
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		: Dict								=     logging.get_logger(__name__)


A		: Dict								=     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		: 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'''),
    ]
)

A		: List[Any]								=     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		: 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'''),
    ]
)

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

A		: Dict								=     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		: Dict								=     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		: 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'''),
    ]
)

A		: Optional[int]								=     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		: List[str]								=     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		: Optional[Any]								=     OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

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

A		: Dict								=     OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

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



class    a_ (			_BaseAutoModelClass    ):
			a	:			str					   =							FLAX_MODEL_MAPPING







A		: Any								=     auto_class_update(FlaxAutoModel)



class    a_ (			_BaseAutoModelClass    ):
			a	:			Tuple					   =							FLAX_MODEL_FOR_PRETRAINING_MAPPING







A		: List[str]								=     auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')



class    a_ (			_BaseAutoModelClass    ):
			a	:			List[str]					   =							FLAX_MODEL_FOR_CAUSAL_LM_MAPPING







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



class    a_ (			_BaseAutoModelClass    ):
			a	:			List[Any]					   =							FLAX_MODEL_FOR_MASKED_LM_MAPPING







A		: Optional[Any]								=     auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')



class    a_ (			_BaseAutoModelClass    ):
			a	:			Optional[Any]					   =							FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







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



class    a_ (			_BaseAutoModelClass    ):
			a	:			Any					   =							FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







A		: str								=     auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)



class    a_ (			_BaseAutoModelClass    ):
			a	:			List[str]					   =							FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING







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



class    a_ (			_BaseAutoModelClass    ):
			a	:			Optional[Any]					   =							FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING







A		: int								=     auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)



class    a_ (			_BaseAutoModelClass    ):
			a	:			Any					   =							FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING







A		: List[Any]								=     auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')



class    a_ (			_BaseAutoModelClass    ):
			a	:			str					   =							FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING







A		: Dict								=     auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)



class    a_ (			_BaseAutoModelClass    ):
			a	:			Union[str, Any]					   =							FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







A		: str								=     auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)



class    a_ (			_BaseAutoModelClass    ):
			a	:			List[str]					   =							FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING







A		: Optional[int]								=     auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')



class    a_ (			_BaseAutoModelClass    ):
			a	:			int					   =							FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING







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

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

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class    a_ (			_a    ):
			a	:			Union[List[PIL.Image.Image], np.ndarray]
			a	:			Optional[List[bool]]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_cycle_diffusion import CycleDiffusionPipeline
		from .pipeline_stable_diffusion import StableDiffusionPipeline
		from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
		from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
		from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
		from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
		from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
		from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
		from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
		from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
		from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
		from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
		from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
		from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
		from .pipeline_stable_unclip import StableUnCLIPPipeline
		from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
		from .safety_checker import StableDiffusionSafetyChecker
		from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
		from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    StableDiffusionDepthaImgPipeline,
		    StableDiffusionDiffEditPipeline,
		    StableDiffusionPixaPixZeroPipeline,
		)
else:
		from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
		from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
		from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
		if not (
		    is_torch_available()
		    and is_transformers_available()
		    and is_k_diffusion_available()
		    and is_k_diffusion_version('''>=''', '''0.0.12''')
		):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
		from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
		if not (is_transformers_available() and is_onnx_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_onnx_objects import *  # noqa F403
else:
		from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
		from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
		from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
		from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
		from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
		import flax



		@flax.struct.dataclass
		class    a_ (			_a    ):
					a	:			np.ndarray
					a	:			List[bool]







		from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
		from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
		from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
		from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
		from .safety_checker_flax import FlaxStableDiffusionSafetyChecker