




from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


snake_case_        =			8

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]=BITS      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	x.device

     lowercase__   :       Dict				=	(x * 255).int().clamp(0     ,					255      )

     lowercase__   :       Optional[int]				=	2 ** torch.arange(bits - 1     ,					-1     ,					-1     ,					device=__lowerCAmelCase      )
     lowercase__   :       Optional[int]				=	rearrange(__lowerCAmelCase     ,					'd -> d 1 1'      )
     lowercase__   :       Dict				=	rearrange(__lowerCAmelCase     ,					'b c h w -> b c 1 h w'      )

     lowercase__   :       Tuple				=	((x & mask) != 0).float()
     lowercase__   :       str				=	rearrange(__lowerCAmelCase     ,					'b c d h w -> b (c d) h w'      )
     lowercase__   :       List[Any]				=	bits * 2 - 1
     return bits

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]=BITS      ):



     '''simple docstring'''





     lowercase__   :       Any				=	x.device

     lowercase__   :       Union[str, Any]				=	(x > 0).int()
     lowercase__   :       List[Any]				=	2 ** torch.arange(bits - 1     ,					-1     ,					-1     ,					device=__lowerCAmelCase     ,					dtype=torch.intaa      )

     lowercase__   :       str				=	rearrange(__lowerCAmelCase     ,					'd -> d 1 1'      )
     lowercase__   :       List[str]				=	rearrange(__lowerCAmelCase     ,					'b (c d) h w -> b c d h w'     ,					d=8      )
     lowercase__   :       int				=	reduce(x * mask     ,					'b c d h w -> b c h w'     ,					'sum'      )
     return (dec / 255).clamp(0.0     ,					1.0      )

def      snake_case__      (   self		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Dict = 0.0     ,					SCREAMING_SNAKE_CASE_		:   Dict = True     ,					SCREAMING_SNAKE_CASE_		:   str=None     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any] = True     ,					):



     '''simple docstring'''





     if self.num_inference_steps is None:
          raise ValueError(
              'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'      )

     # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
     # Ideally, read DDIM paper in-detail understanding

     # Notation (<variable name> -> <name in paper>
     # - pred_noise_t -> e_theta(x_t, t)
     # - pred_original_sample -> f_theta(x_t, t) or x_0
     # - std_dev_t -> sigma_t
     # - eta -> η
     # - pred_sample_direction -> "direction pointing to x_t"
     # - pred_prev_sample -> "x_t-1"

     # 1. get previous step value (=t-1)
     lowercase__   :       Optional[Any]				=	timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     lowercase__   :       Optional[int]				=	self.alphas_cumprod[timestep]
     lowercase__   :       Any				=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     lowercase__   :       Optional[Any]				=	1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     lowercase__   :       str				=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     lowercase__   :       Optional[int]				=	self.bit_scale
     if self.config.clip_sample:
          lowercase__   :       Tuple				=	torch.clamp(__lowerCAmelCase     ,					-scale     ,					__lowerCAmelCase      )

     # 5. compute variance: "sigma_t(η)" -> see formula (16)
     # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
     lowercase__   :       Union[str, Any]				=	self._get_variance(__lowerCAmelCase     ,					__lowerCAmelCase      )
     lowercase__   :       Optional[Any]				=	eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          lowercase__   :       Any				=	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

     # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     lowercase__   :       Dict				=	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     lowercase__   :       int				=	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if eta > 0:
          # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
          lowercase__   :       List[Any]				=	model_output.device if torch.is_tensor(__lowerCAmelCase      ) else '''cpu'''
          lowercase__   :       Optional[int]				=	torch.randn(model_output.shape     ,					dtype=model_output.dtype     ,					generator=__lowerCAmelCase      ).to(__lowerCAmelCase      )
          lowercase__   :       List[str]				=	self._get_variance(__lowerCAmelCase     ,					__lowerCAmelCase      ) ** 0.5 * eta * noise

          lowercase__   :       Optional[int]				=	prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase     ,					pred_original_sample=__lowerCAmelCase      )

def      snake_case__      (   self		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   List[str]="epsilon"     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]=None     ,					SCREAMING_SNAKE_CASE_		:   Any = True     ,					):



