

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 	A_		(	SCREAMING_SNAKE_CASE    ):
							def __init__(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Distribution       ,SCREAMING_SNAKE_CASE__		:      Dict=None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=None       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0):
								__lowerCamelCase    :    Optional[Any] 	=			1.0 if scale is None else scale
								__lowerCamelCase    :    int 	=			0.0 if loc is None else loc

								super().__init__(SCREAMING_SNAKE_CASE__       ,[AffineTransform(loc=self.loc       ,scale=self.scale       ,event_dim=SCREAMING_SNAKE_CASE__)])
							@property
							def    lowerCAmelCase					(		self		:      Any):
								return self.base_dist.mean * self.scale + self.loc
							@property
							def    lowerCAmelCase					(		self		:      List[str]):
								return self.base_dist.variance * self.scale**2


							@property
							def    lowerCAmelCase					(		self		:      Any):
								return self.variance.sqrt()






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Dict[str, int]       ,SCREAMING_SNAKE_CASE__		:      Callable[..., Tuple[torch.Tensor]]       ,**SCREAMING_SNAKE_CASE__		:      Any):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			args_dim
								__lowerCamelCase    :    str 	=			nn.ModuleList([nn.Linear(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__) for dim in args_dim.values()])
								__lowerCamelCase    :    str 	=			domain_map


							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								__lowerCamelCase    :    Any 	=			[proj(SCREAMING_SNAKE_CASE__) for proj in self.proj]

								return self.domain_map(*SCREAMING_SNAKE_CASE__)






class 	A_		(	nn.Module    ):
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								super().__init__()
								__lowerCamelCase    :    Optional[int] 	=			function


							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,*SCREAMING_SNAKE_CASE__		:      Any):
								return self.function(SCREAMING_SNAKE_CASE__       ,*SCREAMING_SNAKE_CASE__)






class 	A_		:
							_UpperCAmelCase   :  type
							_UpperCAmelCase   :  int
							_UpperCAmelCase   :  Dict[str, int]
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      int = 1):
								__lowerCamelCase    :    Tuple 	=			dim
								__lowerCamelCase    :    Any 	=			{k: dim * self.args_dim[k] for k in self.args_dim}
							def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      List[str]):
								if self.dim == 1:
									return self.distribution_class(*SCREAMING_SNAKE_CASE__)
								else:
									return Independent(self.distribution_class(*SCREAMING_SNAKE_CASE__)       ,1)
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.Tensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.Tensor] = None       ,):
								__lowerCamelCase    :    str 	=			self._base_distribution(SCREAMING_SNAKE_CASE__)
								if loc is None and scale is None:
									return distr
								else:
									return AffineTransformed(SCREAMING_SNAKE_CASE__       ,loc=SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__       ,event_dim=self.event_dim)
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return () if self.dim == 1 else (self.dim,)
							@property
							def    lowerCAmelCase					(		self		:      Tuple):
								return len(self.event_shape)
							@property
							def    lowerCAmelCase					(		self		:      Optional[int]):
								return 0.0
							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      int):
								return ParameterProjection(
								    in_features=SCREAMING_SNAKE_CASE__       ,args_dim=self.args_dim       ,domain_map=LambdaLayer(self.domain_map)       ,)
							def    lowerCAmelCase					(		self		:      Any       ,*SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								raise NotImplementedError()


							@staticmethod
							def    lowerCAmelCase					(		SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								return (x + torch.sqrt(torch.square(SCREAMING_SNAKE_CASE__) + 4.0)) / 2.0






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict[str, int]										=				{"df": 1, "loc": 1, "scale": 1}
							_UpperCAmelCase   :  type										=				StudentT
							@classmethod
							def    lowerCAmelCase					(		cls		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								__lowerCamelCase    :    Union[str, Any] 	=			cls.squareplus(SCREAMING_SNAKE_CASE__).clamp_min(torch.finfo(scale.dtype).eps)
								__lowerCamelCase    :    str 	=			2.0 + cls.squareplus(SCREAMING_SNAKE_CASE__)
								return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict[str, int]										=				{"loc": 1, "scale": 1}
							_UpperCAmelCase   :  type										=				Normal
							@classmethod
							def    lowerCAmelCase					(		cls		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								__lowerCamelCase    :    int 	=			cls.squareplus(SCREAMING_SNAKE_CASE__).clamp_min(torch.finfo(scale.dtype).eps)
								return loc.squeeze(-1), scale.squeeze(-1)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict[str, int]										=				{"total_count": 1, "logits": 1}
							_UpperCAmelCase   :  type										=				NegativeBinomial
							@classmethod
							def    lowerCAmelCase					(		cls		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor):
								__lowerCamelCase    :    Tuple 	=			cls.squareplus(SCREAMING_SNAKE_CASE__)
								return total_count.squeeze(-1), logits.squeeze(-1)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			distr_args
								if self.dim == 1:
									return self.distribution_class(total_count=SCREAMING_SNAKE_CASE__       ,logits=SCREAMING_SNAKE_CASE__)
								else:
									return Independent(self.distribution_class(total_count=SCREAMING_SNAKE_CASE__       ,logits=SCREAMING_SNAKE_CASE__)       ,1)


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.Tensor] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[torch.Tensor] = None):
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			distr_args

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

								return self._base_distribution((total_count, logits))







# 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


a		=[
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	__lowerCamelCase    :    int 	=			True
	while ask_again:
		__lowerCamelCase    :    Dict 	=			input(lowerCamelCase__      )
		try:
			if default is not None and len(lowerCamelCase__      ) == 0:
				return default
			return convert_value(lowerCamelCase__      ) if convert_value is not None else result
		except Exception:
			if error_message is not None:
				print(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=[]     ,			lowerCamelCase__=None     ,			lowerCamelCase__=0      )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			BulletMenu(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			menu.run(default_choice=lowerCamelCase__      )
	return convert_value(lowerCamelCase__      ) if convert_value is not None else result






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			int(lowerCamelCase__      )
	return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return DynamoBackend(DYNAMO_BACKENDS[value]      ).value






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	return {"yes": True, "no": False}[value.lower()]






class 	A_		(	argparse.RawDescriptionHelpFormatter    ):
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    int 	=			super()._format_usage(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			usage.replace('<command> [<args>] '       ,'')
								return usage




