




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							     =					logging.get_logger(__name__)

__a							     =					{
    '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(	_SCREAMING_SNAKE_CASE  ):






    """simple docstring"""





    lowerCAmelCase         = '''longformer'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE = 512  ,_SCREAMING_SNAKE_CASE = 2  ,_SCREAMING_SNAKE_CASE = 1  ,_SCREAMING_SNAKE_CASE = 0  ,_SCREAMING_SNAKE_CASE = 2  ,_SCREAMING_SNAKE_CASE = 30_522  ,_SCREAMING_SNAKE_CASE = 768  ,_SCREAMING_SNAKE_CASE = 12  ,_SCREAMING_SNAKE_CASE = 12  ,_SCREAMING_SNAKE_CASE = 3_072  ,_SCREAMING_SNAKE_CASE = "gelu"  ,_SCREAMING_SNAKE_CASE = 0.1  ,_SCREAMING_SNAKE_CASE = 0.1  ,_SCREAMING_SNAKE_CASE = 512  ,_SCREAMING_SNAKE_CASE = 2  ,_SCREAMING_SNAKE_CASE = 0.02  ,_SCREAMING_SNAKE_CASE = 1e-12  ,_SCREAMING_SNAKE_CASE = False  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[Any]:
         super().__init__(pad_token_id=_lowerCAmelCase  ,**_lowerCAmelCase    )

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


class     __a(	_SCREAMING_SNAKE_CASE  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = "default"  ,_SCREAMING_SNAKE_CASE = None    )					->						str:
         super().__init__(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase    )
         UpperCAmelCase_     :				Union[str, Any]  =     True





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





    @property
    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Dict  =     super().outputs
         if self.task == "default":
              UpperCAmelCase_     :				List[Any]  =     {0: '''batch'''}
         return outputs





    @property
    def 				a__ ( self    )					->						Any:
         return 1e-4





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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,)					->						Union[str, Any]:
         UpperCAmelCase_     :				Union[str, Any]  =     super().generate_dummy_inputs(
             preprocessor=_lowerCAmelCase  ,batch_size=_lowerCAmelCase  ,seq_length=_lowerCAmelCase  ,is_pair=_lowerCAmelCase  ,framework=_lowerCAmelCase    )
         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
         UpperCAmelCase_     :				Union[str, Any]  =     torch.zeros_like(inputs['''input_ids''']    )
         # make every second token global
         UpperCAmelCase_     :				List[Any]  =     1

         return inputs





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




 def __getitem__(				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]		):
       return {"x": self.x[i], "y": self.y[i]}





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

      SCREAMING_SNAKE_CASE_      		=     AutoTokenizer.from_pretrained('bert-base-cased'				)
      SCREAMING_SNAKE_CASE_      		=     {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      SCREAMING_SNAKE_CASE_      		=     load_dataset('csv'     ,     data_files=__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     datasets['train'].unique('label'				)

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

      def tokenize_function(__UpperCAmelCase		:      Optional[int]				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE_      		=     tokenizer(
                examples['sentence1']     ,     examples['sentence2']     ,     truncation=__UpperCAmelCase     ,     max_length=__UpperCAmelCase     ,     padding='max_length'				)
            if "label" in examples:
                  SCREAMING_SNAKE_CASE_      		=     [label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      SCREAMING_SNAKE_CASE_      		=     datasets.map(
          __UpperCAmelCase     ,     batched=__UpperCAmelCase     ,     remove_columns=['sentence1', 'sentence2', 'label']     ,     )

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(__UpperCAmelCase     ,     padding='max_length'     ,     max_length=1_28     ,     return_tensors='pt'				)
            return tokenizer.pad(__UpperCAmelCase     ,     padding='longest'     ,     return_tensors='pt'				)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['train']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=2				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['validation']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=1				)

      return train_dataloader, eval_dataloader