

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					@property
					def   __A      (			self      ):
						A__							:   int					       =		1
						A__							:   Optional[int]					       =		3
						A__							:   Any					       =		(32, 32)

						A__							:   Tuple					       =		floats_tensor((batch_size, num_channels) + sizes   ,	rng=random.Random(0      )      ).to(A__      )
						return image






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   str					       =		UNetaDConditionModel(
						    block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,	cross_attention_dim=32   ,	)
						return model






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   int					       =		AutoencoderKL(
						    block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,	latent_channels=4   ,	)
						return model






					@property
					def   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   Optional[int]					       =		RobertaSeriesConfig(
						    hidden_size=32   ,	project_dim=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=5006   ,	)
						return RobertaSeriesModelWithTransformation(A__      )






					@property
					def   __A      (			self      ):
						def extract(*A__   ,	**A__      ):



							class    _a						:


												'''simple docstring'''






												def __init__(			self      ):
													A__							:   Optional[int]					       =		torch.ones([0]      )






												def   __A      (			self   ,	A__      ):
													self.pixel_values.to(A__      )
													return self



							return Out()

						return extract






					def   __A      (			self      ):
						A__							:   Any					       =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__							:   int					       =		self.dummy_cond_unet
						A__							:   Tuple					       =		PNDMScheduler(skip_prk_steps=A__      )
						A__							:   Tuple					       =		self.dummy_vae
						A__							:   List[Any]					       =		self.dummy_text_encoder
						A__							:   List[Any]					       =		XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""      )
						A__							:   List[Any]					       =		77

						A__							:   str					       =		self.dummy_image.to(A__      )
						A__							:   Union[str, Any]					       =		init_image / 2 + 0.5

						# make sure here that pndm scheduler skips prk
						A__							:   Dict					       =		AltDiffusionImgaImgPipeline(
						    unet=A__   ,	scheduler=A__   ,	vae=A__   ,	text_encoder=A__   ,	tokenizer=A__   ,	safety_checker=A__   ,	feature_extractor=self.dummy_extractor   ,	)
						A__							:   Tuple					       =		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,	do_normalize=A__      )
						A__							:   int					       =		alt_pipe.to(A__      )
						alt_pipe.set_progress_bar_config(disable=A__      )

						A__							:   Tuple					       =		"""A painting of a squirrel eating a burger"""
						A__							:   Any					       =		torch.Generator(device=A__      ).manual_seed(0      )
						A__							:   Dict					       =		alt_pipe(
						    [prompt]   ,	generator=A__   ,	guidance_scale=6.0   ,	num_inference_steps=2   ,	output_type="""np"""   ,	image=A__   ,	)

						A__							:   str					       =		output.images

						A__							:   List[Any]					       =		torch.Generator(device=A__      ).manual_seed(0      )
						A__							:   Optional[Any]					       =		alt_pipe(
						    [prompt]   ,	generator=A__   ,	guidance_scale=6.0   ,	num_inference_steps=2   ,	output_type="""np"""   ,	image=A__   ,	return_dict=A__   ,	)[0]

						A__							:   int					       =		image[0, -3:, -3:, -1]
						A__							:   str					       =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						A__							:   List[Any]					       =		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]      )

						assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 5e-3






					@unittest.skipIf(torch_device != """cuda"""   ,	"""This test requires a GPU"""      )
					def   __A      (			self      ):
						A__							:   int					       =		self.dummy_cond_unet
						A__							:   Dict					       =		PNDMScheduler(skip_prk_steps=A__      )
						A__							:   Any					       =		self.dummy_vae
						A__							:   Any					       =		self.dummy_text_encoder
						A__							:   Optional[Any]					       =		XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""      )
						A__							:   Optional[int]					       =		77

						A__							:   int					       =		self.dummy_image.to(A__      )

						# put models in fp16
						A__							:   Optional[int]					       =		unet.half()
						A__							:   Union[str, Any]					       =		vae.half()
						A__							:   Dict					       =		bert.half()

						# make sure here that pndm scheduler skips prk
						A__							:   Optional[Any]					       =		AltDiffusionImgaImgPipeline(
						    unet=A__   ,	scheduler=A__   ,	vae=A__   ,	text_encoder=A__   ,	tokenizer=A__   ,	safety_checker=A__   ,	feature_extractor=self.dummy_extractor   ,	)
						A__							:   Optional[Any]					       =		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,	do_normalize=A__      )
						A__							:   List[Any]					       =		alt_pipe.to(A__      )
						alt_pipe.set_progress_bar_config(disable=A__      )

