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

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 		SCREAMING_SNAKE_CASE  :







				'''simple docstring'''
				UpperCAmelCase__					      =				PegasusConfig
				UpperCAmelCase__					      =				{}
				UpperCAmelCase__					      =				'''gelu'''





				def __init__(    self       :    str							,		lowercase__       :    int							,		lowercase__       :    Optional[Any]=13							,		lowercase__       :    str=7							,		lowercase__       :    Tuple=True							,		lowercase__       :    List[Any]=False							,		lowercase__       :    Union[str, Any]=99							,		lowercase__       :    Dict=32							,		lowercase__       :    Tuple=5							,		lowercase__       :    Tuple=4							,		lowercase__       :    int=37							,		lowercase__       :    Any=0.1							,		lowercase__       :    List[Any]=0.1							,		lowercase__       :    Optional[int]=20							,		lowercase__       :    Optional[Any]=2							,		lowercase__       :    List[Any]=1							,		lowercase__       :    Optional[Any]=0							,		)				->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase      :      Tuple       =    parent
										_UpperCamelCase      :      Optional[Any]       =    batch_size
										_UpperCamelCase      :      List[str]       =    seq_length
										_UpperCamelCase      :      str       =    is_training
										_UpperCamelCase      :      int       =    use_labels
										_UpperCamelCase      :      Optional[int]       =    vocab_size
										_UpperCamelCase      :      Union[str, Any]       =    hidden_size
										_UpperCamelCase      :      str       =    num_hidden_layers
										_UpperCamelCase      :      int       =    num_attention_heads
										_UpperCamelCase      :      Optional[Any]       =    intermediate_size

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





				def 			snake_case__					(    self       :    int       )				->str:


										'''simple docstring'''




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

										_UpperCamelCase      :      Optional[int]       =    ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size       )

										_UpperCamelCase      :      Optional[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							,		)
										_UpperCamelCase      :      Optional[Any]       =    prepare_pegasus_inputs_dict(_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase       )
										return config, inputs_dict





				def 			snake_case__					(    self       :    Dict							,		lowercase__       :    List[Any]							,		lowercase__       :    Union[str, Any]							,		lowercase__       :    str       )				->Any:


										'''simple docstring'''




										_UpperCamelCase      :      Tuple       =    20
										_UpperCamelCase      :      Any       =    model_class_name(_UpperCAmelCase       )

										_UpperCamelCase      :      Dict       =    model.encode(inputs_dict["input_ids"]       )

										_UpperCamelCase      ,							_UpperCamelCase      :      List[Any]       =    (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										_UpperCamelCase      :      List[Any]       =    model.init_cache(decoder_input_ids.shape[0]							,		_UpperCAmelCase							,		_UpperCAmelCase       )
										_UpperCamelCase      :      Optional[Any]       =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,		dtype="i4"       )

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

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

										_UpperCamelCase      :      Any       =    model.decode(_UpperCAmelCase							,		_UpperCAmelCase       )

										_UpperCamelCase      :      int       =    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       :    Optional[int]							,		lowercase__       :    Optional[Any]							,		lowercase__       :    Any							,		lowercase__       :    Any       )				->List[Any]:


										'''simple docstring'''




										_UpperCamelCase      :      str       =    20
										_UpperCamelCase      :      List[str]       =    model_class_name(_UpperCAmelCase       )

										_UpperCamelCase      :      List[Any]       =    model.encode(inputs_dict["input_ids"]       )

										_UpperCamelCase      ,							_UpperCamelCase      :      List[Any]       =    (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

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

										_UpperCamelCase      :      List[Any]       =    model.decode(_UpperCAmelCase							,		_UpperCAmelCase							,		decoder_attention_mask=_UpperCAmelCase       )

										_UpperCamelCase      :      Tuple       =    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				(  UpperCAmelCase  ,UpperCAmelCase  ,UpperCAmelCase  ,UpperCAmelCase=None  ,UpperCAmelCase=None  ,)  ->      Optional[Any]:




