

'''simple docstring'''

import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union







class lowerCamelCase      (						enum.Enum							):
				snake_case_           =   '''timm'''
				snake_case_           =   '''transformers'''

def     __magic_name__       ( A					,   A					,   A							)			->		Optional[Any]:
	if stage_names is None:
		raise ValueError('Stage_names must be set for transformers backbones'							)

	if out_features is not None:
		if not isinstance(A					,   (list,)							):
			raise ValueError(F'''out_features must be a list {type(A							)}'''							)
		if any(feat not in stage_names for feat in out_features							):
			raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''							)

	if out_indices is not None:
		if not isinstance(A					,   (list, tuple)							):
			raise ValueError(F'''out_indices must be a list or tuple, got {type(A							)}'''							)
		if any(idx >= len(A							) for idx in out_indices							):
			raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'							)

	if out_features is not None and out_indices is not None:
		if len(A							) != len(A							):
			raise ValueError('out_features and out_indices should have the same length if both are set'							)
		if out_features != [stage_names[idx] for idx in out_indices]:
			raise ValueError('out_features and out_indices should correspond to the same stages if both are set'							)

def     __magic_name__       ( A					,   A					,   A					,   )			->		List[str]:
	if out_indices is None and out_features is None:
		snake_case							 =				[len(A							) - 1]
		snake_case							 =				[stage_names[-1]]
	elif out_indices is None and out_features is not None:
		snake_case							 =				[stage_names.index(A							) for layer in out_features]
	elif out_features is None and out_indices is not None:
		snake_case							 =				[stage_names[idx] for idx in out_indices]
	return out_features, out_indices

def     __magic_name__       ( A					,   A					,   A					,   )			->		Tuple[List[str], List[int]]:
	verify_out_features_out_indices(out_features=A					,   out_indices=A					,   stage_names=A							)
	snake_case , snake_case							 =				_align_output_features_output_indices(
	    out_features=A					,   out_indices=A					,   stage_names=A							)
	# Verify that the aligned out_features and out_indices are valid
	verify_out_features_out_indices(out_features=A					,   out_indices=A					,   stage_names=A							)
	return output_features, output_indices







class lowerCamelCase      :
				snake_case_           =   None

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       None:
					if getattr(self,	'_backbone',	lowercase_   ) is None:
						raise ValueError('self._backbone must be set before calling _init_timm_backbone'   )

					# These will diagree with the defaults for the transformers models e.g. for resnet50
					# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
					# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
					snake_case							 =				[stage['module'] for stage in self._backbone.feature_info.info]
					snake_case							 =				[stage['num_chs'] for stage in self._backbone.feature_info.info]
					snake_case							 =				self._backbone.feature_info.out_indices
					snake_case							 =				self._backbone.feature_info.module_name()

					# We verify the out indices and out features are valid
					verify_out_features_out_indices(
					    out_features=lowercase_,	out_indices=lowercase_,	stage_names=self.stage_names   )
					snake_case , snake_case							 =				out_features, out_indices

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       None:
					snake_case							 =				getattr(lowercase_,	'stage_names'   )
					snake_case							 =				getattr(lowercase_,	'out_features',	lowercase_   )
					snake_case							 =				getattr(lowercase_,	'out_indices',	lowercase_   )

					snake_case							 =				stage_names
					snake_case , snake_case							 =				get_aligned_output_features_output_indices(
					    out_features=lowercase_,	out_indices=lowercase_,	stage_names=lowercase_   )
					# Number of channels for each stage. This is set in the transformer backbone model init
					snake_case							 =				None

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       None:
					snake_case							 =				config

					snake_case							 =				getattr(lowercase_,	'use_timm_backbone',	lowercase_   )
					snake_case							 =				BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

					if self.backbone_type == BackboneType.TIMM:
						self._init_timm_backbone(lowercase_   )
					elif self.backbone_type == BackboneType.TRANSFORMERS:
						self._init_transformers_backbone(lowercase_   )
					else:
						raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''   )

				@property
				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					return self._out_features

				@out_features.setter
				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Union[str, Any]:
					snake_case , snake_case							 =				get_aligned_output_features_output_indices(
					    out_features=lowercase_,	out_indices=lowercase_,	stage_names=self.stage_names   )

				@property
				def 				_lowerCamelCase						(					self   )						->       Dict:
					return self._out_indices

				@out_indices.setter
				def 				_lowerCamelCase						(					self,	lowercase_   )						->       List[str]:
					snake_case , snake_case							 =				get_aligned_output_features_output_indices(
					    out_features=lowercase_,	out_indices=lowercase_,	stage_names=self.stage_names   )

