"""simple docstring"""
import torch
from torch import nn




class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=1    ,			lowercase=False)	->			List[Any]:





        '''simple docstring'''





        super().__init__()

        a__:     Any										=	n_token
        a__:     Optional[int]										=	d_embed
        a__:     Any										=	d_proj

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

        a__:     str										=	self.cutoffs[0]
        a__:     str										=	len(self.cutoffs) - 1
        a__:     Tuple										=	self.shortlist_size + self.n_clusters

        if self.n_clusters > 0:
          a__:     Union[str, Any]										=	nn.Parameter(torch.zeros(self.n_clusters    ,			self.d_embed))
          a__:     List[str]										=	nn.Parameter(torch.zeros(self.n_clusters))

        a__:     List[str]										=	nn.ModuleList()
        a__:     Optional[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(lowercase__    ,			lowercase__)))
            else:
              self.out_projs.append(lowercase__)

          self.out_layers.append(nn.Linear(lowercase__    ,			lowercase__))
        else:
          for i in range(len(self.cutoffs)):
            a__							,    a__:     int										=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
            a__:     Tuple										=	d_embed // (div_val**i)

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

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

        a__:     str										=	keep_order



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			List[str]:





        '''simple docstring'''





        if proj is None:
          a__:     Optional[Any]										=	nn.functional.linear(lowercase__    ,			lowercase__    ,			bias=lowercase__)
        else:
          # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
          a__:     Dict										=	nn.functional.linear(lowercase__    ,			proj.t().contiguous())
          a__:     Optional[int]										=	nn.functional.linear(lowercase__    ,			lowercase__    ,			bias=lowercase__)
          # 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    ,			lowercase    ,			lowercase=None    ,			lowercase=False)	->			Tuple:





        '''simple docstring'''





        if labels is not None:
          # Shift so that tokens < n predict n
          a__:     int										=	hidden[..., :-1, :].contiguous()
          a__:     Union[str, Any]										=	labels[..., 1:].contiguous()
          a__:     Optional[Any]										=	hidden.view(-1    ,			hidden.size(-1))
          a__:     List[Any]										=	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:
          a__:     Optional[int]										=	hidden.view(-1    ,			hidden.size(-1))

        if self.n_clusters == 0:
          a__:     Optional[int]										=	self._compute_logit(lowercase__    ,			self.out_layers[0].weight    ,			self.out_layers[0].bias    ,			self.out_projs[0])
          if labels is not None:
            a__:     List[str]										=	labels != -1_00
            a__:     Any										=	torch.zeros_like(lowercase__    ,			dtype=hidden.dtype    ,			device=hidden.device)
            a__:     str										=	(
                -nn.functional.log_softmax(lowercase__    ,			dim=-1)[mask].gather(1    ,			labels[mask].unsqueeze(1)).squeeze(1)
            )
          else:
            a__:     Union[str, Any]										=	nn.functional.log_softmax(lowercase__    ,			dim=-1)
        else:
          # construct weights and biases
          a__							,    a__:     List[str]										=	[], []
          for i in range(len(self.cutoffs)):
            if self.div_val == 1:
              a__							,    a__:     Dict										=	self.cutoff_ends[i], self.cutoff_ends[i + 1]
              a__:     Optional[Any]										=	self.out_layers[0].weight[l_idx:r_idx]
              a__:     Any										=	self.out_layers[0].bias[l_idx:r_idx]
            else:
              a__:     Optional[int]										=	self.out_layers[i].weight
              a__:     Optional[int]										=	self.out_layers[i].bias

            if i == 0:
              a__:     Tuple										=	torch.cat([weight_i, self.cluster_weight]    ,			dim=0)
              a__:     Optional[int]										=	torch.cat([bias_i, self.cluster_bias]    ,			dim=0)

            weights.append(lowercase__)
            biases.append(lowercase__)

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

          a__:     int										=	self._compute_logit(lowercase__    ,			lowercase__    ,			lowercase__    ,			lowercase__)
          a__:     Union[str, Any]										=	nn.functional.log_softmax(lowercase__    ,			dim=1)

          if labels is None:
            a__:     Dict										=	hidden.new_empty((head_logit.size(0), self.n_token))
          else:
            a__:     Dict										=	torch.zeros_like(lowercase__    ,			dtype=hidden.dtype    ,			device=hidden.device)

          a__:     str										=	0
          a__:     str										=	[0] + self.cutoffs
          for i in range(len(lowercase__) - 1):
            a__							,    a__:     int										=	cutoff_values[i], cutoff_values[i + 1]

            if labels is not None:
              a__:     List[Any]										=	(labels >= l_idx) & (labels < r_idx)
              a__:     Any										=	mask_i.nonzero().squeeze()

              if indices_i.numel() == 0:
                continue

              a__:     List[str]										=	labels.index_select(0    ,			lowercase__) - l_idx
              a__:     str										=	head_logprob.index_select(0    ,			lowercase__)
              a__:     int										=	hidden.index_select(0    ,			lowercase__)
            else:
              a__:     Union[str, Any]										=	hidden