						'''simple docstring'''
						if attention_mask is None:
												_UpperCamelCase      :      List[str]       =    np.not_equal(__lowercase  ,config.pad_token_id       ).astype(np.inta       )
						if decoder_attention_mask is None:
												_UpperCamelCase      :      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 		SCREAMING_SNAKE_CASE  (       UpperCAmelCase__ ,			unittest.TestCase						):







				'''simple docstring'''
				UpperCAmelCase__					      =				(
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				UpperCAmelCase__					      =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				UpperCAmelCase__					      =				True
				UpperCAmelCase__					      =				False
				UpperCAmelCase__					      =				False
				UpperCAmelCase__					      =				False





				def 			snake_case__					(    self       :    str       )				->Dict:


										'''simple docstring'''




										_UpperCamelCase      :      Optional[Any]       =    FlaxPegasusModelTester(self       )
										_UpperCamelCase      :      int       =    ConfigTester(self							,		config_class=_UpperCAmelCase       )





				def 			snake_case__					(    self       :    Any       )				->str:


										'''simple docstring'''




										self.config_tester.run_common_tests()





				def 			snake_case__					(    self       :    Dict       )				->Dict:


										'''simple docstring'''




										_UpperCamelCase      ,							_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(_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase       )





				def 			snake_case__					(    self       :    Union[str, Any]       )				->Tuple:


										'''simple docstring'''




										_UpperCamelCase      ,							_UpperCamelCase      :      Tuple       =    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(_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase       )





				def 			snake_case__					(    self       :    int       )				->Dict:


										'''simple docstring'''




										_UpperCamelCase      ,							_UpperCamelCase      :      Tuple       =    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__       ):
																						_UpperCamelCase      :      int       =    self._prepare_for_class(_UpperCAmelCase							,		_UpperCAmelCase       )
																						_UpperCamelCase      :      str       =    model_class(_UpperCAmelCase       )

																						@jax.jit
																						def encode_jitted(lowercase__       :    Optional[int]							,		lowercase__       :    str=None							,		**lowercase__       :    List[str]       ):
																												return model.encode(input_ids=_UpperCAmelCase							,		attention_mask=_UpperCAmelCase       )

																						with self.subTest("JIT Enabled"       ):
																												_UpperCamelCase      :      List[str]       =    encode_jitted(**_UpperCAmelCase       ).to_tuple()

																						with self.subTest("JIT Disabled"       ):
																												with jax.disable_jit():
																																		_UpperCamelCase      :      Union[str, Any]       =    encode_jitted(**_UpperCAmelCase       ).to_tuple()

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





				def 			snake_case__					(    self       :    Dict       )				->List[str]:


										'''simple docstring'''




										_UpperCamelCase      ,							_UpperCamelCase      :      Optional[int]       =    self.model_tester.prepare_config_and_inputs_for_common()

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

																						_UpperCamelCase      :      List[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(lowercase__       :    Optional[int]							,		lowercase__       :    str							,		lowercase__       :    Dict       ):
																												return model.decode(
																												    decoder_input_ids=_UpperCAmelCase							,		decoder_attention_mask=_UpperCAmelCase							,		encoder_outputs=_UpperCAmelCase							,		)

																						with self.subTest("JIT Enabled"       ):
																												_UpperCamelCase      :      Optional[int]       =    decode_jitted(**_UpperCAmelCase       ).to_tuple()

																						with self.subTest("JIT Disabled"       ):
																												with jax.disable_jit():
																																		_UpperCamelCase      :      Union[str, Any]       =    decode_jitted(**_UpperCAmelCase       ).to_tuple()

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





				@slow
				def 			snake_case__					(    self       :    Dict       )				->int:


