

from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      42





class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case		):







   '''simple docstring'''




   @register_to_config
   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			int = 32     , __lowerCamelCase					:			int = 64     , __lowerCamelCase					:			int = 20     , __lowerCamelCase					:			int = 768     , __lowerCamelCase					:			str=77     , __lowerCamelCase					:			Union[str, Any]=4     , __lowerCamelCase					:			float = 0.0     , __lowerCamelCase					:			str = "silu"     , __lowerCamelCase					:			Optional[str] = None     , __lowerCamelCase					:			Optional[str] = None     , __lowerCamelCase					:			Optional[str] = "linear"     , __lowerCamelCase					:			Optional[str] = "prd"     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			Optional[int] = None     , ):
       super().__init__()
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   attention_head_dim
       SCREAMING_SNAKE_CASE										=   num_attention_heads * attention_head_dim
       SCREAMING_SNAKE_CASE										=   additional_embeddings

       SCREAMING_SNAKE_CASE										=   time_embed_dim or inner_dim
       SCREAMING_SNAKE_CASE										=   embedding_proj_dim or embedding_dim
       SCREAMING_SNAKE_CASE										=   clip_embed_dim or embedding_dim

       SCREAMING_SNAKE_CASE										=   Timesteps(__lowerCamelCase     , __lowerCamelCase     , 0			)
       SCREAMING_SNAKE_CASE										=   TimestepEmbedding(__lowerCamelCase     , __lowerCamelCase     , out_dim=__lowerCamelCase     , act_fn=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)

       if embedding_proj_norm_type is None:
           SCREAMING_SNAKE_CASE										=   None
       elif embedding_proj_norm_type == "layer":
           SCREAMING_SNAKE_CASE										=   nn.LayerNorm(__lowerCamelCase			)
       else:
           raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"			)

       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)

       if encoder_hid_proj_type is None:
           SCREAMING_SNAKE_CASE										=   None
       elif encoder_hid_proj_type == "linear":
           SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)
       else:
           raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"			)

       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , num_embeddings + additional_embeddings     , __lowerCamelCase			)			)

       if added_emb_type == "prd":
           SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , 1     , __lowerCamelCase			)			)
       elif added_emb_type is None:
           SCREAMING_SNAKE_CASE										=   None
       else:
           raise ValueError(
               f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."			)

       SCREAMING_SNAKE_CASE										=   nn.ModuleList(
           [
               BasicTransformerBlock(
                   __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , dropout=__lowerCamelCase     , activation_fn="gelu"     , attention_bias=__lowerCamelCase     , )
               for d in range(__lowerCamelCase			)
           ]			)

       if norm_in_type == "layer":
           SCREAMING_SNAKE_CASE										=   nn.LayerNorm(__lowerCamelCase			)
       elif norm_in_type is None:
           SCREAMING_SNAKE_CASE										=   None
       else:
           raise ValueError(f"Unsupported norm_in_type: {norm_in_type}."			)

       SCREAMING_SNAKE_CASE										=   nn.LayerNorm(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   nn.Linear(__lowerCamelCase     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   torch.full(
           [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]     , -10_000.0			)
       causal_attention_mask.triu_(1			)
       SCREAMING_SNAKE_CASE										=   causal_attention_mask[None, ...]
       self.register_buffer("causal_attention_mask"     , __lowerCamelCase     , persistent=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , __lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   nn.Parameter(torch.zeros(1     , __lowerCamelCase			)			)





   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   {}

       def fn_recursive_add_processors(__lowerCamelCase					:			str     , __lowerCamelCase					:			torch.nn.Module     , __lowerCamelCase					:			Dict[str, AttentionProcessor]			):
           if hasattr(__lowerCamelCase     , "set_processor"			):
               SCREAMING_SNAKE_CASE										=   module.processor

           for sub_name, child in module.named_children():
               fn_recursive_add_processors(f"{name}.{sub_name}"     , __lowerCamelCase     , __lowerCamelCase			)

           return processors

       for name, module in self.named_children():
           fn_recursive_add_processors(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

       return processors





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Union[AttentionProcessor, Dict[str, AttentionProcessor]]			):
       SCREAMING_SNAKE_CASE										=   len(self.attn_processors.keys()			)

       if isinstance(__lowerCamelCase     , __lowerCamelCase			) and len(__lowerCamelCase			) != count:
           raise ValueError(
               f"A dict of processors was passed, but the number of processors {len(__lowerCamelCase			)} does not match the"
               f" number of attention layers: {count}. Please make sure to pass {count} processor classes."			)

       def fn_recursive_attn_processor(__lowerCamelCase					:			str     , __lowerCamelCase					:			torch.nn.Module     , __lowerCamelCase					:			Union[str, Any]			):
           if hasattr(__lowerCamelCase     , "set_processor"			):
               if not isinstance(__lowerCamelCase     , __lowerCamelCase			):
                   module.set_processor(__lowerCamelCase			)
               else:
                   module.set_processor(processor.pop(f"{name}.processor"			)			)

           for sub_name, child in module.named_children():
               fn_recursive_attn_processor(f"{name}.{sub_name}"     , __lowerCamelCase     , __lowerCamelCase			)

       for name, module in self.named_children():
           fn_recursive_attn_processor(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]			):
       self.set_attn_processor(AttnProcessor()			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Union[torch.Tensor, float, int]     , __lowerCamelCase					:			torch.FloatTensor     , __lowerCamelCase					:			Optional[torch.FloatTensor] = None     , __lowerCamelCase					:			Optional[torch.BoolTensor] = None     , __lowerCamelCase					:			bool = True     , ):
       SCREAMING_SNAKE_CASE										=   hidden_states.shape[0]

