


'''simple docstring'''



import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Optional[Any]        =     os.getenv("""SM_HP_MP_PARAMETERS""" ,      """{}"""					)
			try:
						# Parse it and check the field "partitions" is included, it is required for model parallel.
						UpperCAmelCase__  :					Tuple        =     json.loads(_lowercase					)
						if "partitions" not in smp_options:
									return False
			except json.JSONDecodeError:
						return False

			# Get the sagemaker specific framework parameters from mpi_options variable.
			UpperCAmelCase__  :					int        =     os.getenv("""SM_FRAMEWORK_PARAMS""" ,      """{}"""					)
			try:
						# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
						UpperCAmelCase__  :					Optional[int]        =     json.loads(_lowercase					)
						if not mpi_options.get("""sagemaker_mpi_enabled""" ,      _lowercase					):
									return False
			except json.JSONDecodeError:
						return False
			# Lastly, check if the `smdistributed` module is present.
			return importlib.util.find_spec("""smdistributed"""					) is not None


if is_sagemaker_model_parallel_available():
		import smdistributed.modelparallel.torch as smp

		smp.init()


@dataclass
class 		_snake_case					(     a__	):
					lowerCAmelCase      :Tuple   =					field(
					    default=''''''       ,   metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}       ,   )




					def 				snake_case__						(      self):
								super().__post_init__()
								warnings.warn(
								    """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
								    """`TrainingArguments` instead."""							,							__A							,							)




					@cached_property
					def 				snake_case__						(      self):
								logger.info("""PyTorch: setting up devices""")
								if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
											logger.warning(
											    """torch.distributed process group is initialized, but local_rank == -1. """
											    """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""")
								if self.no_cuda:
											UpperCAmelCase__  :					Dict        =     torch.device("""cpu""")
											UpperCAmelCase__  :					Union[str, Any]        =     0
								elif is_sagemaker_model_parallel_available():
											UpperCAmelCase__  :					List[str]        =     smp.local_rank()
											UpperCAmelCase__  :					List[str]        =     torch.device("""cuda"""							,							__A)
											UpperCAmelCase__  :					Dict        =     1
								elif is_sagemaker_dp_enabled():
											import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

											torch.distributed.init_process_group(backend="""smddp"""							,							timeout=self.ddp_timeout_delta)
											UpperCAmelCase__  :					List[Any]        =     int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""))
											UpperCAmelCase__  :					Union[str, Any]        =     torch.device("""cuda"""							,							self.local_rank)
											UpperCAmelCase__  :					str        =     1
								elif self.local_rank == -1:
											# if n_gpu is > 1 we'll use nn.DataParallel.
											# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
											# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
											# trigger an error that a device index is missing. Index 0 takes into account the
											# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
											# will use the first GPU in that env, i.e. GPU#1
											UpperCAmelCase__  :					str        =     torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""")
											# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
											# the default value.
											UpperCAmelCase__  :					int        =     torch.cuda.device_count()
								else:
											# Here, we'll use torch.distributed.
											# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
											if not torch.distributed.is_initialized():
														torch.distributed.init_process_group(backend="""nccl"""							,							timeout=self.ddp_timeout_delta)
											UpperCAmelCase__  :					List[str]        =     torch.device("""cuda"""							,							self.local_rank)
											UpperCAmelCase__  :					Optional[Any]        =     1

								if device.type == "cuda":
											torch.cuda.set_device(__A)

								return device




					@property
					def 				snake_case__						(      self):
								if is_sagemaker_model_parallel_available():
											return smp.dp_size()

								return super().world_size




					@property
					def 				snake_case__						(      self):
								return not is_sagemaker_model_parallel_available()




					@property
					def 				snake_case__						(      self):
								return False



'''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 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Dict        =     1.0 if scale is None else scale
								UpperCAmelCase__  :					Dict        =     0.0 if loc is None else loc

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




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




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




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


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					int        =     args_dim
								UpperCAmelCase__  :					Optional[int]        =     nn.ModuleList([nn.Linear(_lowerCamelCase							,							_lowerCamelCase) for dim in args_dim.values()])
								UpperCAmelCase__  :					Optional[Any]        =     domain_map




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     [proj(_lowerCamelCase) for proj in self.proj]

								return self.domain_map(*_lowerCamelCase)


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					Optional[int]        =     function




					def 				snake_case__						(      self							,							_lowerCamelCase							,							*_lowerCamelCase):
								return self.function(_lowerCamelCase							,							*_lowerCamelCase)


class 		_snake_case					:
					lowerCAmelCase      :type
					lowerCAmelCase      :int
					lowerCAmelCase      :Dict[str, int]




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




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.dim == 1:
											return self.distribution_class(*_lowerCamelCase)
								else:
											return Independent(self.distribution_class(*_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					Dict        =     self._base_distribution(_lowerCamelCase)
								if loc is None and scale is None:
											return distr
								else:
											return AffineTransformed(_lowerCamelCase							,							loc=_lowerCamelCase							,							scale=_lowerCamelCase							,							event_dim=self.event_dim)




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




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




					@property
					def 				snake_case__						(      self):
								return 0.0




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return ParameterProjection(
								    in_features=_lowerCamelCase							,							args_dim=self.args_dim							,							domain_map=LambdaLayer(self.domain_map)							,							)




					def 				snake_case__						(      self							,							*_lowerCamelCase):
								raise NotImplementedError()




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return (x + torch.sqrt(torch.square(_lowerCamelCase) + 4.0)) / 2.0


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"df": 1, "loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					StudentT




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								UpperCAmelCase__  :					Any        =     2.0 + cls.squareplus(_lowerCamelCase)
								return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					Normal




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								return loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"total_count": 1, "logits": 1}
					lowerCAmelCase      :type   =					NegativeBinomial




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase)
								return total_count.squeeze(-1), logits.squeeze(-1)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     distr_args
								if self.dim == 1:
											return self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)
								else:
											return Independent(self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     distr_args

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

								return self._base_distribution((total_count, logits))