
"""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


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{
    """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__	(     _lowercase):
		A_						:	List[str]           =     'longformer'

		def __init__(				self	,		_SCREAMING_SNAKE_CASE = 5_12	,		_SCREAMING_SNAKE_CASE = 2	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = 0	,		_SCREAMING_SNAKE_CASE = 2	,		_SCREAMING_SNAKE_CASE = 3_05_22	,		_SCREAMING_SNAKE_CASE = 7_68	,		_SCREAMING_SNAKE_CASE = 12	,		_SCREAMING_SNAKE_CASE = 12	,		_SCREAMING_SNAKE_CASE = 30_72	,		_SCREAMING_SNAKE_CASE = "gelu"	,		_SCREAMING_SNAKE_CASE = 0.1	,		_SCREAMING_SNAKE_CASE = 0.1	,		_SCREAMING_SNAKE_CASE = 5_12	,		_SCREAMING_SNAKE_CASE = 2	,		_SCREAMING_SNAKE_CASE = 0.02	,		_SCREAMING_SNAKE_CASE = 1E-12	,		_SCREAMING_SNAKE_CASE = False	,		**_SCREAMING_SNAKE_CASE	,		):
				super().__init__(pad_token_id=__UpperCamelCase	,		**__UpperCamelCase			)

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




class 			A__	(     _lowercase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = "default"	,		_SCREAMING_SNAKE_CASE = None			):
				super().__init__(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase			)
				__lowerCAmelCase      :						List[str]						      =				True

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

		@property
		def 							__lowerCamelCase							(				self			):
				__lowerCAmelCase      :						Optional[int]						      =				super().outputs
				if self.task == "default":
						__lowerCAmelCase      :						Dict						      =				{0: 'batch'}
				return outputs

		@property
		def 							__lowerCamelCase							(				self			):
				return 1E-4

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







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = -1	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = None	,		):
				__lowerCAmelCase      :						str						      =				super().generate_dummy_inputs(
				    preprocessor=__UpperCamelCase	,		batch_size=__UpperCamelCase	,		seq_length=__UpperCamelCase	,		is_pair=__UpperCamelCase	,		framework=__UpperCamelCase			)
				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      :						Any						      =				torch.zeros_like(inputs['input_ids']			)
				# make every second token global
				__lowerCAmelCase      :						Any						      =				1

				return inputs

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)