
import torch
from torch import nn




class 				lowerCamelCase__							(       nn.Module):




	'''simple docstring'''




	def __init__(      self	:List[Any]  ,     a	:Optional[Any]  ,     a	:Dict  ,     a	:List[str]  ,     a	:Any  ,     a	:Optional[Any]=1  ,     a	:str=False		)	->				Optional[int]:
								super().__init__()

								__UpperCamelCase			:     Union[str, Any]         =      n_token
								__UpperCamelCase			:     Optional[Any]         =      d_embed
								__UpperCamelCase			:     int         =      d_proj

								__UpperCamelCase			:     Union[str, Any]         =      cutoffs + [n_token]
								__UpperCamelCase			:     List[Any]         =      [0] + self.cutoffs
								__UpperCamelCase			:     int         =      div_val

								__UpperCamelCase			:     Dict         =      self.cutoffs[0]
								__UpperCamelCase			:     Optional[int]         =      len(self.cutoffs		) - 1
								__UpperCamelCase			:     str         =      self.shortlist_size + self.n_clusters

								if self.n_clusters > 0:
															__UpperCamelCase			:     List[str]         =      nn.Parameter(torch.zeros(self.n_clusters  ,     self.d_embed		)		)
															__UpperCamelCase			:     Optional[int]         =      nn.Parameter(torch.zeros(self.n_clusters		)		)

								__UpperCamelCase			:     int         =      nn.ModuleList()
								__UpperCamelCase			:     List[Any]         =      nn.ParameterList()

								if div_val == 1:
															for i in range(len(self.cutoffs		)		):
																						if d_proj != d_embed:
																													self.out_projs.append(nn.Parameter(torch.FloatTensor(a  ,     a		)		)		)
																						else:
																													self.out_projs.append(a		)

															self.out_layers.append(nn.Linear(a  ,     a		)		)
								else:
															for i in range(len(self.cutoffs		)		):
																						__UpperCamelCase     ,		__UpperCamelCase			:     str         =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						__UpperCamelCase			:     Union[str, Any]         =      d_embed // (div_val**i)

																						self.out_projs.append(nn.Parameter(torch.FloatTensor(a  ,     a		)		)		)

																						self.out_layers.append(nn.Linear(a  ,     r_idx - l_idx		)		)

								__UpperCamelCase			:     int         =      keep_order







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:str  ,     a	:Optional[int]  ,     a	:List[Any]		)	->				Tuple:
								if proj is None:
															__UpperCamelCase			:     Dict         =      nn.functional.linear(a  ,     a  ,     bias=a		)
								else:
															# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
															__UpperCamelCase			:     Any         =      nn.functional.linear(a  ,     proj.t().contiguous()		)
															__UpperCamelCase			:     Any         =      nn.functional.linear(a  ,     a  ,     bias=a		)
															# else:
															#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
															#     if bias is not None:
															#         logit = logit + bias

								return logit







	def      _lowerCamelCase       (      self	:List[Any]  ,     a	:Union[str, Any]  ,     a	:str=None  ,     a	:int=False		)	->				List[str]:

								if labels is not None:
															# Shift so that tokens < n predict n
															__UpperCamelCase			:     Optional[int]         =      hidden[..., :-1, :].contiguous()
															__UpperCamelCase			:     Tuple         =      labels[..., 1:].contiguous()
															__UpperCamelCase			:     Tuple         =      hidden.view(-1  ,     hidden.size(-1		)		)
															__UpperCamelCase			:     Optional[int]         =      labels.view(-1		)
															if hidden.size(0		) != labels.size(0		):
																						raise RuntimeError("Input and labels should have the same size in the batch dimension."		)
								else:
															__UpperCamelCase			:     List[Any]         =      hidden.view(-1  ,     hidden.size(-1		)		)

								if self.n_clusters == 0:
															__UpperCamelCase			:     str         =      self._compute_logit(a  ,     self.out_layers[0].weight  ,     self.out_layers[0].bias  ,     self.out_projs[0]		)
															if labels is not None:
																						__UpperCamelCase			:     Optional[int]         =      labels != -1_0_0
																						__UpperCamelCase			:     int         =      torch.zeros_like(a  ,     dtype=hidden.dtype  ,     device=hidden.device		)
																						__UpperCamelCase			:     Dict         =      (
																						    -nn.functional.log_softmax(a  ,     dim=-1		)[mask].gather(1  ,     labels[mask].unsqueeze(1		)		).squeeze(1		)
																						)
															else:
																						__UpperCamelCase			:     Union[str, Any]         =      nn.functional.log_softmax(a  ,     dim=-1		)
								else:
															# construct weights and biases
															__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      [], []
															for i in range(len(self.cutoffs		)		):
																						if self.div_val == 1:
																													__UpperCamelCase     ,		__UpperCamelCase			:     str         =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
																													__UpperCamelCase			:     str         =      self.out_layers[0].weight[l_idx:r_idx]
																													__UpperCamelCase			:     Any         =      self.out_layers[0].bias[l_idx:r_idx]
																						else:
																													__UpperCamelCase			:     Dict         =      self.out_layers[i].weight
																													__UpperCamelCase			:     Optional[Any]         =      self.out_layers[i].bias

																						if i == 0:
																													__UpperCamelCase			:     Union[str, Any]         =      torch.cat([weight_i, self.cluster_weight]  ,     dim=0		)
																													__UpperCamelCase			:     List[str]         =      torch.cat([bias_i, self.cluster_bias]  ,     dim=0		)

																						weights.append(a		)
																						biases.append(a		)

