





'''simple docstring'''

from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


a        =						logging.get_logger(__name__)


a        =						OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

a        =						OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

a        =						OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

a        =						OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

a        =						OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

a        =						OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

a        =						OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

a        =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
a        =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
a        =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
a        =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
a        =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
a        =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : int	      =	FLAX_MODEL_MAPPING





a        =						auto_class_update(FlaxAutoModel)







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Dict	      =	FLAX_MODEL_FOR_PRETRAINING_MAPPING





a        =						auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Any	      =	FLAX_MODEL_FOR_CAUSAL_LM_MAPPING





a        =						auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Tuple	      =	FLAX_MODEL_FOR_MASKED_LM_MAPPING





a        =						auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Any	      =	FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





a        =						auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Any	      =	FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





a        =						auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : int	      =	FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING





a        =						auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : List[str]	      =	FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING





a        =						auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : List[Any]	      =	FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING





a        =						auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : int	      =	FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING





a        =						auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Union[str, Any]	      =	FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING





a        =						auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Union[str, Any]	      =	FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING





a        =						auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")







class __a (   _BaseAutoModelClass			):
   __UpperCamelCase   : Union[str, Any]	      =	FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING





a        =						auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)












'''simple docstring'''

from __future__ import annotations

import bisect

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   if hi < 0:
      __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )

   while lo < hi:
      __SCREAMING_SNAKE_CASE      							=       lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         __SCREAMING_SNAKE_CASE      							=       mid + 1
      else:
         __SCREAMING_SNAKE_CASE      							=       mid

   return lo

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_left(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0							,	__UpperCAmelCase = -1      )						->					None:

   '''simple docstring'''







   sorted_collection.insert(bisect_right(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      ) - 1

   while left <= right:
      __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2
      __SCREAMING_SNAKE_CASE      							=       sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         __SCREAMING_SNAKE_CASE      							=       midpoint - 1
      else:
         __SCREAMING_SNAKE_CASE      							=       midpoint + 1
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       bisect.bisect_left(__UpperCAmelCase							,	__UpperCAmelCase      )
   if index != len(__UpperCAmelCase      ) and sorted_collection[index] == item:
      return index
   return None

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					int | None:

   '''simple docstring'''







   if right < left:
      return None

   __SCREAMING_SNAKE_CASE      							=       left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint - 1      )
   else:
      return binary_search_by_recursion(__UpperCAmelCase							,	__UpperCAmelCase							,	midpoint + 1							,	__UpperCAmelCase      )


if __name__ == "__main__":
    a        =						input("Enter numbers separated by comma:\n").strip()
    a        =						sorted(int(item) for item in user_input.split(","))
    a        =						int(input("Enter a single number to be found in the list:\n"))
    a        =						binary_search(collection, target)
    if result is None:
        print(F'''{target} was not found in {collection}.''')
    else:
        print(F'''{target} was found at position {result} in {collection}.''')





