'''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


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)


__lowerCAmelCase						:							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"),
    ]
)

__lowerCAmelCase						:							Union[str, 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"),
    ]
)

__lowerCAmelCase						:							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"),
    ]
)

__lowerCAmelCase						:							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"),
    ]
)

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

__lowerCAmelCase						:							Union[str, Any]    							=						OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

__lowerCAmelCase						:							Optional[Any]    							=						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"),
    ]
)

__lowerCAmelCase						:							Tuple    							=						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"),
    ]
)

__lowerCAmelCase						:							int    							=						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"),
    ]
)

__lowerCAmelCase						:							str    							=						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"),
    ]
)

__lowerCAmelCase						:							Optional[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"),
    ]
)

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

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

__lowerCAmelCase						:							str    							=						OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

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






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_MAPPING

__lowerCAmelCase						:							Dict    							=						auto_class_update(FlaxAutoModel)






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_PRETRAINING_MAPPING

__lowerCAmelCase						:							List[Any]    							=						auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

__lowerCAmelCase						:							List[Any]    							=						auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_MASKED_LM_MAPPING

__lowerCAmelCase						:							Dict    							=						auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

__lowerCAmelCase						:							Tuple    							=						auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

__lowerCAmelCase						:							List[str]    							=						auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__lowerCAmelCase						:							Any    							=						auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

__lowerCAmelCase						:							Dict    							=						auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

__lowerCAmelCase						:							Tuple    							=						auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

__lowerCAmelCase						:							int    							=						auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

__lowerCAmelCase						:							Any    							=						auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")






class     A ( _BaseAutoModelClass ):
							a_										=   FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

__lowerCAmelCase						:							int    							=						auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)


"""simple docstring"""




import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a		:       Optional[Any]     						=       False
a		:       str     						=       False



def     __magic_name__		(					UpperCamelCase     :		Namespace     )     ->						Optional[int]:
    return TrainCommand(UpperCamelCase     )







class     lowercase(_lowercase       ):



     @staticmethod
     def 			lowercase__  (							__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			parser.add_parser('train'     ,				help='CLI tool to train a model on a task.'   )

         train_parser.add_argument(
             '--train_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				required=__SCREAMING_SNAKE_CASE     ,				help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'     ,				)
         train_parser.add_argument(
             '--column_label'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0     ,				help='Column of the dataset csv file with example labels.'   )
         train_parser.add_argument(
             '--column_text'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1     ,				help='Column of the dataset csv file with example texts.'   )
         train_parser.add_argument(
             '--column_id'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=2     ,				help='Column of the dataset csv file with example ids.'   )
         train_parser.add_argument(
             '--skip_first_row'     ,				action='store_true'     ,				help='Skip the first row of the csv file (headers).'   )

         train_parser.add_argument('--validation_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=''     ,				help='path to validation dataset.'   )
         train_parser.add_argument(
             '--validation_split'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0.1     ,				help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'     ,				)

         train_parser.add_argument('--output'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='./'     ,				help='path to saved the trained model.'   )

         train_parser.add_argument(
             '--task'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='text_classification'     ,				help='Task to train the model on.'   )
         train_parser.add_argument(
             '--model'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='bert-base-uncased'     ,				help='Model\'s name or path to stored model.'   )
         train_parser.add_argument('--train_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3_2     ,				help='Batch size for training.'   )
         train_parser.add_argument('--valid_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=6_4     ,				help='Batch size for validation.'   )
         train_parser.add_argument('--learning_rate'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3e-5     ,				help='Learning rate.'   )
         train_parser.add_argument('--adam_epsilon'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1e-08     ,				help='Epsilon for Adam optimizer.'   )
         train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE   )



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			logging.get_logger('transformers-cli/training'   )

         a__		=			'tf' if is_tf_available() else 'torch'

         os.makedirs(args.output     ,				exist_ok=__SCREAMING_SNAKE_CASE   )
         a__		=			args.output

         a__		=			args.column_label
         a__		=			args.column_text
         a__		=			args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'   )
         if args.task == "text_classification":
             a__		=			TextClassificationPipeline.from_pretrained(args.model   )
         elif args.task == "token_classification":
             raise NotImplementedError
         elif args.task == "question_answering":
             raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'   )
         a__		=			Processor.create_from_csv(
             args.train_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)
         a__		=			None
         if args.validation_data:
             self.logger.info(f'Loading validation dataset from {args.validation_data}'   )
             a__		=			Processor.create_from_csv(
                 args.validation_data     ,				column_label=args.column_label     ,				column_text=args.column_text     ,				column_id=args.column_id     ,				skip_first_row=args.skip_first_row     ,				)

         a__		=			args.validation_split
         a__		=			args.train_batch_size
         a__		=			args.valid_batch_size
         a__		=			args.learning_rate
         a__		=			args.adam_epsilon



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         if self.framework == "tf":
             return self.run_tf()
         return self.run_torch()



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         raise NotImplementedError




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         self.pipeline.fit(
             self.train_dataset     ,				validation_data=self.valid_dataset     ,				validation_split=self.validation_split     ,				learning_rate=self.learning_rate     ,				adam_epsilon=self.adam_epsilon     ,				train_batch_size=self.train_batch_size     ,				valid_batch_size=self.valid_batch_size     ,				)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output   )


