






'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
     from ...onnx.config import PatchingSpec
     from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}






class        a_		(    __lowerCAmelCase       ):

  '''simple docstring'''





  lowercase  	=   "longformer"

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 2    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 0    ,	_SCREAMING_SNAKE_CASE = 2    ,	_SCREAMING_SNAKE_CASE = 30522    ,	_SCREAMING_SNAKE_CASE = 768    ,	_SCREAMING_SNAKE_CASE = 12    ,	_SCREAMING_SNAKE_CASE = 12    ,	_SCREAMING_SNAKE_CASE = 3072    ,	_SCREAMING_SNAKE_CASE = "gelu"    ,	_SCREAMING_SNAKE_CASE = 0.1    ,	_SCREAMING_SNAKE_CASE = 0.1    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 2    ,	_SCREAMING_SNAKE_CASE = 0.0_2    ,	_SCREAMING_SNAKE_CASE = 1e-12    ,	_SCREAMING_SNAKE_CASE = False    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Any:



        """simple docstring"""






        super().__init__(pad_token_id=lowerCamelCase__    ,	**lowerCamelCase__   )

        UpperCamelCase       						=      attention_window
        UpperCamelCase       						=      sep_token_id
        UpperCamelCase       						=      bos_token_id
        UpperCamelCase       						=      eos_token_id
        UpperCamelCase       						=      vocab_size
        UpperCamelCase       						=      hidden_size
        UpperCamelCase       						=      num_hidden_layers
        UpperCamelCase       						=      num_attention_heads
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      intermediate_size
        UpperCamelCase       						=      hidden_dropout_prob
        UpperCamelCase       						=      attention_probs_dropout_prob
        UpperCamelCase       						=      max_position_embeddings
        UpperCamelCase       						=      type_vocab_size
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      layer_norm_eps
        UpperCamelCase       						=      onnx_export






class        a_		(    __lowerCAmelCase       ):

  '''simple docstring'''





  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = "default"    ,	_SCREAMING_SNAKE_CASE = None   )						->   List[str]:



        """simple docstring"""






        super().__init__(lowerCamelCase__    ,	lowerCamelCase__    ,	lowerCamelCase__   )
        UpperCamelCase       						=      True

  @property
  def 						A__		(   self   )						->   Mapping[str, Mapping[int, str]]:



        """simple docstring"""






        if self.task == "multiple-choice":
              UpperCamelCase       						=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
              UpperCamelCase       						=      {0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""global_attention_mask""", dynamic_axis),
            ]   )

  @property
  def 						A__		(   self   )						->   Mapping[str, Mapping[int, str]]:



        """simple docstring"""






        UpperCamelCase       						=      super().outputs
        if self.task == "default":
              UpperCamelCase       						=      {0: '''batch'''}
        return outputs

  @property
  def 						A__		(   self   )						->   float:



        """simple docstring"""






        return 1e-4

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return max(super().default_onnx_opset    ,	14   )


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      super().generate_dummy_inputs(
            preprocessor=lowerCamelCase__    ,	batch_size=lowerCamelCase__    ,	seq_length=lowerCamelCase__    ,	is_pair=lowerCamelCase__    ,	framework=lowerCamelCase__   )
        import torch

        # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
        # makes the export fail randomly
        UpperCamelCase       						=      torch.zeros_like(inputs["""input_ids"""]   )
        # make every second token global
        UpperCamelCase       						=      1

        return inputs








'''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 platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available







def 					lowercase__				(   __UpperCamelCase=None					)->				Union[str, Any]:
      if subparsers is not None:
            UpperCamelCase       						=      subparsers.add_parser("""env"""					)
      else:
            UpperCamelCase       						=      argparse.ArgumentParser("""Accelerate env command"""					)

      parser.add_argument(
          """--config_file"""	,		default=__UpperCamelCase	,		help="""The config file to use for the default values in the launching script."""					)

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







def 					lowercase__				(   __UpperCamelCase					)->				List[str]:
      UpperCamelCase       						=      torch.__version__
      UpperCamelCase       						=      torch.cuda.is_available()
      UpperCamelCase       						=      is_xpu_available()
      UpperCamelCase       						=      is_npu_available()

      UpperCamelCase       						=      """Not found"""
      # Get the default from the config file.
      if args.config_file is not None or os.path.isfile(__UpperCamelCase					):
            UpperCamelCase       						=      load_config_from_file(args.config_file					).to_dict()

      UpperCamelCase       						=      {
          """`Accelerate` version""": version,
          """Platform""": platform.platform(),
          """Python version""": platform.python_version(),
          """Numpy version""": np.__version__,
          """PyTorch version (GPU?)""": F"{pt_version} ({pt_cuda_available})",
          """PyTorch XPU available""": str(__UpperCamelCase					),
          """PyTorch NPU available""": str(__UpperCamelCase					),
          """System RAM""": F"{psutil.virtual_memory().total / 1024 ** 3:.2f} GB",
      }
      if pt_cuda_available:
            UpperCamelCase       						=      torch.cuda.get_device_name()

      print("""\nCopy-and-paste the text below in your GitHub issue\n"""					)
      print("""\n""".join([F"- {prop}: {val}" for prop, val in info.items()]					)					)

      print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""					)
      UpperCamelCase       						=      (
          """\n""".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]					)
          if isinstance(__UpperCamelCase	,		__UpperCamelCase					)
          else F"\t{accelerate_config}"
      )
      print(__UpperCamelCase					)

      UpperCamelCase       						=      accelerate_config

      return info







def 					lowercase__				(   )->				int:
      UpperCamelCase       						=      env_command_parser()
      UpperCamelCase       						=      parser.parse_args()
      env_command(__UpperCamelCase					)
      return 0


if __name__ == "__main__":
     raise SystemExit(main())
