





"""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 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_MAPPING






a_			=      auto_class_update(FlaxAutoModel)






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_PRETRAINING_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_MASKED_LM_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					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 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING






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






class 		__snake_case (		_BaseAutoModelClass					):





 """simple docstring"""


 _lowerCamelCase				=					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 pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					""""""
 _lowerCamelCase				=					"""hf-legacy"""  # "hf://"" is reserved for hffs

 def __init__(   self ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(self ,						**__lowerCamelCase  )
     __A   :						int									=					repo_info
     __A   :						Optional[int]									=					token
     __A   :						int									=					None

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if self.dir_cache is None:
         __A   :						int									=					{}
         for hf_file in self.repo_info.siblings:
             # TODO(QL): add sizes
             __A   :						Tuple									=					{
                 '''name''': hf_file.rfilename,
                 '''size''': None,
                 '''type''': '''file''',
             }
             self.dir_cache.update(
                 {
                     str(__lowerCamelCase  ): {'''name''': str(__lowerCamelCase  ), '''size''': None, '''type''': '''directory'''}
                     for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
                 }  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase = "rb" ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     if not isinstance(self.repo_info ,						__lowerCamelCase  ):
         raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
     __A   :						Union[str, Any]									=					hf_hub_url(self.repo_info.id ,						__lowerCamelCase ,						revision=self.repo_info.sha  )
     return fsspec.open(
         __lowerCamelCase ,						mode=__lowerCamelCase ,						headers=get_authentication_headers_for_url(__lowerCamelCase ,						use_auth_token=self.token  ) ,						client_kwargs={'''trust_env''': True} ,						).open()

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     self._get_dirs()
     __A   :						Optional[Any]									=					self._strip_protocol(__lowerCamelCase  )
     if path in self.dir_cache:
         return self.dir_cache[path]
     else:
         raise FileNotFoundError(__lowerCamelCase  )







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=False ,						**__lowerCamelCase  ):



     '''simple docstring'''




     self._get_dirs()
     __A   :						Any									=					PurePosixPath(path.strip('''/'''  )  )
     __A   :						Any									=					{}
     for p, f in self.dir_cache.items():
         __A   :						List[Any]									=					PurePosixPath(p.strip('''/'''  )  )
         __A   :						Dict									=					p.parent
         if root == path:
             __A   :						Union[str, Any]									=					f
     __A   :						List[str]									=					list(paths.values()  )
     if detail:
         return out
     else:
         return sorted(f['''name'''] for f in out  )




