





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   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_MAPPING



__A							   =							auto_class_update(FlaxAutoModel)



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_PRETRAINING_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_MASKED_LM_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     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   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



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



class   _SCREAMING_SNAKE_CASE		(      _BaseAutoModelClass       ):







 '''simple docstring'''
 lowercase_						=     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 unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def 					A__   (			self      :		Optional[int]		)	->	List[str]:


       '''simple docstring'''

       return [
           {"col_1": 3, "col_2": "a"},
           {"col_1": 2, "col_2": "b"},
           {"col_1": 1, "col_2": "c"},
           {"col_1": 0, "col_2": "d"},
       ]






      def 					A__   (			self      :		Any		)	->	List[str]:


       '''simple docstring'''

       lowercase       : List[str]						={'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
       return Dataset.from_dict(UpperCAmelCase		)






      def 					A__   (			self      :		Any		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Tuple						=self._create_example_records()
       lowercase       : Tuple						=Dataset.from_list(UpperCAmelCase		)
       self.assertListEqual(dset.column_names  ,   ['''col_1''', '''col_2''']		)
       for i, r in enumerate(UpperCAmelCase		):
        self.assertDictEqual(UpperCAmelCase  ,   example_records[i]		)






      def 					A__   (			self      :		Dict		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[Any]						=self._create_example_records()
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       lowercase       : Tuple						=Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}		)
       self.assertEqual(dset.info  ,   dset_from_dict.info		)






      def 					A__   (			self      :		str		)	->	List[Any]:  # checks what happens with missing columns


       '''simple docstring'''

       lowercase       : Tuple						=[{'''col_1''': 1}, {'''col_2''': '''x'''}]
       lowercase       : Optional[Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertDictEqual(dset[0]  ,   {'''col_1''': 1}		)
       self.assertDictEqual(dset[1]  ,   {'''col_1''': None}		)  # NB: first record is used for columns






      def 					A__   (			self      :		List[Any]		)	->	int:  # checks if the type can be inferred from the second record


       '''simple docstring'''

       lowercase       : List[Any]						=[{'''col_1''': []}, {'''col_1''': [1, 2]}]
       lowercase       : Union[str, Any]						=Dataset.from_list(UpperCAmelCase		)
       self.assertEqual(dset.info.features['''col_1''']  ,   Sequence(Value('''int64'''		)		)		)






      def 					A__   (			self      :		int		)	->	str:


       '''simple docstring'''

       lowercase       : List[str]						=Dataset.from_list([]		)
       self.assertEqual(len(UpperCAmelCase		)  ,   0		)
       self.assertListEqual(dset.column_names  ,   []		)



