


"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel






@require_tf
class 						_A  :







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	PegasusConfig
    UpperCAmelCase	:							int     =	{}
    UpperCAmelCase	:							Union[str, Any]     =	"""gelu"""






    def __init__( self    :   Dict    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[Any]=13    ,			__UpperCAmelCase    :   Dict=7    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   List[str]=False    ,			__UpperCAmelCase    :   Tuple=99    ,			__UpperCAmelCase    :   str=32    ,			__UpperCAmelCase    :   Dict=2    ,			__UpperCAmelCase    :   List[str]=4    ,			__UpperCAmelCase    :   Tuple=37    ,			__UpperCAmelCase    :   Tuple=0.1    ,			__UpperCAmelCase    :   Union[str, Any]=0.1    ,			__UpperCAmelCase    :   int=40    ,			__UpperCAmelCase    :   Optional[int]=2    ,			__UpperCAmelCase    :   Dict=1    ,			__UpperCAmelCase    :   Dict=0    ,			):
      a						:		Dict											=      parent
      a						:		Tuple											=      batch_size
      a						:		Dict											=      seq_length
      a						:		Dict											=      is_training
      a						:		List[str]											=      use_labels
      a						:		Optional[int]											=      vocab_size
      a						:		Tuple											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		Optional[Any]											=      num_attention_heads
      a						:		List[Any]											=      intermediate_size

      a						:		Tuple											=      hidden_dropout_prob
      a						:		int											=      attention_probs_dropout_prob
      a						:		Union[str, Any]											=      max_position_embeddings
      a						:		List[Any]											=      eos_token_id
      a						:		Any											=      pad_token_id
      a						:		Tuple											=      bos_token_id






    def      __snake_case							( self    :   List[str]):
      a						:		Union[str, Any]											=      ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size)
      a						:		str											=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)    ,			1)
      a						:		str											=      tf.concat([input_ids, eos_tensor]    ,			axis=1)

      a						:		Optional[Any]											=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

      a						:		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    ,			)
      a						:		Optional[int]											=      prepare_pegasus_inputs_dict(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      return config, inputs_dict






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		int											=      TFPegasusModel(config=__UpperCAmelCase).get_decoder()
      a						:		Optional[Any]											=      inputs_dict["input_ids"]

      a						:		Any											=      input_ids[:1, :]
      a						:		Optional[int]											=      inputs_dict["attention_mask"][:1, :]
      a						:		int											=      inputs_dict["head_mask"]
      a						:		List[Any]											=      1

      # first forward pass
      a						:		Tuple											=      model(__UpperCAmelCase    ,			attention_mask=__UpperCAmelCase    ,			head_mask=__UpperCAmelCase    ,			use_cache=__UpperCAmelCase)

      a							,		a						:		Any											=      outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      a						:		Dict											=      ids_tensor((self.batch_size, 3)    ,			config.vocab_size)
      a						:		Union[str, Any]											=      tf.cast(ids_tensor((self.batch_size, 3)    ,			2)    ,			tf.inta)

      # append to next input_ids and
      a						:		Any											=      tf.concat([input_ids, next_tokens]    ,			axis=-1)
      a						:		List[str]											=      tf.concat([attention_mask, next_attn_mask]    ,			axis=-1)

      a						:		List[Any]											=      model(__UpperCAmelCase    ,			attention_mask=__UpperCAmelCase)[0]
      a						:		Optional[Any]											=      model(__UpperCAmelCase    ,			attention_mask=__UpperCAmelCase    ,			past_key_values=__UpperCAmelCase)[0]

      self.parent.assertEqual(next_tokens.shape[1]    ,			output_from_past.shape[1])

      # select random slice
      a						:		List[Any]											=      int(ids_tensor((1,)    ,			output_from_past.shape[-1]))
      a						:		Optional[int]											=      output_from_no_past[:, -3:, random_slice_idx]
      a						:		Union[str, Any]											=      output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(__UpperCAmelCase    ,			__UpperCAmelCase    ,			rtol=1e-3)






def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=None  ,       A_=None  ,       A_=None  ,       A_=None  ,       A_=None  ,       )->				List[str]:
  '''simple docstring'''







  if attention_mask is None:
    a						:		Optional[int]											=      tf.cast(tf.math.not_equal(A_  ,       config.pad_token_id	)  ,       tf.inta	)
  if decoder_attention_mask is None:
    a						:		List[str]											=      tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape  ,       dtype=tf.inta	),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,       config.pad_token_id	)  ,       tf.inta	),
        ]  ,       axis=-1  ,       )
  if head_mask is None:
    a						:		str											=      tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    a						:		List[Any]											=      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
  if cross_attn_head_mask is None:
    a						:		Dict											=      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }






@require_tf
class 						_A  (  _a   ,_a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							Any     =	(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    UpperCAmelCase	:							Tuple     =	(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    UpperCAmelCase	:							str     =	(
        {
            """conversational""": TFPegasusForConditionalGeneration,
            """feature-extraction""": TFPegasusModel,
            """summarization""": TFPegasusForConditionalGeneration,
            """text2text-generation""": TFPegasusForConditionalGeneration,
            """translation""": TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    UpperCAmelCase	:							int     =	True
    UpperCAmelCase	:							str     =	False
    UpperCAmelCase	:							Tuple     =	False






    def      __snake_case							( self    :   List[Any]):
      a						:		List[str]											=      TFPegasusModelTester(self)
      a						:		str											=      ConfigTester(self    ,			config_class=__UpperCAmelCase)






    def      __snake_case							( self    :   int):
      self.config_tester.run_common_tests()






    def      __snake_case							( self    :   int):
      a						:		List[Any]											=      self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase)






@require_sentencepiece
@require_tokenizers
@require_tf
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							List[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	:							List[Any]     =	[
        """California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
        """ reduce the risk of wildfires.""",
        """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    UpperCAmelCase	:							Dict     =	"""google/pegasus-xsum"""






    @cached_property
    def      __snake_case							( self    :   Optional[Any]):
      return AutoTokenizer.from_pretrained(self.model_name)






    @cached_property
    def      __snake_case							( self    :   Dict):
      a						:		str											=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
      return model






    def      __snake_case							( self    :   int    ,			**__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[int]											=      self.translate_src_text(**__UpperCAmelCase)
      assert self.expected_text == generated_words






    def      __snake_case							( self    :   List[str]    ,			**__UpperCAmelCase    :   int):
      a						:		Tuple											=      self.tokenizer(self.src_text    ,			**__UpperCAmelCase    ,			padding=__UpperCAmelCase    ,			return_tensors="tf")
      a						:		str											=      self.model.generate(
          model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2    ,			use_cache=__UpperCAmelCase    ,			)
      a						:		Tuple											=      self.tokenizer.batch_decode(generated_ids.numpy()    ,			skip_special_tokens=__UpperCAmelCase)
      return generated_words






    @slow
    def      __snake_case							( self    :   Any):
      self._assert_generated_batch_equal_expected()










"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






    @property
    def      __snake_case							( self    :   int):
      if self.task == "multiple-choice":
        a						:		List[Any]											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		int											=      {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("global_attention_mask", dynamic_axis),
          ])






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






