



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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Optional[Any]			       =    "longformer"



					def __init__(     self					:       int				,					_snake_case					:       Union[List[int], int] = 5_12				,					_snake_case					:       int = 2				,					_snake_case					:       int = 1				,					_snake_case					:       int = 0				,					_snake_case					:       int = 2				,					_snake_case					:       int = 3_05_22				,					_snake_case					:       int = 7_68				,					_snake_case					:       int = 12				,					_snake_case					:       int = 12				,					_snake_case					:       int = 30_72				,					_snake_case					:       str = "gelu"				,					_snake_case					:       float = 0.1				,					_snake_case					:       float = 0.1				,					_snake_case					:       int = 5_12				,					_snake_case					:       int = 2				,					_snake_case					:       float = 0.02				,					_snake_case					:       float = 1E-12				,					_snake_case					:       bool = False				,					**_snake_case					:       str				,					):






								"""simple docstring"""


								super().__init__(pad_token_id=_snake_case				,					**_snake_case					)

								A__       =    attention_window
								A__       =    sep_token_id
								A__       =    bos_token_id
								A__       =    eos_token_id
								A__       =    vocab_size
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    hidden_act
								A__       =    intermediate_size
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    max_position_embeddings
								A__       =    type_vocab_size
								A__       =    initializer_range
								A__       =    layer_norm_eps
								A__       =    onnx_export







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       str				,					_snake_case					:       "PretrainedConfig"				,					_snake_case					:       str = "default"				,					_snake_case					:       "List[PatchingSpec]" = None					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case				,					_snake_case					)
								A__       =    True



					@property
					def     _a							(     self					:       str					):






								"""simple docstring"""


								if self.task == "multiple-choice":
											A__       =    {0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
											A__       =    {0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								        ('global_attention_mask', dynamic_axis),
								    ]					)



					@property
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    super().outputs
								if self.task == "default":
											A__       =    {0: 'batch'}
								return outputs



					@property
					def     _a							(     self					:       str					):






								"""simple docstring"""


								return 1E-4



					@property
					def     _a							(     self					:       str					):






								"""simple docstring"""


								return max(super().default_onnx_opset				,					14					)



					def     _a							(     self					:       Tuple				,					_snake_case					:       "PreTrainedTokenizerBase"				,					_snake_case					:       int = -1				,					_snake_case					:       int = -1				,					_snake_case					:       bool = False				,					_snake_case					:       Optional[TensorType] = None				,					):






								"""simple docstring"""


								A__       =    super().generate_dummy_inputs(
								    preprocessor=_snake_case				,					batch_size=_snake_case				,					seq_length=_snake_case				,					is_pair=_snake_case				,					framework=_snake_case					)
								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__       =    torch.zeros_like(inputs['input_ids']					)
								# make every second token global
								A__       =    1

								return inputs









import pytest

import datasets


# Import fixture modules as plugins
SCREAMING_SNAKE_CASE__       =      ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Optional[int]:
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ['integration', 'unit']					):
									continue
						item.add_marker(pytest.mark.unit					)


def A						(       __UpperCamelCase					)						->      str:
			config.addinivalue_line('markers' ,	'torchaudio_latest: mark test to run with torchaudio>=0.12'					)


@pytest.fixture(autouse=__UpperCamelCase					)
def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Union[str, Any]:
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			A__       =    tmp_path_factory.getbasetemp() / 'cache'
			A__       =    test_hf_cache_home / 'datasets'
			A__       =    test_hf_cache_home / 'metrics'
			A__       =    test_hf_cache_home / 'modules'
			monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' ,	str(__UpperCamelCase					)					)
			monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' ,	str(__UpperCamelCase					)					)
			monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' ,	str(__UpperCamelCase					)					)
			A__       =    test_hf_datasets_cache / 'downloads'
			monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' ,	str(__UpperCamelCase					)					)
			A__       =    test_hf_datasets_cache / 'downloads' / 'extracted'
			monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,	str(__UpperCamelCase					)					)


@pytest.fixture(autouse=__UpperCamelCase ,	scope='session'					)
def A						(       )						->      Union[str, Any]:
			datasets.disable_progress_bar()


@pytest.fixture(autouse=__UpperCamelCase					)
def A						(       __UpperCamelCase					)						->      int:
			# don't take tests into account when counting downloads
			monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' ,	__UpperCamelCase					)






@pytest.fixture
def A						(       __UpperCamelCase					)						->      Any:
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' ,	__UpperCamelCase					)




