

import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


a_     =			logging.get_logger(__name__)






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							='AutoTokenizer'
			lowerCamelCase__							=['tokenizer']

			lowerCamelCase__							={
			    'semantic_prompt': 1,
			    'coarse_prompt': 2,
			    'fine_prompt': 2,
			}
			def __init__(	self							:       Any					,					a							:       int					,					a							:       Any=None		)							->				List[Any]:

				"""simple docstring"""

				super().__init__(a		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				speaker_embeddings
			@classmethod
			def 							__UpperCamelCase       (	cls							:       Optional[int]					,					a							:       Optional[Any]					,					a							:       Any="speaker_embeddings_path.json"					,					**a							:       List[str]		)							->				List[str]:

				"""simple docstring"""

				if speaker_embeddings_dict_path is not None:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				get_file_from_repo(
					    a					,					a					,					subfolder=kwargs.pop("subfolder"					,					a		)					,					cache_dir=kwargs.pop("cache_dir"					,					a		)					,					force_download=kwargs.pop("force_download"					,					a		)					,					proxies=kwargs.pop("proxies"					,					a		)					,					resume_download=kwargs.pop("resume_download"					,					a		)					,					local_files_only=kwargs.pop("local_files_only"					,					a		)					,					use_auth_token=kwargs.pop("use_auth_token"					,					a		)					,					revision=kwargs.pop("revision"					,					a		)					,					)
					if speaker_embeddings_path is None:
						logger.warning(
						    F"`{os.path.join(a					,					a		)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."		)
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
					else:
						with open(a		) as speaker_embeddings_json:
							SCREAMING_SNAKE_CASE      :					str    =				json.load(a		)
				else:
					SCREAMING_SNAKE_CASE      :					List[str]    =				None

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				AutoTokenizer.from_pretrained(a					,					**a		)

				return cls(tokenizer=a					,					speaker_embeddings=a		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       Tuple					,					a							:       Tuple="speaker_embeddings_path.json"					,					a							:       Union[str, Any]="speaker_embeddings"					,					a							:       bool = False					,					**a							:       List[str]					,					)							->				List[Any]:

				"""simple docstring"""

				if self.speaker_embeddings is not None:
					os.makedirs(os.path.join(a					,					a					,					"v2"		)					,					exist_ok=a		)

					SCREAMING_SNAKE_CASE      :					Any    =				{}

					SCREAMING_SNAKE_CASE      :					int    =				save_directory

					for prompt_key in self.speaker_embeddings:
						if prompt_key != "repo_or_path":
							SCREAMING_SNAKE_CASE      :					Tuple    =				self._load_voice_preset(a		)

							SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{}
							for key in self.speaker_embeddings[prompt_key]:
								np.save(
								    os.path.join(
								        embeddings_dict["repo_or_path"]					,					a					,					F"{prompt_key}_{key}"		)					,					voice_preset[key]					,					allow_pickle=a					,					)
								SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				os.path.join(a					,					F"{prompt_key}_{key}.npy"		)

							SCREAMING_SNAKE_CASE      :					int    =				tmp_dict

					with open(os.path.join(a					,					a		)					,					"w"		) as fp:
						json.dump(a					,					a		)

				super().save_pretrained(a					,					a					,					**a		)
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       str = None					,					**a							:       str		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.speaker_embeddings[voice_preset]

				SCREAMING_SNAKE_CASE      :					Any    =				{}
				for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
					if key not in voice_preset_paths:
						raise ValueError(
						    F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."		)

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				get_file_from_repo(
					    self.speaker_embeddings.get("repo_or_path"					,					"/"		)					,					voice_preset_paths[key]					,					subfolder=kwargs.pop("subfolder"					,					a		)					,					cache_dir=kwargs.pop("cache_dir"					,					a		)					,					force_download=kwargs.pop("force_download"					,					a		)					,					proxies=kwargs.pop("proxies"					,					a		)					,					resume_download=kwargs.pop("resume_download"					,					a		)					,					local_files_only=kwargs.pop("local_files_only"					,					a		)					,					use_auth_token=kwargs.pop("use_auth_token"					,					a		)					,					revision=kwargs.pop("revision"					,					a		)					,					)
					if path is None:
						raise ValueError(
						    F"`{os.path.join(self.speaker_embeddings.get('repo_or_path'					,					'/'		)					,					voice_preset_paths[key]		)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."		)

