
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__   :			int												=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :			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    lowerCAmelCase__     (	__lowercase			):
		a__       :						List[str]        =		"""longformer"""






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Union[List[int], int] = 5_12							,			SCREAMING_SNAKE_CASE__		:							int = 2							,			SCREAMING_SNAKE_CASE__		:							int = 1							,			SCREAMING_SNAKE_CASE__		:							int = 0							,			SCREAMING_SNAKE_CASE__		:							int = 2							,			SCREAMING_SNAKE_CASE__		:							int = 3_05_22							,			SCREAMING_SNAKE_CASE__		:							int = 7_68							,			SCREAMING_SNAKE_CASE__		:							int = 12							,			SCREAMING_SNAKE_CASE__		:							int = 12							,			SCREAMING_SNAKE_CASE__		:							int = 30_72							,			SCREAMING_SNAKE_CASE__		:							str = "gelu"							,			SCREAMING_SNAKE_CASE__		:							float = 0.1							,			SCREAMING_SNAKE_CASE__		:							float = 0.1							,			SCREAMING_SNAKE_CASE__		:							int = 5_12							,			SCREAMING_SNAKE_CASE__		:							int = 2							,			SCREAMING_SNAKE_CASE__		:							float = 0.02							,			SCREAMING_SNAKE_CASE__		:							float = 1e-12							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			**SCREAMING_SNAKE_CASE__		:							Dict							,			)     ->							Tuple:
						super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )

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





class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							int							,			SCREAMING_SNAKE_CASE__		:							"PretrainedConfig"							,			SCREAMING_SNAKE_CASE__		:							str = "default"							,			SCREAMING_SNAKE_CASE__		:							"List[PatchingSpec]" = None  )     ->							List[str]:
						super().__init__(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	True






		@property
		def  __A       (		self		:							List[str]  )     ->							Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
										__lowerCamelCase    					=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
										__lowerCamelCase    					=	{0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						        ('''global_attention_mask''', dynamic_axis),
						    ]  )






		@property
		def  __A       (		self		:							Dict  )     ->							Mapping[str, Mapping[int, str]]:
						__lowerCamelCase    					=	super().outputs
						if self.task == "default":
										__lowerCamelCase    					=	{0: '''batch'''}
						return outputs






		@property
		def  __A       (		self		:							int  )     ->							float:
						return 1e-4






		@property
		def  __A       (		self		:							Optional[Any]  )     ->							int:
						# needs to be >= 14 to support tril operator
						return max(super().default_onnx_opset							,			14  )







		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							"PreTrainedTokenizerBase"							,			SCREAMING_SNAKE_CASE__		:							int = -1							,			SCREAMING_SNAKE_CASE__		:							int = -1							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							Optional[TensorType] = None							,			)     ->							Mapping[str, Any]:
						__lowerCamelCase    					=	super().generate_dummy_inputs(
						    preprocessor=SCREAMING_SNAKE_CASE__							,			batch_size=SCREAMING_SNAKE_CASE__							,			seq_length=SCREAMING_SNAKE_CASE__							,			is_pair=SCREAMING_SNAKE_CASE__							,			framework=SCREAMING_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
						__lowerCamelCase    					=	torch.zeros_like(inputs['''input_ids''']  )
						# make every second token global
						__lowerCamelCase    					=	1

						return inputs





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "WavLMForAudioFrameClassification",
					    "WavLMForCTC",
					    "WavLMForSequenceClassification",
					    "WavLMForXVector",
					    "WavLMModel",
					    "WavLMPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavlm import (
										    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavLMForAudioFrameClassification,
										    WavLMForCTC,
										    WavLMForSequenceClassification,
										    WavLMForXVector,
										    WavLMModel,
										    WavLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			int												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



