



"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {"""vocab_file""": """vocab.txt"""}

SCREAMING_SNAKE_CASE_				= {
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

SCREAMING_SNAKE_CASE_				= {
    """YituTech/conv-bert-base""": 5_12,
    """YituTech/conv-bert-medium-small""": 5_12,
    """YituTech/conv-bert-small""": 5_12,
}


SCREAMING_SNAKE_CASE_				= {
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					VOCAB_FILES_NAMES
							__lowerCAmelCase    					=					PRETRAINED_VOCAB_FILES_MAP
							__lowerCAmelCase    					=					PRETRAINED_INIT_CONFIGURATION
							__lowerCAmelCase    					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCAmelCase    					=					ConvBertTokenizer






							def __init__(   self			,     a_=None			,     a_=None			,     a_=True			,     a_="[UNK]"			,     a_="[SEP]"			,     a_="[PAD]"			,     a_="[CLS]"			,     a_="[MASK]"			,     a_=True			,     a_=None			,     **a_			,     ):
									super().__init__(
									    a_			,     tokenizer_file=a_			,     do_lower_case=a_			,     unk_token=a_			,     sep_token=a_			,     pad_token=a_			,     cls_token=a_			,     mask_token=a_			,     tokenize_chinese_chars=a_			,     strip_accents=a_			,     **a_			,     )

									a_     :      Any 						=     json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
									if (
									    normalizer_state.get("lowercase"			,     a_      ) != do_lower_case
									    or normalizer_state.get("strip_accents"			,     a_      ) != strip_accents
									    or normalizer_state.get("handle_chinese_chars"			,     a_      ) != tokenize_chinese_chars
									):
											a_     :      List[Any] 						=     getattr(a_			,     normalizer_state.pop("type"      )      )
											a_     :      Optional[Any] 						=     do_lower_case
											a_     :      Any 						=     strip_accents
											a_     :      Dict 						=     tokenize_chinese_chars
											a_     :      Union[str, Any] 						=     normalizer_class(**a_      )

									a_     :      Union[str, Any] 						=     do_lower_case






							def snake_case_  (   self			,     a_			,     a_=None      ):
									a_     :      int 						=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

									return output






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									a_     :      Tuple 						=     [self.sep_token_id]
									a_     :      Optional[int] 						=     [self.cls_token_id]
									if token_ids_a is None:
											return len(cls + token_ids_a + sep      ) * [0]
									return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									a_     :      Any 						=     self._tokenizer.model.save(a_			,     name=a_      )
									return tuple(a_      )




"""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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE_				= """Create a default config file for Accelerate with only a few flags set."""



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__="no", SCREAMING_SNAKE_CASE__ = default_json_config_file, SCREAMING_SNAKE_CASE__ = False	)			->      Tuple:
		a_     :      Union[str, Any] 						=     Path(SCREAMING_SNAKE_CASE__	)
		path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__, exist_ok=SCREAMING_SNAKE_CASE__	)
		if path.exists():
				print(
				    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""	)
				return False
		a_     :      Optional[int] 						=     mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""	)
		a_     :      str 						=     {
		    "compute_environment": "LOCAL_MACHINE",
		    "mixed_precision": mixed_precision,
		}
		if torch.cuda.is_available():
				a_     :      int 						=     torch.cuda.device_count()
				a_     :      Optional[Any] 						=     num_gpus
				a_     :      int 						=     False
				if num_gpus > 1:
						a_     :      Any 						=     "MULTI_GPU"
				else:
						a_     :      int 						=     "NO"
		elif is_xpu_available() and use_xpu:
				a_     :      int 						=     torch.xpu.device_count()
				a_     :      str 						=     num_xpus
				a_     :      Tuple 						=     False
				if num_xpus > 1:
						a_     :      int 						=     "MULTI_XPU"
				else:
						a_     :      List[str] 						=     "NO"
		elif is_npu_available():
				a_     :      List[Any] 						=     torch.npu.device_count()
				a_     :      int 						=     num_npus
				a_     :      List[Any] 						=     False
				if num_npus > 1:
						a_     :      str 						=     "MULTI_NPU"
				else:
						a_     :      Union[str, Any] 						=     "NO"
		else:
				a_     :      Optional[Any] 						=     0
				a_     :      Optional[Any] 						=     True
				a_     :      Tuple 						=     1
				a_     :      Optional[int] 						=     "NO"
		a_     :      str 						=     ClusterConfig(**SCREAMING_SNAKE_CASE__	)
		config.to_json_file(SCREAMING_SNAKE_CASE__	)
		return path



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[Any]:
		a_     :      Dict 						=     parser.add_parser("default", parents=SCREAMING_SNAKE_CASE__, help=SCREAMING_SNAKE_CASE__, formatter_class=SCREAMING_SNAKE_CASE__	)
		parser.add_argument(
		    "--config_file", default=SCREAMING_SNAKE_CASE__, 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'."
		    ), dest="save_location", )

		parser.add_argument(
		    "--mixed_precision", choices=["no", "fp16", "bf16"], type=SCREAMING_SNAKE_CASE__, help="Whether or not to use mixed precision training. "
		    "Choose between FP16 and BF16 (bfloat16) training. "
		    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.", default="no", )
		parser.set_defaults(func=SCREAMING_SNAKE_CASE__	)
		return parser



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Tuple:
		a_     :      Any 						=     write_basic_config(args.mixed_precision, args.save_location	)
		if config_file:
				print(F"""accelerate configuration saved at {config_file}"""	)