






'''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


UpperCamelCase     :   Union[str, Any]	      =		logging.get_logger(__name__)


UpperCamelCase     :   int	      =		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'),
    ]
)

UpperCamelCase     :   Optional[Any]	      =		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'),
    ]
)

UpperCamelCase     :   List[str]	      =		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'),
    ]
)

UpperCamelCase     :   Optional[Any]	      =		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'),
    ]
)

UpperCamelCase     :   Optional[Any]	      =		OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

UpperCamelCase     :   Optional[int]	      =		OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

UpperCamelCase     :   Dict	      =		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'),
    ]
)

UpperCamelCase     :   List[Any]	      =		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'),
    ]
)

UpperCamelCase     :   List[str]	      =		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'),
    ]
)

UpperCamelCase     :   int	      =		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'),
    ]
)

UpperCamelCase     :   Tuple	      =		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'),
    ]
)

UpperCamelCase     :   Tuple	      =		OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

UpperCamelCase     :   str	      =		OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

UpperCamelCase     :   Optional[int]	      =		OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

UpperCamelCase     :   int	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCamelCase     :   List[str]	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCamelCase     :   str	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase     :   List[Any]	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCamelCase     :   Any	      =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCamelCase     :   List[str]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCamelCase     :   List[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase     :   List[str]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCamelCase     :   Tuple	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCamelCase     :   Union[str, Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCamelCase     :   Optional[Any]	      =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(FlaxAutoModel)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_PRETRAINING_MAPPING



UpperCamelCase     :   Optional[int]	      =		auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



UpperCamelCase     :   Dict	      =		auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_MASKED_LM_MAPPING



UpperCamelCase     :   List[str]	      =		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



UpperCamelCase     :   Tuple	      =		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



UpperCamelCase     :   Any	      =		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



UpperCamelCase     :   Tuple	      =		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



UpperCamelCase     :   int	      =		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')




class 		UpperCamelCase__     (_BaseAutoModelClass      ):




		'''simple docstring'''





		_UpperCamelCase						=       FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



UpperCamelCase     :   Union[str, Any]	      =		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)





"""simple docstring"""
from collections import defaultdict

def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		first_str.lower().strip()
   _lowerCamelCase	:					int   =		second_str.lower().strip()

   # Remove whitespace
   _lowerCamelCase	:					Any   =		first_str.replace(""" """						, """"""   )
   _lowerCamelCase	:					List[str]   =		second_str.replace(""" """						, """"""   )

   # Strings of different lengths are not anagrams
   if len(__snake_case   ) != len(__snake_case   ):
      return False

   # Default values for count should be 0
   _lowerCamelCase	:					defaultdict[str, int]   =		defaultdict(__snake_case   )

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(__snake_case   )   ):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()   )


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase				=						input("""Enter the first string """).strip()
 UpperCAmelCase				=						input("""Enter the second string """).strip()

 UpperCAmelCase				=						check_anagrams(input_a, input_b)
 print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')