				@property
				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					# the current backbones will output the number of channels for each stage
					# even if that stage is not in the out_features list.
					return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names   )}

				@property
				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					return [self.out_feature_channels[name] for name in self.out_features]

				def 				_lowerCamelCase						(					self,	*lowercase_,	**lowercase_   )						->       Dict:
					snake_case							 =				dict(inspect.signature(self.forward   ).parameters   )
					snake_case							 =				{k: v for k, v in kwargs.items() if k in signature}
					return self(*lowercase_,	**lowercase_   )

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_ = None,	lowercase_ = None,	lowercase_ = None,	)						->       Optional[Any]:
					raise NotImplementedError('This method should be implemented by the derived class.'   )






				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				super().to_dict()
					snake_case							 =				output.pop('_out_features'   )
					snake_case							 =				output.pop('_out_indices'   )
					return output







class lowerCamelCase      :

				@property
				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					return self._out_features

				@out_features.setter
				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Any:
					snake_case , snake_case							 =				get_aligned_output_features_output_indices(
					    out_features=lowercase_,	out_indices=lowercase_,	stage_names=self.stage_names   )

				@property
				def 				_lowerCamelCase						(					self   )						->       List[str]:
					return self._out_indices

				@out_indices.setter
				def 				_lowerCamelCase						(					self,	lowercase_   )						->       int:
					snake_case , snake_case							 =				get_aligned_output_features_output_indices(
					    out_features=lowercase_,	out_indices=lowercase_,	stage_names=self.stage_names   )






				def 				_lowerCamelCase						(					self   )						->       List[str]:
					snake_case							 =				super().to_dict()
					snake_case							 =				output.pop('_out_features'   )
					snake_case							 =				output.pop('_out_indices'   )
					return output








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''mega'''

				def __init__(					self,	lowercase_=30522,	lowercase_=128,	lowercase_=4,	lowercase_=256,	lowercase_=16,	lowercase_=True,	lowercase_=64,	lowercase_=False,	lowercase_=-1,	lowercase_=None,	lowercase_=True,	lowercase_="scalenorm",	lowercase_=True,	lowercase_="silu",	lowercase_="softmax",	lowercase_=0.1,	lowercase_=0.1,	lowercase_=0.1,	lowercase_=False,	lowercase_=True,	lowercase_=256,	lowercase_=True,	lowercase_=0.1,	lowercase_=2048,	lowercase_=False,	lowercase_=2,	lowercase_=0.02,	lowercase_=0.2,	lowercase_=0.02,	lowercase_=1.0,	lowercase_=1,	lowercase_=0,	lowercase_=2,	lowercase_="rotary",	lowercase_=None,	lowercase_=True,	lowercase_=True,	**lowercase_,	)						->       List[Any]:
					super().__init__(pad_token_id=lowercase_,	bos_token_id=lowercase_,	eos_token_id=lowercase_,	**lowercase_   )

					snake_case							 =				vocab_size
					snake_case							 =				hidden_size
					snake_case							 =				num_hidden_layers
					snake_case							 =				activation
					snake_case							 =				attention_activation
					snake_case							 =				intermediate_size
					snake_case							 =				ema_projection_size
					snake_case							 =				bidirectional
					snake_case							 =				shared_representation_size
					snake_case							 =				use_chunking
					snake_case							 =				chunk_size
					snake_case							 =				truncation
					snake_case							 =				normalize_before_mega
					snake_case							 =				normalization_type
					snake_case							 =				norm_affine
					snake_case							 =				dropout_prob
					snake_case							 =				hidden_dropout_prob
					snake_case							 =				attention_probs_dropout_prob
					snake_case							 =				use_feature_dropout
					snake_case							 =				use_normalized_ffn
					snake_case							 =				nffn_hidden_size
					snake_case							 =				normalize_before_ffn
					snake_case							 =				nffn_activation_dropout_prob
					snake_case							 =				max_positions
					snake_case							 =				add_token_type_embeddings
					snake_case							 =				type_vocab_size
					snake_case							 =				initializer_range
					snake_case							 =				ema_delta_alpha_range
					snake_case							 =				ema_beta_range
					snake_case							 =				ema_gamma_omega_range
					snake_case							 =				relative_positional_bias
					snake_case							 =				use_cache
					snake_case							 =				classifier_dropout
					snake_case							 =				add_lm_hidden_dense_layer
					snake_case							 =				1  # not used but required by Hugging Face







class lowerCamelCase      (						__lowerCAmelCase							):

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





