None:
														lowerCamelCase_	:							Optional[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        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Any             =			(
			    (
			        FlaxPegasusForConditionalGeneration,
			        FlaxPegasusModel,
			    )
			    if is_flax_available()
			    else ()
			)
			_UpperCAmelCase		:Optional[Any]             =			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
			_UpperCAmelCase		:Tuple             =			True
			_UpperCAmelCase		:List[Any]             =			False
			_UpperCAmelCase		:List[str]             =			False
			_UpperCAmelCase		:int             =			False


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							List[str]									=FlaxPegasusModelTester(self )
										lowerCamelCase_	:							int									=ConfigTester(self       ,	config_class=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										self.config_tester.run_common_tests()


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							Dict									=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(snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							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_	:							Any									=self._prepare_for_class(snake_case__       ,	snake_case__ )
																								lowerCamelCase_	:							List[Any]									=model_class(snake_case__ )

																								@jax.jit
																								def encode_jitted(snake_case__					:  Any       ,	snake_case__					:  Any=None       ,	**snake_case__					:  int ):
																															return model.encode(input_ids=snake_case__       ,	attention_mask=snake_case__ )

																								with self.subTest("JIT Enabled" ):
																															lowerCamelCase_	:							Any									=encode_jitted(**snake_case__ ).to_tuple()

																								with self.subTest("JIT Disabled" ):
																															with jax.disable_jit():
																																						lowerCamelCase_	:							List[Any]									=encode_jitted(**snake_case__ ).to_tuple()

																								self.assertEqual(len(snake_case__ )       ,	len(snake_case__ ) )
																								for jitted_output, output in zip(snake_case__       ,	snake_case__ ):
																															self.assertEqual(jitted_output.shape       ,	output.shape )


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										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_	:							List[str]									=model_class(snake_case__ )
																								lowerCamelCase_	:							Union[str, Any]									=model.encode(inputs_dict["input_ids"]       ,	inputs_dict["attention_mask"] )

																								lowerCamelCase_	:							List[str]									={
																								    "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(snake_case__					:  Optional[int]       ,	snake_case__					:  str       ,	snake_case__					:  Any ):
																															return model.decode(
																															    decoder_input_ids=snake_case__       ,	decoder_attention_mask=snake_case__       ,	encoder_outputs=snake_case__       ,	)

																								with self.subTest("JIT Enabled" ):
																															lowerCamelCase_	:							Any									=decode_jitted(**snake_case__ ).to_tuple()

																								with self.subTest("JIT Disabled" ):
																															with jax.disable_jit():
																																						lowerCamelCase_	:							Any									=decode_jitted(**snake_case__ ).to_tuple()

																								self.assertEqual(len(snake_case__ )       ,	len(snake_case__ ) )
																								for jitted_output, output in zip(snake_case__       ,	snake_case__ ):
																															self.assertEqual(jitted_output.shape       ,	output.shape )


			@slow
			def      UpperCAmelCase__						(				self					:  str ):
										for model_class_name in self.all_model_classes:
																	lowerCamelCase_	:							List[Any]									=model_class_name.from_pretrained("google/pegasus-large"       ,	from_pt=snake_case__ )
																	lowerCamelCase_	:							List[str]									=np.ones((1, 1) )
																	lowerCamelCase_	:							List[Any]									=model(snake_case__ )
																	self.assertIsNotNone(snake_case__ )


			@slow
			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Any									=FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" )
										lowerCamelCase_	:							Any									=PegasusTokenizer.from_pretrained("google/pegasus-xsum" )

										lowerCamelCase_	:							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_	:							int									=[
										    "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_	:							List[str]									=tokenizer(snake_case__       ,	return_tensors="np"       ,	truncation=snake_case__       ,	max_length=512       ,	padding=snake_case__ )
										lowerCamelCase_	:							Optional[Any]									=model.generate(**snake_case__       ,	num_beams=2 ).sequences
										lowerCamelCase_	:							Union[str, Any]									=tokenizer.batch_decode(snake_case__       ,	skip_special_tokens=snake_case__ )
										assert tgt_text == decoded











"""simple docstring"""



from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


A__	:    Dict			 = logging.get_logger(__name__)

A__	:    Dict			 = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A__	:    Optional[int]			 = {
    'facebook/blenderbot_small-90M': 512,
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			BlenderbotSmallTokenizer


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=None       ,	snake_case__					:  str=None       ,	snake_case__					:  Any="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  str=False       ,	snake_case__					:  int=True       ,	**snake_case__					:  Tuple       ,	):
										super().__init__(
										    ByteLevelBPETokenizer(
										        vocab=snake_case__       ,	merges=snake_case__       ,	add_prefix_space=snake_case__       ,	trim_offsets=snake_case__       ,	)       ,	bos_token=snake_case__       ,	eos_token=snake_case__       ,	unk_token=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Optional[int]									=add_prefix_space


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None ):
										lowerCamelCase_	:							Optional[Any]									=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																	return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							int									=[self.sep_token_id]
										lowerCamelCase_	:							List[Any]									=[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 + sep + token_ids_a + sep ) * [0]



