



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     :    Union[str, Any]      						=					"""platform"""
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class     UpperCamelCase_				:
	lowercase    =				PegasusConfig
	lowercase    =				{}
	lowercase    =				'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:
								UpperCAmelCase  :	Optional[Any]							  =	parent
								UpperCAmelCase  :	List[Any]							  =	batch_size
								UpperCAmelCase  :	Any							  =	seq_length
								UpperCAmelCase  :	Tuple							  =	is_training
								UpperCAmelCase  :	Tuple							  =	use_labels
								UpperCAmelCase  :	Tuple							  =	vocab_size
								UpperCAmelCase  :	str							  =	hidden_size
								UpperCAmelCase  :	Optional[Any]							  =	num_hidden_layers
								UpperCAmelCase  :	int							  =	num_attention_heads
								UpperCAmelCase  :	str							  =	intermediate_size

								UpperCAmelCase  :	Any							  =	hidden_dropout_prob
								UpperCAmelCase  :	str							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	List[Any]							  =	max_position_embeddings
								UpperCAmelCase  :	Any							  =	eos_token_id
								UpperCAmelCase  :	Any							  =	pad_token_id
								UpperCAmelCase  :	Optional[int]							  =	bos_token_id





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Dict							  =	ids_tensor([self.batch_size, self.seq_length - 1]	,	self.vocab_size					).clip(3	,	self.vocab_size					)
								UpperCAmelCase  :	Tuple							  =	np.expand_dims(np.array([self.eos_token_id] * self.batch_size					)	,	1					)
								UpperCAmelCase  :	Union[str, Any]							  =	np.concatenate([input_ids, eos_tensor]	,	axis=1					)

								UpperCAmelCase  :	List[str]							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size					)

								UpperCAmelCase  :	Tuple							  =	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  :	List[str]							  =	prepare_pegasus_inputs_dict(A	,	A	,	A					)
								return config, inputs_dict





	def 				_lowercase(						self	,	A	,	A	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	Dict							  =	20
								UpperCAmelCase  :	Optional[Any]							  =	model_class_name(A					)

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

								UpperCAmelCase  :	int							  =	(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

								UpperCAmelCase  :	int							  =	model.init_cache(decoder_input_ids.shape[0]	,	A	,	A					)
								UpperCAmelCase  :	int							  =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,	dtype="""i4"""					)

								UpperCAmelCase  :	Optional[int]							  =	jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]	,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,	)
								UpperCAmelCase  :	List[str]							  =	model.decode(
								    decoder_input_ids[:, :-1]	,	A	,	decoder_attention_mask=A	,	past_key_values=A	,	decoder_position_ids=A	,	)

								UpperCAmelCase  :	List[Any]							  =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,	dtype="""i4"""					)
								UpperCAmelCase  :	Optional[Any]							  =	model.decode(
								    decoder_input_ids[:, -1:]	,	A	,	decoder_attention_mask=A	,	past_key_values=outputs_cache.past_key_values	,	decoder_position_ids=A	,	)

								UpperCAmelCase  :	List[Any]							  =	model.decode(A	,	A					)

								UpperCAmelCase  :	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 				_lowercase(						self	,	A	,	A	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	str							  =	20
								UpperCAmelCase  :	Optional[int]							  =	model_class_name(A					)

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

								UpperCAmelCase  :	str							  =	(
								    inputs_dict["""decoder_input_ids"""],
								    inputs_dict["""decoder_attention_mask"""],
								)

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

								UpperCAmelCase  :	int							  =	model.init_cache(decoder_input_ids.shape[0]	,	A	,	A					)
								UpperCAmelCase  :	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)	,	)

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

								UpperCAmelCase  :	Any							  =	model.decode(A	,	A	,	decoder_attention_mask=A					)

								UpperCAmelCase  :	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    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       )   ->    int:
							if attention_mask is None:
														UpperCAmelCase  :	int							  =	np.not_equal(_lowercase			,       config.pad_token_id	).astype(np.inta	)
							if decoder_attention_mask is None:
														UpperCAmelCase  :	List[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     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				(
	    (
	        FlaxPegasusForConditionalGeneration,
	        FlaxPegasusModel,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase    =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
	lowercase    =				True
	lowercase    =				False
	lowercase    =				False
	lowercase    =				False





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	List[Any]							  =	FlaxPegasusModelTester(self					)
								UpperCAmelCase  :	Optional[Any]							  =	ConfigTester(self	,	config_class=A					)





	def 				_lowercase(						self					)    ->    List[str]:
								self.config_tester.run_common_tests()





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	List[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(A	,	A	,	A					)





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Optional[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_with_attn_mask(A	,	A	,	A					)





