



from collections import OrderedDict

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


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


_lowercase						: Dict     						=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"),
    ]
)

_lowercase						: str     						=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"),
    ]
)

_lowercase						: Tuple     						=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"),
    ]
)

_lowercase						: Dict     						=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"),
    ]
)

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

_lowercase						: List[str]     						=OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

_lowercase						: str     						=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"),
    ]
)

_lowercase						: Dict     						=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"),
    ]
)

_lowercase						: 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"),
    ]
)

_lowercase						: Optional[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"),
    ]
)

_lowercase						: 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"),
    ]
)

_lowercase						: Tuple     						=OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

_lowercase						: Any     						=OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

_lowercase						: List[Any]     						=OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

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

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Optional[Any]         =	FLAX_MODEL_MAPPING




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

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Optional[int]         =	FLAX_MODEL_FOR_PRETRAINING_MAPPING




_lowercase						: Dict     						=auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Optional[int]         =	FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




_lowercase						: Any     						=auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:List[Any]         =	FLAX_MODEL_FOR_MASKED_LM_MAPPING




_lowercase						: int     						=auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Optional[int]         =	FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




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

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:str         =	FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




_lowercase						: str     						=auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:List[Any]         =	FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




_lowercase						: Dict     						=auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:str         =	FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




_lowercase						: Tuple     						=auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Any         =	FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




_lowercase						: List[str]     						=auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Union[str, Any]         =	FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




_lowercase						: str     						=auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Tuple         =	FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




_lowercase						: Optional[Any]     						=auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Tuple         =	FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




_lowercase						: int     						=auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")

class   snake_case__							(_BaseAutoModelClass						):







						"""simple docstring"""




						__lowerCAmelCase	:Union[str, Any]         =	FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




_lowercase						: Tuple     						=auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)











import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