       SCREAMING_SNAKE_CASE										=   timestep
       if not torch.is_tensor(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   torch.tensor([timesteps]     , dtype=torch.long     , device=hidden_states.device			)
       elif torch.is_tensor(__lowerCamelCase			) and len(timesteps.shape			) == 0:
           SCREAMING_SNAKE_CASE										=   timesteps[None].to(hidden_states.device			)

       # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
       SCREAMING_SNAKE_CASE										=   timesteps * torch.ones(__lowerCamelCase     , dtype=timesteps.dtype     , device=timesteps.device			)

       SCREAMING_SNAKE_CASE										=   self.time_proj(__lowerCamelCase			)

       # timesteps does not contain any weights and will always return f32 tensors
       # but time_embedding might be fp16, so we need to cast here.
       SCREAMING_SNAKE_CASE										=   timesteps_projected.to(dtype=self.dtype			)
       SCREAMING_SNAKE_CASE										=   self.time_embedding(__lowerCamelCase			)

       if self.embedding_proj_norm is not None:
           SCREAMING_SNAKE_CASE										=   self.embedding_proj_norm(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.embedding_proj(__lowerCamelCase			)
       if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
           SCREAMING_SNAKE_CASE										=   self.encoder_hidden_states_proj(__lowerCamelCase			)
       elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
           raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"			)

       SCREAMING_SNAKE_CASE										=   self.proj_in(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.positional_embedding.to(hidden_states.dtype			)

       SCREAMING_SNAKE_CASE										=   []
       SCREAMING_SNAKE_CASE										=   0

       if encoder_hidden_states is not None:
           additional_embeds.append(__lowerCamelCase			)
           additional_embeddings_len += encoder_hidden_states.shape[1]

       if len(proj_embeddings.shape			) == 2:
           SCREAMING_SNAKE_CASE										=   proj_embeddings[:, None, :]

       if len(hidden_states.shape			) == 2:
           SCREAMING_SNAKE_CASE										=   hidden_states[:, None, :]

       SCREAMING_SNAKE_CASE										=   additional_embeds + [
           proj_embeddings,
           time_embeddings[:, None, :],
           hidden_states,
       ]

       if self.prd_embedding is not None:
           SCREAMING_SNAKE_CASE										=   self.prd_embedding.to(hidden_states.dtype			).expand(__lowerCamelCase     , -1     , -1			)
           additional_embeds.append(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   torch.cat(
           __lowerCamelCase     , dim=1     , )

       # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
       SCREAMING_SNAKE_CASE										=   additional_embeddings_len + proj_embeddings.shape[1] + 1
       if positional_embeddings.shape[1] < hidden_states.shape[1]:
           SCREAMING_SNAKE_CASE										=   F.pad(
               __lowerCamelCase     , (
                   0,
                   0,
                   additional_embeddings_len,
                   self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
               )     , value=0.0     , )

       SCREAMING_SNAKE_CASE										=   hidden_states + positional_embeddings

       if attention_mask is not None:
           SCREAMING_SNAKE_CASE										=   (1 - attention_mask.to(hidden_states.dtype			)) * -10_000.0
           SCREAMING_SNAKE_CASE										=   F.pad(__lowerCamelCase     , (0, self.additional_embeddings)     , value=0.0			)
           SCREAMING_SNAKE_CASE										=   (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype			)
           SCREAMING_SNAKE_CASE										=   attention_mask.repeat_interleave(self.config.num_attention_heads     , dim=0			)

       if self.norm_in is not None:
           SCREAMING_SNAKE_CASE										=   self.norm_in(__lowerCamelCase			)

       for block in self.transformer_blocks:
           SCREAMING_SNAKE_CASE										=   block(__lowerCamelCase     , attention_mask=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   self.norm_out(__lowerCamelCase			)

       if self.prd_embedding is not None:
           SCREAMING_SNAKE_CASE										=   hidden_states[:, -1]
       else:
           SCREAMING_SNAKE_CASE										=   hidden_states[:, additional_embeddings_len:]

       SCREAMING_SNAKE_CASE										=   self.proj_to_clip_embeddings(__lowerCamelCase			)

       if not return_dict:
           return (predicted_image_embedding,)

       return PriorTransformerOutput(predicted_image_embedding=__lowerCamelCase			)





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   (prior_latents * self.clip_std) + self.clip_mean
       return prior_latents


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			:					int						=			logging.get_logger(__name__)


__A			:					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'),
    ]
)

__A			:					Optional[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'),
    ]
)

__A			:					Tuple						=			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			:					Dict						=			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			:					Any						=			OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

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

__A			:					Union[str, 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'),
    ]
)

__A			:					Optional[int]						=			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			:					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'),
    ]
)

__A			:					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'),
    ]
)

__A			:					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'),
    ]
)

__A			:					Any						=			OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

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

__A			:					List[str]						=			OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

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





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_MAPPING




__A			:					Optional[int]						=			auto_class_update(FlaxAutoModel)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_PRETRAINING_MAPPING




__A			:					Optional[Any]						=			auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




__A			:					Tuple						=			auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MASKED_LM_MAPPING




__A			:					List[Any]						=			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




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





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




__A			:					List[Any]						=			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




__A			:					Any						=			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




__A			:					Union[str, Any]						=			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




__A			:					Optional[Any]						=			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




__A			:					int						=			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')





class 			_SCREAMING_SNAKE_CASE (   _BaseAutoModelClass		):







   '''simple docstring'''




   lowerCamelCase__				=      FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




__A			:					Optional[int]						=			auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)