



"""simple docstring"""

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_						:    Tuple				=      logging.get_logger(__name__)


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

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

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

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

A_						:    Union[str, Any]				=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
A_						:    Tuple				=      _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_						:    Union[str, Any]				=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
A_						:    List[Any]				=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
A_						:    List[str]				=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
A_						:    str				=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
A_						:    Dict				=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
A_						:    Dict				=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
A_						:    Optional[Any]				=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
A_						:    Optional[Any]				=      _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_						:    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     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= FLAX_MODEL_MAPPING




A_						:    int				=      auto_class_update(FlaxAutoModel)
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= FLAX_MODEL_FOR_PRETRAINING_MAPPING




A_						:    Dict				=      auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




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


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		str  						= FLAX_MODEL_FOR_MASKED_LM_MAPPING




A_						:    List[Any]				=      auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Any  						= FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




A_						:    Union[str, Any]				=      auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Any  						= FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




A_						:    Dict				=      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[str]  						= FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




A_						:    Union[str, Any]				=      auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




A_						:    Optional[int]				=      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




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


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




A_						:    str				=      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Tuple  						= FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




A_						:    Tuple				=      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




A_						:    Tuple				=      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')
class     lowerCAmelCase__						(	_BaseAutoModelClass ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




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










"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A_						:    Union[str, Any]				=      False
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	pass




@nightly
@require_torch_gpu
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[str]:





					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'    )
					# remove text_unet
					pipe.remove_unused_weights()
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							int							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Dict							=     VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     generator.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=2     ,       output_type='numpy'    ).images

					assert np.abs(image - new_image    ).sum() < 1E-5, "Models don't have the same forward pass"







	def 			_lowerCAmelCase     (      self				:       Dict    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     VersatileDiffusionTextToImagePipeline.from_pretrained(
					    'shi-labs/versatile-diffusion'     ,       torch_dtype=torch.floataa    )
					pipe.to(_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     'A painting of a squirrel eating a burger '
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.manual_seed(0    )
					SCREAMING_SNAKE_CASE						:							Any							=     pipe(
					    prompt=_SCREAMING_SNAKE_CASE     ,       generator=_SCREAMING_SNAKE_CASE     ,       guidance_scale=7.5     ,       num_inference_steps=50     ,       output_type='numpy'    ).images

					SCREAMING_SNAKE_CASE						:							Tuple							=     image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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





