




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_	:				Optional[Any]  				= logging.get_logger(__name__)


lowercase_	:				Optional[Any]  				= 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_	:				List[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'),
    ]
)

lowercase_	:				Dict  				= 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_	:				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'),
    ]
)

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

lowercase_	:				List[Any]  				= OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

lowercase_	:				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'),
    ]
)

lowercase_	:				str  				= 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_	:				List[Any]  				= 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_	:				Dict  				= 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_	:				int  				= 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_	:				Union[str, Any]  				= OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

lowercase_	:				Optional[int]  				= OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

lowercase_	:				List[str]  				= OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

lowercase_	:				Tuple  				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
lowercase_	:				Dict  				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
lowercase_	:				Dict  				= _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[int]  				= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_	:				str  				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
lowercase_	:				Union[str, Any]  				= _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
lowercase_	:				Optional[int]  				= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
lowercase_	:				Optional[int]  				= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
lowercase_	:				Tuple  				= _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_	:				List[Any]  				= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
lowercase_	:				List[str]  				= _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : Dict      =     FLAX_MODEL_MAPPING




lowercase_	:				Any  				= auto_class_update(FlaxAutoModel)






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : List[Any]      =     FLAX_MODEL_FOR_PRETRAINING_MAPPING




lowercase_	:				Dict  				= auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : str      =     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




lowercase_	:				Any  				= auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : List[Any]      =     FLAX_MODEL_FOR_MASKED_LM_MAPPING




lowercase_	:				Dict  				= auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : Tuple      =     FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




lowercase_	:				Optional[int]  				= auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : List[Any]      =     FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




lowercase_	:				Tuple  				= auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : Optional[int]      =     FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




lowercase_	:				Union[str, Any]  				= auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : int      =     FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




lowercase_	:				List[Any]  				= auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : Any      =     FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




lowercase_	:				str  				= auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : List[Any]      =     FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




lowercase_	:				Tuple  				= auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : str      =     FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




lowercase_	:				int  				= auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : List[Any]      =     FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




lowercase_	:				Tuple  				= auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')






class     __lowerCAmelCase (       _BaseAutoModelClass ):
			snake_case_      : Any      =     FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




lowercase_	:				List[str]  				= auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)











"""simple docstring"""





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)  # pylint: disable=invalid-name


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(SCREAMING_SNAKE_CASE			):
                  return ext

      raise Exception(
          F'Unable to determine file format from file extension {path}. '
          F'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       pipeline(
          task=args.task							,						model=args.model if args.model else None							,						config=args.config							,						tokenizer=args.tokenizer							,						device=args.device							,						)
      lowerCAmelCase  						=       try_infer_format_from_ext(args.input			) if args.format == """infer""" else args.format
      lowerCAmelCase  						=       PipelineDataFormat.from_str(
          format=SCREAMING_SNAKE_CASE							,						output_path=args.output							,						input_path=args.input							,						column=args.column if args.column else nlp.default_input_names							,						overwrite=args.overwrite							,						)
      return RunCommand(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)






class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       nlp
            lowerCAmelCase  						=       reader





      @staticmethod
      def     _snake_case			(       lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       parser.add_parser("""run"""      ,						help="""Run a pipeline through the CLI""" )
            run_parser.add_argument("""--task"""      ,						choices=get_supported_tasks()      ,						help="""Task to run""" )
            run_parser.add_argument("""--input"""      ,						type=lowercase      ,						help="""Path to the file to use for inference""" )
            run_parser.add_argument("""--output"""      ,						type=lowercase      ,						help="""Path to the file that will be used post to write results.""" )
            run_parser.add_argument("""--model"""      ,						type=lowercase      ,						help="""Name or path to the model to instantiate.""" )
            run_parser.add_argument("""--config"""      ,						type=lowercase      ,						help="""Name or path to the model's config to instantiate.""" )
            run_parser.add_argument(
                """--tokenizer"""      ,						type=lowercase      ,						help="""Name of the tokenizer to use. (default: same as the model name)""" )
            run_parser.add_argument(
                """--column"""      ,						type=lowercase      ,						help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""      ,						)
            run_parser.add_argument(
                """--format"""      ,						type=lowercase      ,						default="""infer"""      ,						choices=PipelineDataFormat.SUPPORTED_FORMATS      ,						help="""Input format to read from"""      ,						)
            run_parser.add_argument(
                """--device"""      ,						type=lowercase      ,						default=-1      ,						help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""      ,						)
            run_parser.add_argument("""--overwrite"""      ,						action="""store_true"""      ,						help="""Allow overwriting the output file.""" )
            run_parser.set_defaults(func=lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._nlp, []

            for entry in self._reader:
                  lowerCAmelCase  						=       nlp(**lowercase ) if self._reader.is_multi_columns else nlp(lowercase )
                  if isinstance(lowercase      ,						lowercase ):
                        outputs.append(lowercase )
                  else:
                        outputs += output

        # Saving data
            if self._nlp.binary_output:
                  lowerCAmelCase  						=       self._reader.save_binary(lowercase )
                  logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}' )
            else:
                  self._reader.save(lowercase )






