




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


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Dict          =  {
    "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   (      UpperCamelCase__      ):
						UpperCamelCase      =       """longformer"""

						def __init__(		self :Optional[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 :List[str]     ,			):
										'''simple docstring'''

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

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




class 		__A   (      UpperCamelCase__      ):

						def __init__(		self :Optional[Any]     ,			__snake_case :"PretrainedConfig"     ,			__snake_case :str = "default"     ,			__snake_case :"List[PatchingSpec]" = None			):
										'''simple docstring'''

										super().__init__(__snake_case     ,			__snake_case     ,			__snake_case			)
										__magic_name__						:						Optional[Any]										=True

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=super().outputs
										if self.task == "default":
														__magic_name__						:						List[str]										={0: """batch"""}
										return outputs

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return 1E-4

						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return max(super().default_onnx_opset     ,			14			)


						def      A__    (		self :int     ,			__snake_case :"PreTrainedTokenizerBase"     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Dict										=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
										__magic_name__						:						List[Any]										=torch.zeros_like(inputs["""input_ids"""]			)
										# make every second token global
										__magic_name__						:						Optional[int]										=1

										return inputs








'''simple docstring'''






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCAmelCase       							=				[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def    _snake_case  (	A					,       A=None					,       A=None					,       A=None			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      True
							while ask_again:
														lowerCAmelCase__			  =      input(A			)
														try:
																					if default is not None and len(A			) == 0:
																												return default
																					return convert_value(A			) if convert_value is not None else result
														except Exception:
																					if error_message is not None:
																												print(A			)


def    _snake_case  (	A					,       A=[]					,       A=None					,       A=0			)		->       List[Any]:
							lowerCAmelCase__			  =      BulletMenu(A					,       A			)
							lowerCAmelCase__			  =      menu.run(default_choice=A			)
							return convert_value(A			) if convert_value is not None else result


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]			)


def    _snake_case  (	A			)		->       str:
							lowerCAmelCase__			  =      int(A			)
							return DynamoBackend(DYNAMO_BACKENDS[value]			).value


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]			)


def    _snake_case  (	A			)		->       List[str]:
							return {"yes": True, "no": False}[value.lower()]






class 						a__							(				argparse.RawDescriptionHelpFormatter    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      super()._format_usage(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      usage.replace('''<command> [<args>] '''   ,       ''''''  )
														return usage