						A__							:   Any					       =		"""A painting of a squirrel eating a burger"""
						A__							:   Tuple					       =		torch.manual_seed(0      )
						A__							:   Dict					       =		alt_pipe(
						    [prompt]   ,	generator=A__   ,	num_inference_steps=2   ,	output_type="""np"""   ,	image=A__   ,	).images

						assert image.shape == (1, 32, 32, 3)






					@unittest.skipIf(torch_device != """cuda"""   ,	"""This test requires a GPU"""      )
					def   __A      (			self      ):
						A__							:   int					       =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""      )
						# resize to resolution that is divisible by 8 but not 16 or 32
						A__							:   Tuple					       =		init_image.resize((760, 504)      )

						A__							:   List[str]					       =		"""BAAI/AltDiffusion"""
						A__							:   Optional[Any]					       =		AltDiffusionImgaImgPipeline.from_pretrained(
						    A__   ,	safety_checker=A__   ,	)
						pipe.to(A__      )
						pipe.set_progress_bar_config(disable=A__      )
						pipe.enable_attention_slicing()

						A__							:   Any					       =		"""A fantasy landscape, trending on artstation"""

						A__							:   Dict					       =		torch.manual_seed(0      )
						A__							:   Union[str, Any]					       =		pipe(
						    prompt=A__   ,	image=A__   ,	strength=0.7_5   ,	guidance_scale=7.5   ,	generator=A__   ,	output_type="""np"""   ,	)
						A__							:   Any					       =		output.images[0]

						A__							:   List[str]					       =		image[255:258, 383:386, -1]

						assert image.shape == (504, 760, 3)
						A__							:   Optional[Any]					       =		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]      )

						assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2



@slow
@require_torch_gpu
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def   __A      (			self      ):
						A__							:   Dict					       =		load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/img2img/sketch-mountains-input.jpg"""      )
						A__							:   int					       =		init_image.resize((768, 512)      )
						A__							:   List[str]					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""      )

						A__							:   List[str]					       =		"""BAAI/AltDiffusion"""
						A__							:   List[Any]					       =		AltDiffusionImgaImgPipeline.from_pretrained(
						    A__   ,	safety_checker=A__   ,	)
						pipe.to(A__      )
						pipe.set_progress_bar_config(disable=A__      )
						pipe.enable_attention_slicing()

						A__							:   Optional[Any]					       =		"""A fantasy landscape, trending on artstation"""

						A__							:   Any					       =		torch.manual_seed(0      )
						A__							:   Optional[Any]					       =		pipe(
						    prompt=A__   ,	image=A__   ,	strength=0.7_5   ,	guidance_scale=7.5   ,	generator=A__   ,	output_type="""np"""   ,	)
						A__							:   List[Any]					       =		output.images[0]

						assert image.shape == (512, 768, 3)
						# img2img is flaky across GPUs even in fp32, so using MAE here
						assert np.abs(expected_image - image      ).max() < 1e-2






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_	:			Union[str, Any]  	=      logging.get_logger(__name__)


A_	:			int  	=      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_	:			Tuple  	=      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_	:			Any  	=      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_	:			Union[str, Any]  	=      OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

A_	:			Tuple  	=      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_	:			Any  	=      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_	:			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_	:			List[str]  	=      OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

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

A_	:			Optional[Any]  	=      OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

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



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	FLAX_MODEL_MAPPING



A_	:			Any  	=      auto_class_update(FlaxAutoModel)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	FLAX_MODEL_FOR_PRETRAINING_MAPPING



A_	:			Union[str, Any]  	=      auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



A_	:			Union[str, Any]  	=      auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	FLAX_MODEL_FOR_MASKED_LM_MAPPING



A_	:			Tuple  	=      auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	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    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



A_	:			Tuple  	=      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



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



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Tuple  							=	FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



A_	:			Dict  	=      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



A_	:			Union[str, Any]  	=      auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



A_	:			Optional[Any]  	=      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       str  							=	FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



A_	:			List[Any]  	=      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



A_	:			Union[str, Any]  	=      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')



class    _a						(_BaseAutoModelClass    ):


					'''simple docstring'''






					UpperCAmelCase__:       int  							=	FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



A_	:			List[str]  	=      auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)



