
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_a   :      Optional[Any]       =				logging.get_logger(__name__)

_a   :      List[Any]       =				{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       """bigbird_pegasus"""
    __lowercase				:			str	=       ["""past_key_values"""]
    __lowercase				:			str	=       {
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }
    def __init__(     self    ,     snake_case_=9_61_03    ,     snake_case_=40_96    ,     snake_case_=16    ,     snake_case_=40_96    ,     snake_case_=16    ,     snake_case_=16    ,     snake_case_=40_96    ,     snake_case_=16    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_="gelu_new"    ,     snake_case_=10_24    ,     snake_case_=0.1    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=0.02    ,     snake_case_=2    ,     snake_case_=0.0    ,     snake_case_=True    ,     snake_case_=0    ,     snake_case_=2    ,     snake_case_=1    ,     snake_case_="block_sparse"    ,     snake_case_=64    ,     snake_case_=3    ,     snake_case_=False    ,     **snake_case_    ,     ):
      _snake_case  :    Tuple            = vocab_size
      _snake_case  :    Dict            = max_position_embeddings
      _snake_case  :    Optional[Any]            = d_model
      _snake_case  :    Optional[int]            = encoder_ffn_dim
      _snake_case  :    List[Any]            = encoder_layers
      _snake_case  :    Dict            = encoder_attention_heads
      _snake_case  :    List[Any]            = decoder_ffn_dim
      _snake_case  :    List[Any]            = decoder_layers
      _snake_case  :    Optional[int]            = decoder_attention_heads
      _snake_case  :    Dict            = dropout
      _snake_case  :    str            = attention_dropout
      _snake_case  :    Tuple            = activation_dropout
      _snake_case  :    Union[str, Any]            = activation_function
      _snake_case  :    Optional[int]            = init_std
      _snake_case  :    str            = encoder_layerdrop
      _snake_case  :    List[str]            = decoder_layerdrop
      _snake_case  :    Any            = classifier_dropout
      _snake_case  :    List[str]            = use_cache
      _snake_case  :    Dict            = encoder_layers
      _snake_case  :    str            = scale_embedding  # scale factor will be sqrt(d_model) if True

      # extra config
      _snake_case  :    List[Any]            = attention_type
      _snake_case  :    int            = block_size
      _snake_case  :    Union[str, Any]            = num_random_blocks
      _snake_case  :    Tuple            = use_bias

      super().__init__(
          pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     is_encoder_decoder=snake_case_    ,     decoder_start_token_id=snake_case_    ,     **snake_case_    ,     )






class   _UpperCAmelCase					(       _snake_case):
    @property
    def        lowerCamelCase__						(     self  ):
      if self.task in ["default", "seq2seq-lm"]:
        _snake_case  :    Tuple            = OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]  )

        if self.use_past:
          _snake_case  :    List[str]            = {0: "batch"}
          _snake_case  :    str            = {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
          _snake_case  :    int            = {0: "batch", 1: "decoder_sequence"}
          _snake_case  :    Dict            = {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
          self.fill_with_past_key_values_(snake_case_    ,     direction="inputs"  )
      elif self.task == "causal-lm":
        # TODO: figure this case out.
        _snake_case  :    Optional[int]            = OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]  )
        if self.use_past:
          _snake_case      ,       _snake_case  :    Tuple            = self.num_layers
          for i in range(snake_case_  ):
            _snake_case  :    int            = {0: "batch", 2: "past_sequence + sequence"}
            _snake_case  :    Optional[Any]            = {0: "batch", 2: "past_sequence + sequence"}
      else:
        _snake_case  :    List[Any]            = OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
            ]  )

      return common_inputs
    @property
    def        lowerCamelCase__						(     self  ):
      if self.task in ["default", "seq2seq-lm"]:
        _snake_case  :    int            = super().outputs
      else:
        _snake_case  :    str            = super(snake_case_    ,     self  ).outputs
        if self.use_past:
          _snake_case      ,       _snake_case  :    Union[str, Any]            = self.num_layers
          for i in range(snake_case_  ):
            _snake_case  :    Dict            = {0: "batch", 2: "past_sequence + sequence"}
            _snake_case  :    str            = {0: "batch", 2: "past_sequence + sequence"}
      return common_outputs
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = -1    ,     snake_case_ = -1    ,     snake_case_ = False    ,     snake_case_ = None    ,     ):
      _snake_case  :    List[str]            = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )

      # Generate decoder inputs
      _snake_case  :    List[str]            = seq_length if not self.use_past else 1
      _snake_case  :    Optional[int]            = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )
      _snake_case  :    Optional[int]            = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
      _snake_case  :    Optional[Any]            = dict(**snake_case_    ,     **snake_case_  )

      if self.use_past:
        if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
        else:
          import torch
        _snake_case      ,       _snake_case  :    Any            = common_inputs["input_ids"].shape
        _snake_case  :    Optional[Any]            = common_inputs["decoder_input_ids"].shape[1]
        _snake_case      ,       _snake_case  :    Union[str, Any]            = self.num_attention_heads
        _snake_case  :    Tuple            = (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        _snake_case  :    Optional[int]            = decoder_seq_length + 3
        _snake_case  :    List[str]            = (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        _snake_case  :    Tuple            = torch.cat(
            [common_inputs["decoder_attention_mask"], torch.ones(snake_case_    ,     snake_case_  )]    ,     dim=1  )

        _snake_case  :    List[Any]            = []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        _snake_case      ,       _snake_case  :    Optional[int]            = self.num_layers
        _snake_case  :    Any            = min(snake_case_    ,     snake_case_  )
        _snake_case  :    str            = max(snake_case_    ,     snake_case_  ) - min_num_layers
        _snake_case  :    List[Any]            = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(snake_case_  ):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(snake_case_  ),
                  torch.zeros(snake_case_  ),
                  torch.zeros(snake_case_  ),
                  torch.zeros(snake_case_  ),
              )  )
        # TODO: test this.
        _snake_case  :    List[Any]            = encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(snake_case_    ,     snake_case_  ):
          common_inputs["past_key_values"].append((torch.zeros(snake_case_  ), torch.zeros(snake_case_  ))  )
      return common_inputs
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = -1    ,     snake_case_ = -1    ,     snake_case_ = False    ,     snake_case_ = None    ,     ):
      _snake_case  :    str            = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )

      if self.use_past:
        if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
        else:
          import torch
        _snake_case      ,       _snake_case  :    List[Any]            = common_inputs["input_ids"].shape
        # Not using the same length for past_key_values
        _snake_case  :    List[str]            = seqlen + 2
        _snake_case      ,       _snake_case  :    str            = self.num_layers
        _snake_case      ,       _snake_case  :    Dict            = self.num_attention_heads
        _snake_case  :    Optional[int]            = (
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        _snake_case  :    Union[str, Any]            = common_inputs["attention_mask"].dtype
        _snake_case  :    Tuple            = torch.cat(
            [common_inputs["attention_mask"], torch.ones(snake_case_    ,     snake_case_    ,     dtype=snake_case_  )]    ,     dim=1  )
        _snake_case  :    Union[str, Any]            = [
            (torch.zeros(snake_case_  ), torch.zeros(snake_case_  )) for _ in range(snake_case_  )
        ]
      return common_inputs
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = -1    ,     snake_case_ = -1    ,     snake_case_ = False    ,     snake_case_ = None    ,     ):
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      _snake_case  :    List[Any]            = compute_effective_axis_dimension(
          snake_case_    ,     fixed_dimension=OnnxConfig.default_fixed_batch    ,     num_token_to_add=0  )

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      _snake_case  :    Union[str, Any]            = tokenizer.num_special_tokens_to_add(snake_case_  )
      _snake_case  :    Any            = compute_effective_axis_dimension(
          snake_case_    ,     fixed_dimension=OnnxConfig.default_fixed_sequence    ,     num_token_to_add=snake_case_  )

