



'''simple docstring'''




from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)





class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase=None	,				lowercase=None	,				lowercase=0							):
				A_     :						Optional[int]     =					1.0 if scale is None else scale
				A_     :						Tuple     =					0.0 if loc is None else loc

				super().__init__(__UpperCAmelCase	,				[AffineTransform(loc=self.loc	,				scale=self.scale	,				event_dim=__UpperCAmelCase							)]							)


	@property
	def    _a     (self							):
				return self.base_dist.mean * self.scale + self.loc


	@property
	def    _a     (self							):
				return self.base_dist.variance * self.scale**2


	@property
	def    _a     (self							):
				return self.variance.sqrt()






class      _lowerCAmelCase							(      nn.Module			):


	def __init__(self	,				lowercase	,				lowercase	,				lowercase	,				**lowercase							):
				super().__init__(**__UpperCAmelCase							)
				A_     :						Union[str, Any]     =					args_dim
				A_     :						Optional[Any]     =					nn.ModuleList([nn.Linear(__UpperCAmelCase	,				__UpperCAmelCase							) for dim in args_dim.values()]							)
				A_     :						Tuple     =					domain_map


	def    _a     (self	,				lowercase							):
				A_     :						Dict     =					[proj(__UpperCAmelCase							) for proj in self.proj]

				return self.domain_map(*__UpperCAmelCase							)






class      _lowerCAmelCase							(      nn.Module			):


	def __init__(self	,				lowercase							):
				super().__init__()
				A_     :						str     =					function


	def    _a     (self	,				lowercase	,				*lowercase							):
				return self.function(__UpperCAmelCase	,				*__UpperCAmelCase							)






class      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		Optional[Any]		      = 42
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = 42
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 42


	def __init__(self	,				lowercase = 1							):
				A_     :						Optional[int]     =					dim
				A_     :						Any     =					{k: dim * self.args_dim[k] for k in self.args_dim}


	def    _a     (self	,				lowercase							):


				if self.dim == 1:
							return self.distribution_class(*__UpperCAmelCase							)
				else:
							return Independent(self.distribution_class(*__UpperCAmelCase							)	,				1							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				):
				A_     :						List[Any]     =					self._base_distribution(__UpperCAmelCase							)
				if loc is None and scale is None:
							return distr
				else:
							return AffineTransformed(__UpperCAmelCase	,				loc=__UpperCAmelCase	,				scale=__UpperCAmelCase	,				event_dim=self.event_dim							)


	@property
	def    _a     (self							):
				return () if self.dim == 1 else (self.dim,)


	@property
	def    _a     (self							):
				return len(self.event_shape							)


	@property
	def    _a     (self							):
				return 0.0


	def    _a     (self	,				lowercase							):
				return ParameterProjection(
				    in_features=__UpperCAmelCase	,				args_dim=self.args_dim	,				domain_map=LambdaLayer(self.domain_map							)	,				)


	def    _a     (self	,				*lowercase							):
				raise NotImplementedError()


	@staticmethod
	def    _a     (lowercase							):
				return (x + torch.sqrt(torch.square(__UpperCAmelCase							) + 4.0							)) / 2.0






class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = {'df': 1, 'loc': 1, 'scale': 1}
	__SCREAMING_SNAKE_CASE       :		Dict		      = StudentT


	@classmethod
	def    _a     (cls	,				lowercase	,				lowercase	,				lowercase							):
				A_     :						List[Any]     =					cls.squareplus(__UpperCAmelCase							).clamp_min(torch.finfo(scale.dtype							).eps							)
				A_     :						Any     =					2.0 + cls.squareplus(__UpperCAmelCase							)
				return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)






class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		int		      = {'loc': 1, 'scale': 1}
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = Normal


	@classmethod
	def    _a     (cls	,				lowercase	,				lowercase							):
				A_     :						List[Any]     =					cls.squareplus(__UpperCAmelCase							).clamp_min(torch.finfo(scale.dtype							).eps							)
				return loc.squeeze(-1							), scale.squeeze(-1							)






class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		int		      = {'total_count': 1, 'logits': 1}
	__SCREAMING_SNAKE_CASE       :		Dict		      = NegativeBinomial


	@classmethod
	def    _a     (cls	,				lowercase	,				lowercase							):
				A_     :						str     =					cls.squareplus(__UpperCAmelCase							)
				return total_count.squeeze(-1							), logits.squeeze(-1							)


	def    _a     (self	,				lowercase							):
				A_,   A_     :						List[Any]     =					distr_args
				if self.dim == 1:
							return self.distribution_class(total_count=__UpperCAmelCase	,				logits=__UpperCAmelCase							)
				else:
							return Independent(self.distribution_class(total_count=__UpperCAmelCase	,				logits=__UpperCAmelCase							)	,				1							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None							):
				A_,   A_     :						List[Any]     =					distr_args

				if scale is not None:
							# See scaling property of Gamma.
							logits += scale.log()

				return self._base_distribution((total_count, logits)							)




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						int     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ]							)