     '''simple docstring'''





     lowercase__   :       Dict				=	timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          lowercase__   :       Tuple				=	torch.split(__lowerCAmelCase     ,					sample.shape[1]     ,					dim=1      )
     else:
          lowercase__   :       Dict				=	None

     # 1. compute alphas, betas
     lowercase__   :       Tuple				=	self.alphas_cumprod[t]
     lowercase__   :       str				=	self.alphas_cumprod[t - 1] if t > 0 else self.one
     lowercase__   :       int				=	1 - alpha_prod_t
     lowercase__   :       int				=	1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if prediction_type == "epsilon":
          lowercase__   :       int				=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          lowercase__   :       Optional[int]				=	model_output
     else:
          raise ValueError(f"""Unsupported prediction_type {prediction_type}."""      )

     # 3. Clip "predicted x_0"
     lowercase__   :       Optional[int]				=	self.bit_scale
     if self.config.clip_sample:
          lowercase__   :       str				=	torch.clamp(__lowerCAmelCase     ,					-scale     ,					__lowerCAmelCase      )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     lowercase__   :       str				=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     lowercase__   :       List[Any]				=	self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     lowercase__   :       Dict				=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     lowercase__   :       List[str]				=	0
     if t > 0:
          lowercase__   :       Any				=	torch.randn(
              model_output.size()     ,					dtype=model_output.dtype     ,					layout=model_output.layout     ,					generator=__lowerCAmelCase      ).to(model_output.device      )
          lowercase__   :       str				=	(self._get_variance(__lowerCAmelCase     ,					predicted_variance=__lowerCAmelCase      ) ** 0.5) * noise

     lowercase__   :       str				=	pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase     ,					pred_original_sample=__lowerCAmelCase      )


class 				SCREAMING_SNAKE_CASE__ (__lowerCamelCase					):





    def __init__(				self						,       a						,       a						,       a = 1.0						,       ):
         super().__init__()
         lowercase__   :       Optional[int]				=	bit_scale
         lowercase__   :       Optional[Any]				=	(
             ddim_bit_scheduler_step if isinstance(__lowercase						,       __lowercase) else ddpm_bit_scheduler_step
         )

         self.register_modules(unet=__lowercase						,       scheduler=__lowercase)





    @torch.no_grad()
    def __call__(				self						,       a = 256						,       a = 256						,       a = 50						,       a = None						,       a = 1						,       a = "pil"						,       a = True						,       **a						,       ):
         lowercase__   :       Tuple				=	torch.randn(
             (batch_size, self.unet.config.in_channels, height, width)						,       generator=__lowercase						,       )
         lowercase__   :       int				=	decimal_to_bits(__lowercase) * self.bit_scale
         lowercase__   :       Optional[Any]				=	latents.to(self.device)

         self.scheduler.set_timesteps(__lowercase)

         for t in self.progress_bar(self.scheduler.timesteps):
              # predict the noise residual
              lowercase__   :       List[Any]				=	self.unet(__lowercase						,       __lowercase).sample

              # compute the previous noisy sample x_t -> x_t-1
              lowercase__   :       Optional[Any]				=	self.scheduler.step(__lowercase						,       __lowercase						,       __lowercase).prev_sample

         lowercase__   :       List[str]				=	bits_to_decimal(__lowercase)

         if output_type == "pil":
              lowercase__   :       Optional[int]				=	self.numpy_to_pil(__lowercase)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__lowercase)




from collections import OrderedDict

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


snake_case_        =			logging.get_logger(__name__)


snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

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

snake_case_        =			OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			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'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

snake_case_        =			OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

snake_case_        =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
snake_case_        =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
snake_case_        =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
snake_case_        =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
snake_case_        =			_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModel)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_PRETRAINING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[Any]														=					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_MASKED_LM_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Optional[int]														=					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Dict														=					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	str														=					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Union[str, Any]														=					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	Tuple														=					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



snake_case_        =			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')


class 				SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass					):
    __lowerCamelCase						:	List[str]														=					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



snake_case_        =			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)