	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__					):
																						UpperCAmelCase  :	List[Any]							  =	self._prepare_for_class(A	,	A					)
																						UpperCAmelCase  :	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"""					):
																													UpperCAmelCase  :	Optional[int]							  =	encode_jitted(**A					).to_tuple()

																						with self.subTest("""JIT Disabled"""					):
																													with jax.disable_jit():
																																				UpperCAmelCase  :	Any							  =	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 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	str							  =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__					):
																						UpperCAmelCase  :	Union[str, Any]							  =	model_class(A					)
																						UpperCAmelCase  :	Tuple							  =	model.encode(inputs_dict["""input_ids"""]	,	inputs_dict["""attention_mask"""]					)

																						UpperCAmelCase  :	Tuple							  =	{
																						    """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"""					):
																													UpperCAmelCase  :	Tuple							  =	decode_jitted(**A					).to_tuple()

																						with self.subTest("""JIT Disabled"""					):
																													with jax.disable_jit():
																																				UpperCAmelCase  :	Optional[int]							  =	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 				_lowercase(						self					)    ->    Optional[Any]:
								for model_class_name in self.all_model_classes:
															UpperCAmelCase  :	Any							  =	model_class_name.from_pretrained("""google/pegasus-large"""	,	from_pt=A					)
															UpperCAmelCase  :	str							  =	np.ones((1, 1)					)
															UpperCAmelCase  :	int							  =	model(A					)
															self.assertIsNotNone(A					)






	@slow
	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	int							  =	FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""					)
								UpperCAmelCase  :	int							  =	PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""					)

								UpperCAmelCase  :	Any							  =	[
								    """ 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  :	Optional[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.""",
								]

								UpperCAmelCase  :	List[Any]							  =	tokenizer(A	,	return_tensors="""np"""	,	truncation=A	,	max_length=512	,	padding=A					)
								UpperCAmelCase  :	List[Any]							  =	model.generate(**A	,	num_beams=2					).sequences
								UpperCAmelCase  :	List[Any]							  =	tokenizer.batch_decode(A	,	skip_special_tokens=A					)
								assert tgt_text == decoded






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a     :    Optional[Any]      						=					{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Dict      						=					["""BertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[Any]      						=					[
							    """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BertForMaskedLM""",
							    """BertForMultipleChoice""",
							    """BertForNextSentencePrediction""",
							    """BertForPreTraining""",
							    """BertForQuestionAnswering""",
							    """BertForSequenceClassification""",
							    """BertForTokenClassification""",
							    """BertLayer""",
							    """BertLMHeadModel""",
							    """BertModel""",
							    """BertPreTrainedModel""",
							    """load_tf_weights_in_bert""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[str]      						=					[
							    """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBertEmbeddings""",
							    """TFBertForMaskedLM""",
							    """TFBertForMultipleChoice""",
							    """TFBertForNextSentencePrediction""",
							    """TFBertForPreTraining""",
							    """TFBertForQuestionAnswering""",
							    """TFBertForSequenceClassification""",
							    """TFBertForTokenClassification""",
							    """TFBertLMHeadModel""",
							    """TFBertMainLayer""",
							    """TFBertModel""",
							    """TFBertPreTrainedModel""",
							]
try:
							if not is_tensorflow_text_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    int      						=					["""TFBertTokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Optional[Any]      						=					[
							    """FlaxBertForCausalLM""",
							    """FlaxBertForMaskedLM""",
							    """FlaxBertForMultipleChoice""",
							    """FlaxBertForNextSentencePrediction""",
							    """FlaxBertForPreTraining""",
							    """FlaxBertForQuestionAnswering""",
							    """FlaxBertForSequenceClassification""",
							    """FlaxBertForTokenClassification""",
							    """FlaxBertModel""",
							    """FlaxBertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
							from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_fast import BertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert import (
														    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BertForMaskedLM,
														    BertForMultipleChoice,
														    BertForNextSentencePrediction,
														    BertForPreTraining,
														    BertForQuestionAnswering,
														    BertForSequenceClassification,
														    BertForTokenClassification,
														    BertLayer,
														    BertLMHeadModel,
														    BertModel,
														    BertPreTrainedModel,
														    load_tf_weights_in_bert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bert import (
														    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBertEmbeddings,
														    TFBertForMaskedLM,
														    TFBertForMultipleChoice,
														    TFBertForNextSentencePrediction,
														    TFBertForPreTraining,
														    TFBertForQuestionAnswering,
														    TFBertForSequenceClassification,
														    TFBertForTokenClassification,
														    TFBertLMHeadModel,
														    TFBertMainLayer,
														    TFBertModel,
														    TFBertPreTrainedModel,
														)

							try:
														if not is_tensorflow_text_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_tf import TFBertTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bert import (
														    FlaxBertForCausalLM,
														    FlaxBertForMaskedLM,
														    FlaxBertForMultipleChoice,
														    FlaxBertForNextSentencePrediction,
														    FlaxBertForPreTraining,
														    FlaxBertForQuestionAnswering,
														    FlaxBertForSequenceClassification,
														    FlaxBertForTokenClassification,
														    FlaxBertModel,
														    FlaxBertPreTrainedModel,
														)

else:
							import sys

							a     :    Optional[int]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

