
'''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.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__UpperCAmelCase												=   'Update an existing config file with the latest defaults while maintaining the old configuration.'



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	List[Any]:
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		args.config_file
 if config_file is None and Path(__UpperCamelCase							).exists():
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		default_config_file
 elif not Path(__UpperCamelCase							).exists():
  raise ValueError(f"""The passed config file located at {config_file} doesn\'t exist."""							)
 SCREAMING_SNAKE_CASE  :  List[str]								=		load_config_from_file(__UpperCamelCase							)

 if config_file.endswith('.json'							):
  config.to_json_file(__UpperCamelCase							)
 else:
  config.to_yaml_file(__UpperCamelCase							)
 return config_file



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]   ,     snake_case_				:	Any							)       ->	List[Any]:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		parser.add_parser('update'   ,     parents=__UpperCamelCase   ,     help=__UpperCamelCase   ,     formatter_class=__UpperCamelCase							)
 parser.add_argument(
     '--config_file'   ,     default=__UpperCamelCase   ,     help=(
         'The path to the config file to update. 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\'.'
     )   ,     )

 parser.set_defaults(func=__UpperCamelCase							)
 return parser





def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]							)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  str								=		update_config(__UpperCamelCase							)
 print(f"""Sucessfully updated the configuration file at {config_file}."""							)






import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     copy.deepcopy(__UpperCamelCase			)
     a_     :  Dict				      =     sorted(__UpperCamelCase			)
     a_     :  Dict				      =     [bisect.bisect_right(__UpperCamelCase	,	__UpperCamelCase			) for y in x]
     return quantized



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase=0			):
     a_     :  str				      =     np.arange(start=3	,	stop=__UpperCamelCase	,	step=4			).tolist() if k > 0 else [1_0]
     a_     :  Any				      =     _quantize(__UpperCamelCase	,	__UpperCamelCase			)
     # count number of elements per group
     a_     :  Optional[int]				      =     np.unique(__UpperCamelCase	,	return_counts=__UpperCamelCase			)[1]
     a_     :  List[str]				      =     [0] + bins + [np.inf]
     logger.info("""Using {} as bins for aspect lengths quantization""".format(__UpperCamelCase			)			)
     logger.info("""Count of instances per bin: {}""".format(__UpperCamelCase			)			)
     return groups


class 	a__						(    lowerCAmelCase_     ):

       def __init__( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str     ):
            if not isinstance(lowerCamelCase_   ,				lowerCamelCase_     ):
                 raise ValueError(
                     """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase_     )     )
            a_     :  Optional[Any]				      =     sampler
            a_     :  List[Any]				      =     group_ids
            a_     :  Tuple				      =     batch_size

       def __iter__( self   :  Dict     ):
            a_     :  Tuple				      =     defaultdict(lowerCamelCase_     )
            a_     :  Any				      =     defaultdict(lowerCamelCase_     )

            a_     :  Any				      =     0
            for idx in self.sampler:
                 a_     :  str				      =     self.group_ids[idx]
                 buffer_per_group[group_id].append(lowerCamelCase_     )
                 samples_per_group[group_id].append(lowerCamelCase_     )
                 if len(buffer_per_group[group_id]     ) == self.batch_size:
                      yield buffer_per_group[group_id]  # TODO
                      num_batches += 1
                      del buffer_per_group[group_id]
                 assert len(buffer_per_group[group_id]     ) < self.batch_size

            # now we have run out of elements that satisfy
            # the group criteria, let's return the remaining
            # elements so that the size of the sampler is
            # deterministic
            a_     :  Optional[Any]				      =     len(self     )
            a_     :  Any				      =     expected_num_batches - num_batches
            if num_remaining > 0:
                 # for the remaining batches, group the batches by similar lengths
                 a_     :  str				      =     []
                 for group_id, idxs in sorted(buffer_per_group.items()   ,				key=lambda lowerCamelCase_     : x[0]     ):
                      batch_idx.extend(lowerCamelCase_     )
                      if len(lowerCamelCase_     ) >= self.batch_size:
                           yield batch_idx[: self.batch_size]
                           a_     :  Union[str, Any]				      =     batch_idx[self.batch_size :]
                           num_remaining -= 1
                 if len(lowerCamelCase_     ) > 0:
                      yield batch_idx
                      num_remaining -= 1
            assert num_remaining == 0

       def __len__( self   :  List[Any]     ):
            return (len(self.sampler     ) + self.batch_size - 1) // self.batch_size



