





"""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


a		:				List[Any]  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''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  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         ="""longformer"""






							def __init__(     self   ,      lowerCAmelCase__ = 512   ,      lowerCAmelCase__ = 2   ,      lowerCAmelCase__ = 1   ,      lowerCAmelCase__ = 0   ,      lowerCAmelCase__ = 2   ,      lowerCAmelCase__ = 3_0522   ,      lowerCAmelCase__ = 768   ,      lowerCAmelCase__ = 12   ,      lowerCAmelCase__ = 12   ,      lowerCAmelCase__ = 3072   ,      lowerCAmelCase__ = "gelu"   ,      lowerCAmelCase__ = 0.1   ,      lowerCAmelCase__ = 0.1   ,      lowerCAmelCase__ = 512   ,      lowerCAmelCase__ = 2   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 1E-12   ,      lowerCAmelCase__ = False   ,      **lowerCAmelCase__   ,      )			->				int:
								super().__init__(pad_token_id=lowerCAmelCase__   ,      **lowerCAmelCase__   )

								a   :	Optional[Any]			=       attention_window
								a   :	Dict			=       sep_token_id
								a   :	int			=       bos_token_id
								a   :	Union[str, Any]			=       eos_token_id
								a   :	Tuple			=       vocab_size
								a   :	Optional[Any]			=       hidden_size
								a   :	Union[str, Any]			=       num_hidden_layers
								a   :	int			=       num_attention_heads
								a   :	List[Any]			=       hidden_act
								a   :	Tuple			=       intermediate_size
								a   :	int			=       hidden_dropout_prob
								a   :	Tuple			=       attention_probs_dropout_prob
								a   :	Any			=       max_position_embeddings
								a   :	Union[str, Any]			=       type_vocab_size
								a   :	Dict			=       initializer_range
								a   :	str			=       layer_norm_eps
								a   :	Tuple			=       onnx_export


class  __UpperCamelCase       (		a__		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = "default"   ,      lowerCAmelCase__ = None   )			->				Dict:
								super().__init__(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	Dict			=       True






							@property
							def 					__a     (     self   )			->				Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
									a   :	Any			=       {0: "batch", 1: "choice", 2: "sequence"}
								else:
									a   :	Optional[Any]			=       {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]]:
								a   :	Tuple			=       super().outputs
								if self.task == "default":
									a   :	Any			=       {0: "batch"}
								return outputs






							@property
							def 					__a     (     self   )			->				float:
								return 1E-4






							@property
							def 					__a     (     self   )			->				int:
								# needs to be >= 14 to support tril operator
								return max(super().default_onnx_opset   ,      14   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = -1   ,      lowerCAmelCase__ = -1   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      )			->				Mapping[str, Any]:
								a   :	Tuple			=       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
								a   :	Optional[int]			=       torch.zeros_like(inputs["input_ids"]   )
								# make every second token global
								a   :	Optional[Any]			=       1

								return inputs









"""simple docstring"""




# 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
import os

from accelerate.test_utils import execute_subprocess_async






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	Dict			=       subparsers.add_parser("test"      )
	else:
		a   :	Tuple			=       argparse.ArgumentParser("Accelerate test command"      )

	parser.add_argument(
	    "--config_file"					, default=_lowercase					, help=(
	        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
	        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
	        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
	        "with 'huggingface'."
	    )					, )

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       os.path.sep.join(__file__.split(os.path.sep      )[:-2] + ["test_utils", "scripts", "test_script.py"]      )

	if args.config_file is None:
		a   :	int			=       script_name
	else:
		a   :	int			=       F"""--config_file={args.config_file} {script_name}"""

	a   :	Optional[int]			=       ["accelerate-launch"] + test_args.split()
	a   :	Optional[int]			=       execute_subprocess_async(_lowercase					, env=os.environ.copy()      )
	if result.returncode == 0:
		print("Test is a success! You are ready for your distributed training!"      )






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	Any			=       test_command_parser()
	a   :	Union[str, Any]			=       parser.parse_args()
	test_command(_lowercase      )


if __name__ == "__main__":
		main()


