
"""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


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

_A     :			List[str]  						=			{
    """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_		):
   __lowerCAmelCase				=							"""longformer"""
   def __init__(     self      ,       _a = 512      ,       _a = 2      ,       _a = 1      ,       _a = 0      ,       _a = 2      ,       _a = 30_522      ,       _a = 768      ,       _a = 12      ,       _a = 12      ,       _a = 3_072      ,       _a = "gelu"      ,       _a = 0.1      ,       _a = 0.1      ,       _a = 512      ,       _a = 2      ,       _a = 0.0_2      ,       _a = 1E-12      ,       _a = False      ,       **_a      ,       ):
          super().__init__(pad_token_id=_a      ,       **_a							)

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


class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a = "default"      ,       _a = None							):
          super().__init__(_a      ,       _a      ,       _a							)
          lowercase       :							List[str]									=				True
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							str									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Tuple									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("global_attention_mask", dynamic_axis),
              ]							)
   @property
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				super().outputs
          if self.task == "default":
                 lowercase       :							Tuple									=				{0: "batch"}
          return outputs
   @property
   def      __magic_name__     (     self							):
          return 1E-4
   @property
   def      __magic_name__     (     self							):
          # needs to be >= 14 to support tril operator
          return max(super().default_onnx_opset      ,       14							)

   def      __magic_name__     (     self      ,       _a      ,       _a = -1      ,       _a = -1      ,       _a = False      ,       _a = None      ,       ):
          lowercase       :							Tuple									=				super().generate_dummy_inputs(
              preprocessor=_a      ,       batch_size=_a      ,       seq_length=_a      ,       is_pair=_a      ,       framework=_a							)
          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
          lowercase       :							Any									=				torch.zeros_like(inputs["input_ids"]							)
          # make every second token global
          lowercase       :							Optional[Any]									=				1

          return inputs




"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