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

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

        return out



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





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

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

            weights.append(lowercase__)
            biases.append(lowercase__)

          a__							,    a__							,    a__:     Tuple										=	weights[0], biases[0], self.out_projs[0]
          a__:     int										=	self._compute_logit(lowercase__    ,			lowercase__    ,			lowercase__    ,			lowercase__)

          a__:     Union[str, Any]										=	hidden.new_empty((head_logit.size(0), self.n_token))
          a__:     List[Any]										=	nn.functional.log_softmax(lowercase__    ,			dim=1)

          a__:     List[Any]										=	[0] + self.cutoffs
          for i in range(len(lowercase__) - 1):
            a__							,    a__:     Union[str, Any]										=	cutoff_values[i], cutoff_values[i + 1]

            if i == 0:
              a__:     Optional[Any]										=	head_logprob[:, : self.cutoffs[0]]
            else:
              a__							,    a__							,    a__:     str										=	weights[i], biases[i], self.out_projs[i]

              a__:     Union[str, Any]										=	self._compute_logit(lowercase__    ,			lowercase__    ,			lowercase__    ,			lowercase__)
              a__:     int										=	nn.functional.log_softmax(lowercase__    ,			dim=1)

              a__:     Optional[int]										=	head_logprob[:, -i] + tail_logprob_i
              a__:     Any										=	logprob_i

          return out





'''simple docstring'''






# Copyright 2022 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
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__											=	'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''


def 		_A     (    A__=None      ):






 """simple docstring"""



 if subparsers is not None:
  __lowercase							  = subparsers.add_parser('''tpu-config'''     ,				description=_description      )
 else:
  __lowercase							  = argparse.ArgumentParser('''Accelerate tpu-config command'''     ,				description=_description      )
 # Core arguments
 __lowercase							  = parser.add_argument_group(
     '''Config Arguments'''     ,				'''Arguments that can be configured through `accelerate config`.'''      )
 config_args.add_argument(
     '''--config_file'''     ,				type=A__     ,				default=A__     ,				help='''Path to the config file to use for accelerate.'''     ,				)
 config_args.add_argument(
     '''--tpu_name'''     ,				default=A__     ,				help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''     ,				)
 config_args.add_argument(
     '''--tpu_zone'''     ,				default=A__     ,				help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''     ,				)
 __lowercase							  = parser.add_argument_group('''TPU Arguments'''     ,				'''Arguments for options ran inside the TPU.'''      )
 pod_args.add_argument(
     '''--use_alpha'''     ,				action='''store_true'''     ,				help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''     ,				)
 pod_args.add_argument(
     '''--command_file'''     ,				default=A__     ,				help='''The path to the file containing the commands to run on the pod on startup.'''     ,				)
 pod_args.add_argument(
     '''--command'''     ,				action='''append'''     ,				nargs='''+'''     ,				help='''A command to run on the pod. Can be passed multiple times.'''     ,				)
 pod_args.add_argument(
     '''--install_accelerate'''     ,				action='''store_true'''     ,				help='''Whether to install accelerate on the pod. Defaults to False.'''     ,				)
 pod_args.add_argument(
     '''--accelerate_version'''     ,				default='''latest'''     ,				help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''     ,				)
 pod_args.add_argument(
     '''--debug'''     ,				action='''store_true'''     ,				help='''If set, will print the command that would be run instead of running it.'''      )

 if subparsers is not None:
  parser.set_defaults(func=A__      )
 return parser


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = None

 # Get the default from the config file if it exists.
 if args.config_file is not None or os.path.isfile(A__      ):
  __lowercase							  = load_config_from_file(args.config_file      )
  if not args.command_file and defaults.command_file is not None and not args.command:
   __lowercase							  = defaults.command_file
  if not args.command and defaults.commands is not None:
   __lowercase							  = defaults.commands
  if not args.tpu_name:
   __lowercase							  = defaults.tpu_name
  if not args.tpu_zone:
   __lowercase							  = defaults.tpu_zone
 if args.accelerate_version == "dev":
  __lowercase							  = '''git+https://github.com/huggingface/accelerate.git'''
 elif args.accelerate_version == "latest":
  __lowercase							  = '''accelerate -U'''
 elif isinstance(parse(args.accelerate_version      )     ,				A__      ):
  __lowercase							  = F"accelerate=={args.accelerate_version}"

 if not args.command_file and not args.command:
  raise ValueError('''You must specify either a command file or a command to run on the pod.'''      )

 if args.command_file:
  with open(args.command_file     ,				'''r'''      ) as f:
   __lowercase							  = [f.read().splitlines()]

    # To turn list of lists into list of strings
 if isinstance(args.command[0]     ,				A__      ):
  __lowercase							  = [line for cmd in args.command for line in cmd]
 # Default to the shared folder and install accelerate
 __lowercase							  = ['''cd /usr/share''']
 if args.install_accelerate:
  new_cmd += [F"pip install {args.accelerate_version}"]
 new_cmd += args.command
 __lowercase							  = '''; '''.join(A__      )

 # Then send it to gcloud
 # Eventually try to use google-api-core to do this instead of subprocess
 __lowercase							  = ['''gcloud''']
 if args.use_alpha:
  cmd += ["alpha"]
 cmd += [
     "compute",
     "tpus",
     "tpu-vm",
     "ssh",
     args.tpu_name,
     "--zone",
     args.tpu_zone,
     "--command",
     args.command,
     "--worker",
     "all",
 ]
 if args.debug:
  print(F"Running {' '.join(A__      )}"      )
  return
 subprocess.run(A__      )
 print('''Successfully setup pod.'''      )






def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = tpu_command_parser()
 __lowercase							  = parser.parse_args()

 tpu_command_launcher(A__      )

