"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	Tuple      ) ->      str:



       '''simple docstring'''

       if collection == []:
              return []

       # get some information about the collection
       __lowerCamelCase					:		Dict		   =     len(_lowerCamelCase      )
       __lowerCamelCase					:		int		   =     max(_lowerCamelCase      )
       __lowerCamelCase					:		Tuple		   =     min(_lowerCamelCase      )

       # create the counting array
       __lowerCamelCase					:		Optional[int]		   =     coll_max + 1 - coll_min
       __lowerCamelCase					:		Dict		   =     [0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1						,   _lowerCamelCase      ):
              __lowerCamelCase					:		List[Any]		   =     counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       __lowerCamelCase					:		Dict		   =     [0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0						,   _lowerCamelCase      )      ):
              __lowerCamelCase					:		Dict		   =     collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def    lowercase_   (   _lowerCamelCase:	List[Any]      ) ->      int:



       '''simple docstring'''

       return "".join([chr(_lowerCamelCase      ) for i in counting_sort([ord(_lowerCamelCase      ) for c in string]      )]      )


if __name__ == "__main__":
    # Test string sort
    assert counting_sort_string('''thisisthestring''') == "eghhiiinrsssttt"

    __A  =				input('''Enter numbers separated by a comma:\n''').strip()
    __A  =				[int(item) for item in user_input.split(''',''')]
    print(counting_sort(unsorted))
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)