

from collections import OrderedDict

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


lowerCAmelCase__		      =       logging.get_logger(__name__)


lowerCAmelCase__		      =       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"""),
    ]
)

lowerCAmelCase__		      =       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"""),
    ]
)

lowerCAmelCase__		      =       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"""),
    ]
)

lowerCAmelCase__		      =       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"""),
    ]
)

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

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

lowerCAmelCase__		      =       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"""),
    ]
)

lowerCAmelCase__		      =       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"""),
    ]
)

lowerCAmelCase__		      =       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"""),
    ]
)

lowerCAmelCase__		      =       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"""),
    ]
)

lowerCAmelCase__		      =       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"""),
    ]
)

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

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

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

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_MAPPING

lowerCAmelCase__		      =       auto_class_update(FlaxAutoModel)

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_PRETRAINING_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_MASKED_LM_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

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

class     a__  (    _BaseAutoModelClass	):
			"""simple docstring"""







			__lowerCamelCase       				=				FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

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






"""simple docstring"""

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Any  ,     lowercase_		:       TransformeraDModel  ,     lowercase_		:       AutoencoderKL  ,     lowercase_		:       KarrasDiffusionSchedulers  ,     lowercase_		:       Optional[Dict[int, str]] = None  ,     ):
     super().__init__()
     self.register_modules(transformer=lowercase_  ,     vae=lowercase_  ,     scheduler=lowercase_				)

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

     for l in label:
         if l not in self.labels:
             raise ValueError(
                 f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."				)

     return [self.labels[l] for l in label]






 @torch.no_grad()
 def __call__(     self		:       Optional[int]  ,     lowercase_		:       List[int]  ,     lowercase_		:       float = 4.0  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       int = 50  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	len(lowercase_				)
     snake_case_ :     List[str]				=	self.transformer.config.sample_size
     snake_case_ :     Union[str, Any]				=	self.transformer.config.in_channels

     snake_case_ :     str				=	randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)  ,     generator=lowercase_  ,     device=self.device  ,     dtype=self.transformer.dtype  ,     )
     snake_case_ :     Optional[Any]				=	torch.cat([latents] * 2				) if guidance_scale > 1 else latents

     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_  ,     device=self.device				).reshape(-1				)
     snake_case_ :     Dict				=	torch.tensor([1000] * batch_size  ,     device=self.device				)
     snake_case_ :     Tuple				=	torch.cat([class_labels, class_null]  ,     0				) if guidance_scale > 1 else class_labels

     # set step values
     self.scheduler.set_timesteps(lowercase_				)

     for t in self.progress_bar(self.scheduler.timesteps				):
         if guidance_scale > 1:
             snake_case_ :     List[Any]				=	latent_model_input[: len(lowercase_				) // 2]
             snake_case_ :     Union[str, Any]				=	torch.cat([half, half]  ,     dim=0				)
         snake_case_ :     Optional[Any]				=	self.scheduler.scale_model_input(lowercase_  ,     lowercase_				)

         snake_case_ :     int				=	t
         if not torch.is_tensor(lowercase_				):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             snake_case_ :     Tuple				=	latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_  ,     lowercase_				):
                 snake_case_ :     List[str]				=	torch.floataa if is_mps else torch.floataa
             else:
                 snake_case_ :     Optional[int]				=	torch.intaa if is_mps else torch.intaa
             snake_case_ :     List[Any]				=	torch.tensor([timesteps]  ,     dtype=lowercase_  ,     device=latent_model_input.device				)
         elif len(timesteps.shape				) == 0:
             snake_case_ :     str				=	timesteps[None].to(latent_model_input.device				)
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         snake_case_ :     Tuple				=	timesteps.expand(latent_model_input.shape[0]				)
         # predict noise model_output
         snake_case_ :     List[Any]				=	self.transformer(
             lowercase_  ,     timestep=lowercase_  ,     class_labels=lowercase_				).sample

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

             snake_case_ :     int				=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             snake_case_ :     str				=	torch.cat([half_eps, half_eps]  ,     dim=0				)

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

     if guidance_scale > 1:
         snake_case_,	snake_case_ :     Optional[Any]				=	latent_model_input.chunk(2  ,     dim=0				)
     else:
         snake_case_ :     Dict				=	latent_model_input

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     str				=	(samples / 2 + 0.5).clamp(0  ,     1				)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     snake_case_ :     Union[str, Any]				=	samples.cpu().permute(0  ,     2  ,     3  ,     1				).float().numpy()

     if output_type == "pil":
         snake_case_ :     Union[str, Any]				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