      # Generate dummy inputs according to compute batch and sequence
      _snake_case  :    List[str]            = [" ".join([tokenizer.unk_token]  ) * seq_length] * batch_size
      _snake_case  :    Tuple            = dict(tokenizer(snake_case_    ,     return_tensors=snake_case_  )  )
      return common_inputs
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = -1    ,     snake_case_ = -1    ,     snake_case_ = False    ,     snake_case_ = None    ,     ):
      if self.task in ["default", "seq2seq-lm"]:
        _snake_case  :    Union[str, Any]            = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            snake_case_    ,     batch_size=snake_case_    ,     seq_length=snake_case_    ,     is_pair=snake_case_    ,     framework=snake_case_  )

      elif self.task == "causal-lm":
        _snake_case  :    Union[str, Any]            = self._generate_dummy_inputs_for_causal_lm(
            snake_case_    ,     batch_size=snake_case_    ,     seq_length=snake_case_    ,     is_pair=snake_case_    ,     framework=snake_case_  )
      else:
        _snake_case  :    Optional[int]            = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case_    ,     batch_size=snake_case_    ,     seq_length=snake_case_    ,     is_pair=snake_case_    ,     framework=snake_case_  )

      return common_inputs


    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      if self.task in ["default", "seq2seq-lm"]:
        _snake_case  :    int            = super()._flatten_past_key_values_(snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )
      else:
        _snake_case  :    Union[str, Any]            = super(snake_case_    ,     self  )._flatten_past_key_values_(
            snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )



"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


_a   :      List[Any]       =				-1 if torch_device == """cpu""" else 0






@is_pipeline_test
class   _UpperCAmelCase					(       unittest.TestCase):
    __lowercase				:			List[str]	=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __lowercase				:			str	=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = SummarizationPipeline(model=snake_case_    ,     tokenizer=snake_case_  )
      return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = summarizer.model

      _snake_case  :    Optional[int]            = summarizer("(CNN)The Palestinian Authority officially became"  )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      _snake_case  :    List[str]            = summarizer(
          "(CNN)The Palestinian Authority officially became "    ,     num_beams=2    ,     min_length=2    ,     max_length=5    ,     )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
      _snake_case  :    Tuple            = [
          "SwitchTransformersConfig",
          "T5Config",
          "LongT5Config",
          "LEDConfig",
          "PegasusXConfig",
          "FSMTConfig",
          "M2M100Config",
          "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
      ]
      if model.config.__class__.__name__ not in model_can_handle_longer_seq:
        # Too long and exception is expected.
        # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
        # the embedding layer.
        if not (
            isinstance(snake_case_    ,     snake_case_  )
            and get_gpu_count() > 0
            and len(summarizer.model.trainable_weights  ) > 0
        ):
          with self.assertRaises(snake_case_  ):
            _snake_case  :    Tuple            = summarizer("This " * 10_00  )
      _snake_case  :    Any            = summarizer("This " * 10_00    ,     truncation=TruncationStrategy.ONLY_FIRST  )
    @require_torch
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="pt"  )
      _snake_case  :    List[Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )
    @require_tf
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="tf"  )
      _snake_case  :    Union[str, Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )


    @require_torch
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = pipeline(task="summarization"    ,     device=snake_case_  )
      _snake_case  :    Any            = (
          " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
          " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
          " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
          " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
          " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
          " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
          " situation in Palestinian territories, paving the way for possible war crimes investigations against"
          " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
          " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
          " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
          " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
          " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
          " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
          " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
          " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
          " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
          " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
          " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
          " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
          " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
          " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
          " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
          " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
          " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
          " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
          " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
          " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
          " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
          " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
          " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
          " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
          " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
          " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
          " will include alleged war crimes committed since June. The International Criminal Court was set up in"
          " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
          " and Faith Karimi contributed to this report."
      )
      _snake_case  :    int            = (
          " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
          " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
          " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
          " says governments seeking to penalize Palestine should end pressure ."
      )
      _snake_case  :    Union[str, Any]            = summarizer(snake_case_  )
      self.assertEqual(result[0]["summary_text"]    ,     snake_case_  )

