




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								=       logging.get_logger(__name__)


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

__A								=       OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

__A								=       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								=       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								=       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								=       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								=       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								=       OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

__A								=       OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

__A								=       OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

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







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_MAPPING




__A								=       auto_class_update(FlaxAutoModel)







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_PRETRAINING_MAPPING




__A								=       auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




__A								=       auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_MASKED_LM_MAPPING




__A								=       auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




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







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




__A								=       auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




__A								=       auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




__A								=       auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




__A								=       auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




__A								=       auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




__A								=       auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




__A								=       auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")







class        __lowerCAmelCase					(			_BaseAutoModelClass					):

			"""simple docstring"""







			snake_case_  						=			FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




__A								=       auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)







"""simple docstring"""

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						:    List[Any]				=      argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A_						:    Tuple				=      parser.parse_args()


A_						:    Optional[int]				=      'cpu'
A_						:    Dict				=      'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A_						:    List[str]				=      'path-to-your-trained-model'
A_						:    Any				=      StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
							A_						:    Tuple				=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A_						:    Tuple				=      pipe.to(device)

# to channels last
A_						:    List[str]				=      pipe.unet.to(memory_format=torch.channels_last)
A_						:    List[str]				=      pipe.vae.to(memory_format=torch.channels_last)
A_						:    Optional[int]				=      pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
							A_						:    Union[str, Any]				=      pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A_						:    List[Any]				=      torch.randn(2, 4, 64, 64)
A_						:    int				=      torch.rand(1) * 999
A_						:    int				=      torch.randn(2, 77, 768)
A_						:    Tuple				=      (sample, timestep, encoder_hidden_status)
try:
							A_						:    Any				=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
							A_						:    List[str]				=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A_						:    str				=      ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A_						:    Optional[int]				=      ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
							A_						:    List[str]				=      ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A_						:    Union[str, Any]				=      666
A_						:    Optional[Any]				=      torch.Generator(device).manual_seed(seed)
A_						:    Union[str, Any]				=      {'generator': generator}
if args.steps is not None:
							A_						:    int				=      args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
							A_						:    int				=      pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')