										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																_UpperCamelCase      :      List[Any]       =    model_class_name.from_pretrained("google/pegasus-large"							,		from_pt=_UpperCAmelCase       )
																_UpperCamelCase      :      Any       =    np.ones((1, 1)       )
																_UpperCamelCase      :      Dict       =    model(_UpperCAmelCase       )
																self.assertIsNotNone(_UpperCAmelCase       )







				@slow
				def 			snake_case__					(    self       :    Dict       )				->List[Any]:


										'''simple docstring'''




										_UpperCamelCase      :      List[str]       =    FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"       )
										_UpperCamelCase      :      Optional[Any]       =    PegasusTokenizer.from_pretrained("google/pegasus-xsum"       )

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










import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

snake_case__   :						List[str]													=   logging.get_logger(__name__)


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """vision-encoder-decoder"""
  A_						       = True




  def __init__(  self		,		**_UpperCAmelCase			)      ->			Dict:
     super().__init__(**_UpperCAmelCase			)
     if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            f"""A configuraton of type {self.model_type} cannot be instantiated because """
            f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""			)

     UpperCamelCase_						=  kwargs.pop('encoder'			)
     UpperCamelCase_						=  encoder_config.pop('model_type'			)
     UpperCamelCase_						=  kwargs.pop('decoder'			)
     UpperCamelCase_						=  decoder_config.pop('model_type'			)

     UpperCamelCase_						=  AutoConfig.for_model(_UpperCAmelCase		,		**_UpperCAmelCase			)
     UpperCamelCase_						=  AutoConfig.for_model(_UpperCAmelCase		,		**_UpperCAmelCase			)
     UpperCamelCase_						=  True




  @classmethod
  def  _UpperCAmelCase  (  cls		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			PretrainedConfig:
     logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'			)
     UpperCamelCase_						=  True
     UpperCamelCase_						=  True

     return cls(encoder=encoder_config.to_dict()		,		decoder=decoder_config.to_dict()		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_						=  copy.deepcopy(self.__dict__			)
     UpperCamelCase_						=  self.encoder.to_dict()
     UpperCamelCase_						=  self.decoder.to_dict()
     UpperCamelCase_						=  self.__class__.model_type
     return output




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = version.parse("""1.11"""     )




  @property
  def  _UpperCAmelCase  (  self			)      ->			Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]			)




  @property
  def  _UpperCAmelCase  (  self			)      ->			float:
     return 1e-4




  @property
  def  _UpperCAmelCase  (  self			)      ->			Mapping[str, Mapping[int, str]]:
     return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}			)




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @property
  def  _UpperCAmelCase  (  self			)      ->			Mapping[str, Mapping[int, str]]:
     UpperCamelCase_						=  OrderedDict()
     UpperCamelCase_						=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
     UpperCamelCase_						=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
     UpperCamelCase_						=  {0: 'batch', 1: 'encoder_sequence'}

     return common_inputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		)      ->			Mapping[str, Any]:
     import torch

     UpperCamelCase_						=  OrderedDict()

     UpperCamelCase_						=  super().generate_dummy_inputs(
         _UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		seq_length=_UpperCAmelCase		,		is_pair=_UpperCAmelCase		,		framework=_UpperCAmelCase			)

     UpperCamelCase_		,   UpperCamelCase_						=  dummy_input['input_ids'].shape
     UpperCamelCase_						=  (batch, encoder_sequence, self._config.encoder_hidden_size)
     UpperCamelCase_						=  dummy_input.pop('input_ids'			)
     UpperCamelCase_						=  dummy_input.pop('attention_mask'			)
     UpperCamelCase_						=  torch.zeros(_UpperCAmelCase			)

     return common_inputs




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @property
  def  _UpperCAmelCase  (  self			)      ->			None:
     pass




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			OnnxConfig:
     return VisionEncoderDecoderEncoderOnnxConfig(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = "default"			)      ->			OnnxConfig:
     UpperCamelCase_						=  encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(_UpperCAmelCase		,		_UpperCAmelCase			)





