






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


lowerCamelCase_				: List[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_				: Optional[int]    	=							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_				: List[str]    	=							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_				: Tuple    	=							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_				: Optional[Any]    	=							OrderedDict(
    [
        # Model for Image-classsification
        ('''beit''', '''FlaxBeitForImageClassification'''),
        ('''regnet''', '''FlaxRegNetForImageClassification'''),
        ('''resnet''', '''FlaxResNetForImageClassification'''),
        ('''vit''', '''FlaxViTForImageClassification'''),
    ]
)

lowerCamelCase_				: List[str]    	=							OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

lowerCamelCase_				: List[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'''),
    ]
)

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

lowerCamelCase_				: 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'''),
    ]
)

lowerCamelCase_				: int    	=							OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

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

lowerCamelCase_				: int    	=							OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

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


			'''simple docstring'''





			__a							:							Tuple					     =					FLAX_MODEL_MAPPING
lowerCamelCase_				: List[Any]    	=							auto_class_update(FlaxAutoModel)
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					FLAX_MODEL_FOR_PRETRAINING_MAPPING
lowerCamelCase_				: str    	=							auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
lowerCamelCase_				: Tuple    	=							auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							List[Any]					     =					FLAX_MODEL_FOR_MASKED_LM_MAPPING
lowerCamelCase_				: List[Any]    	=							auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCamelCase_				: Tuple    	=							auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowerCamelCase_				: Union[str, Any]    	=							auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							str					     =					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
lowerCamelCase_				: List[str]    	=							auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							List[Any]					     =					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowerCamelCase_				: Dict    	=							auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							Dict					     =					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
lowerCamelCase_				: str    	=							auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							List[Any]					     =					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
lowerCamelCase_				: str    	=							auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							Union[str, Any]					     =					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowerCamelCase_				: Union[str, Any]    	=							auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							int					     =					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
lowerCamelCase_				: Any    	=							auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')
class    _SCREAMING_SNAKE_CASE							(		_BaseAutoModelClass	):


			'''simple docstring'''





			__a							:							Union[str, Any]					     =					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
lowerCamelCase_				: Any    	=							auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)








'''simple docstring'''


import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase_				: Any    	=							get_logger(__name__)
class    _SCREAMING_SNAKE_CASE							(		enum.Enum	):


			'''simple docstring'''





			__a							:							Tuple					     =					"all_checks"
			__a							:							List[Any]					     =					"basic_checks"
			__a							:							Dict					     =					"no_checks"
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''












def 	__magic_name__( _A						,      _A						,      _A=None					):
						'''simple docstring'''





						if expected_checksums is None:
												logger.info("""Unable to verify checksums."""					)
												return
						if len(set(_A					) - set(_A					)					) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(_A					) - set(_A					)					)					)
						if len(set(_A					) - set(_A					)					) > 0:
												raise UnexpectedDownloadedFile(str(set(_A					) - set(_A					)					)					)
						UpperCamelCase__         = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						UpperCamelCase__         = """ for """ + verification_name if verification_name is not None else """"""
						if len(_A					) > 0:
												raise NonMatchingChecksumError(
												    f"Checksums didn't match{for_verification_name}:\n"
												    f"{bad_urls}\n"
												    """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""					)
						logger.info("""All the checksums matched successfully""" + for_verification_name					)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''












def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						if expected_splits is None:
												logger.info("""Unable to verify splits sizes."""					)
												return
						if len(set(_A					) - set(_A					)					) > 0:
												raise ExpectedMoreSplits(str(set(_A					) - set(_A					)					)					)
						if len(set(_A					) - set(_A					)					) > 0:
												raise UnexpectedSplits(str(set(_A					) - set(_A					)					)					)
						UpperCamelCase__         = [
						    {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(_A					) > 0:
												raise NonMatchingSplitsSizesError(str(_A					)					)
						logger.info("""All the splits matched successfully."""					)







def 	__magic_name__( _A						,      _A = True					):
						'''simple docstring'''





						if record_checksum:
												UpperCamelCase__         = shaaaa()
												with open(_A						,      """rb"""					) as f:
																		for chunk in iter(lambda: f.read(1 << 20					)						,      B""""""					):
																								m.update(_A					)
																		UpperCamelCase__         = m.hexdigest()
						else:
												UpperCamelCase__         = None
						return {"num_bytes": os.path.getsize(_A					), "checksum": checksum}







def 	__magic_name__( _A					):
						'''simple docstring'''





						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False