															__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      weights[0], biases[0], self.out_projs[0]

															__UpperCamelCase			:     Dict         =      self._compute_logit(a  ,     a  ,     a  ,     a		)
															__UpperCamelCase			:     Any         =      nn.functional.log_softmax(a  ,     dim=1		)

															if labels is None:
																						__UpperCamelCase			:     List[str]         =      hidden.new_empty((head_logit.size(0		), self.n_token)		)
															else:
																						__UpperCamelCase			:     Dict         =      torch.zeros_like(a  ,     dtype=hidden.dtype  ,     device=hidden.device		)

															__UpperCamelCase			:     str         =      0
															__UpperCamelCase			:     Tuple         =      [0] + self.cutoffs
															for i in range(len(a		) - 1		):
																						__UpperCamelCase     ,		__UpperCamelCase			:     str         =      cutoff_values[i], cutoff_values[i + 1]

																						if labels is not None:
																													__UpperCamelCase			:     Any         =      (labels >= l_idx) & (labels < r_idx)
																													__UpperCamelCase			:     int         =      mask_i.nonzero().squeeze()

																													if indices_i.numel() == 0:
																																				continue

																													__UpperCamelCase			:     Any         =      labels.index_select(0  ,     a		) - l_idx
																													__UpperCamelCase			:     int         =      head_logprob.index_select(0  ,     a		)
																													__UpperCamelCase			:     str         =      hidden.index_select(0  ,     a		)
																						else:
																													__UpperCamelCase			:     Tuple         =      hidden

																						if i == 0:
																													if labels is not None:
																																				__UpperCamelCase			:     Any         =      head_logprob_i.gather(1  ,     target_i[:, None]		).squeeze(1		)
																													else:
																																				__UpperCamelCase			:     Any         =      head_logprob[:, : self.cutoffs[0]]
																						else:
																													__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      weights[i], biases[i], self.out_projs[i]

																													__UpperCamelCase			:     List[Any]         =      self._compute_logit(a  ,     a  ,     a  ,     a		)
																													__UpperCamelCase			:     Dict         =      nn.functional.log_softmax(a  ,     dim=1		)
																													__UpperCamelCase			:     Union[str, Any]         =      self.cutoffs[0] + i - 1  # No probability for the head cluster
																													if labels is not None:
																																				__UpperCamelCase			:     Dict         =      head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																																				    1  ,     target_i[:, None]		).squeeze(1		)
																													else:
																																				__UpperCamelCase			:     int         =      head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																																				__UpperCamelCase			:     List[Any]         =      logprob_i

																						if labels is not None:
																													if (hasattr(self  ,     "keep_order"		) and self.keep_order) or keep_order:
																																				out.index_copy_(0  ,     a  ,     -logprob_i		)
																													else:
																																				out[offset : offset + logprob_i.size(0		)].copy_(-logprob_i		)
																													offset += logprob_i.size(0		)

								return out







	def      _lowerCamelCase       (      self	:int  ,     a	:Optional[Any]		)	->				Any:
								if self.n_clusters == 0:
															__UpperCamelCase			:     Optional[int]         =      self._compute_logit(a  ,     self.out_layers[0].weight  ,     self.out_layers[0].bias  ,     self.out_projs[0]		)
															return nn.functional.log_softmax(a  ,     dim=-1		)
								else:
															# construct weights and biases
															__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      [], []
															for i in range(len(self.cutoffs		)		):
																						if self.div_val == 1:
																													__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
																													__UpperCamelCase			:     int         =      self.out_layers[0].weight[l_idx:r_idx]
																													__UpperCamelCase			:     str         =      self.out_layers[0].bias[l_idx:r_idx]
																						else:
																													__UpperCamelCase			:     Tuple         =      self.out_layers[i].weight
																													__UpperCamelCase			:     Optional[int]         =      self.out_layers[i].bias

																						if i == 0:
																													__UpperCamelCase			:     List[str]         =      torch.cat([weight_i, self.cluster_weight]  ,     dim=0		)
																													__UpperCamelCase			:     Union[str, Any]         =      torch.cat([bias_i, self.cluster_bias]  ,     dim=0		)

																						weights.append(a		)
																						biases.append(a		)

															__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      weights[0], biases[0], self.out_projs[0]
															__UpperCamelCase			:     List[str]         =      self._compute_logit(a  ,     a  ,     a  ,     a		)

															__UpperCamelCase			:     Tuple         =      hidden.new_empty((head_logit.size(0		), self.n_token)		)
															__UpperCamelCase			:     Any         =      nn.functional.log_softmax(a  ,     dim=1		)

															__UpperCamelCase			:     Optional[Any]         =      [0] + self.cutoffs
															for i in range(len(a		) - 1		):
																						__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      cutoff_values[i], cutoff_values[i + 1]

																						if i == 0:
																													__UpperCamelCase			:     Dict         =      head_logprob[:, : self.cutoffs[0]]
																						else:
																													__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      weights[i], biases[i], self.out_projs[i]

																													__UpperCamelCase			:     Dict         =      self._compute_logit(a  ,     a  ,     a  ,     a		)
																													__UpperCamelCase			:     Union[str, Any]         =      nn.functional.log_softmax(a  ,     dim=1		)

																													__UpperCamelCase			:     List[str]         =      head_logprob[:, -i] + tail_logprob_i
																													__UpperCamelCase			:     List[Any]         =      logprob_i

															return out

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)