					SCREAMING_SNAKE_CASE      :					Optional[int]    =				np.load(a		)

				return voice_preset_dict
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       Optional[dict] = None		)							->				List[Any]:

				"""simple docstring"""

				for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
					if key not in voice_preset:
						raise ValueError(F"Voice preset unrecognized, missing {key} as a key."		)

					if not isinstance(voice_preset[key]					,					np.ndarray		):
						raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)

					if len(voice_preset[key].shape		) != self.preset_shape[key]:
						raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)






			def __call__(	self							:       Union[str, Any]					,					a							:       Dict=None					,					a							:       Dict=None					,					a							:       Tuple="pt"					,					a							:       List[Any]=256					,					a							:       Optional[Any]=False					,					a							:       Tuple=True					,					a							:       str=False					,					**a							:       List[str]					,					)							->				Tuple:

				"""simple docstring"""

				if voice_preset is not None and not isinstance(a					,					a		):
					if (
					    isinstance(a					,					a		)
					    and self.speaker_embeddings is not None
					    and voice_preset in self.speaker_embeddings
					):
						SCREAMING_SNAKE_CASE      :					str    =				self._load_voice_preset(a		)

					else:
						if isinstance(a					,					a		) and not voice_preset.endswith(".npz"		):
							SCREAMING_SNAKE_CASE      :					List[str]    =				voice_preset + ".npz"

						SCREAMING_SNAKE_CASE      :					Dict    =				np.load(a		)

				if voice_preset is not None:
					self._validate_voice_preset_dict(a					,					**a		)
					SCREAMING_SNAKE_CASE      :					Dict    =				BatchFeature(data=a					,					tensor_type=a		)

				SCREAMING_SNAKE_CASE      :					str    =				self.tokenizer(
				    a					,					return_tensors=a					,					padding="max_length"					,					max_length=a					,					return_attention_mask=a					,					return_token_type_ids=a					,					add_special_tokens=a					,					**a					,					)

				if voice_preset is not None:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				voice_preset

				return encoded_text



from collections import OrderedDict

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


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


UpperCAmelCase_      : List[str]  					=       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'),
    ]
)

UpperCAmelCase_      : 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'),
    ]
)

UpperCAmelCase_      : 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'),
    ]
)

UpperCAmelCase_      : 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'),
    ]
)

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

UpperCAmelCase_      : int  					=       OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

UpperCAmelCase_      : 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'),
    ]
)

UpperCAmelCase_      : List[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'),
    ]
)

UpperCAmelCase_      : List[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'),
    ]
)

UpperCAmelCase_      : Union[str, Any]  					=       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'),
    ]
)

UpperCAmelCase_      : Dict  					=       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'),
    ]
)

UpperCAmelCase_      : List[str]  					=       OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

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

UpperCAmelCase_      : Union[str, Any]  					=       OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCAmelCase_      : List[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Any  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCAmelCase_      : Dict  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : Optional[int]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCAmelCase_      : List[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCAmelCase_      : int  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCAmelCase_      : Tuple  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCAmelCase_      : Tuple  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCAmelCase_      : Optional[Any]  					=       _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModel)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Any        =						FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[Any]        =						FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase_      : Union[str, Any]  					=       auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCAmelCase_      : Optional[int]  					=       auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Optional[Any]  					=       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Tuple        =						FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     List[str]        =						FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCAmelCase_      : int  					=       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCAmelCase_      : Dict  					=       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Dict        =						FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_      : str  					=       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[Any]        =						FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCAmelCase_      : Tuple  					=       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class  SCREAMING_SNAKE_CASE__  (				_BaseAutoModelClass			):
      snake_case__				:     Optional[int]        =						FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCAmelCase_      : Union[str, Any]  					=       auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)


