




'''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


UpperCamelCase_		:     Any          =		logging.get_logger(__name__)


UpperCamelCase_		:     Tuple          =		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'''),
    ]
)

UpperCamelCase_		:     Optional[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'''),
    ]
)

UpperCamelCase_		:     Dict          =		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'''),
    ]
)

UpperCamelCase_		:     Optional[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'''),
    ]
)

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

UpperCamelCase_		:     int          =		OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

UpperCamelCase_		:     Optional[int]          =		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'''),
    ]
)

UpperCamelCase_		:     str          =		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'''),
    ]
)

UpperCamelCase_		:     int          =		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'''),
    ]
)

UpperCamelCase_		:     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'''),
    ]
)

UpperCamelCase_		:     int          =		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'''),
    ]
)

UpperCamelCase_		:     str          =		OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

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

UpperCamelCase_		:     int          =		OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

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

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		FLAX_MODEL_MAPPING







UpperCamelCase_		:     str          =		auto_class_update(FlaxAutoModel)

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		FLAX_MODEL_FOR_PRETRAINING_MAPPING







UpperCamelCase_		:     Optional[Any]          =		auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		FLAX_MODEL_FOR_CAUSAL_LM_MAPPING







UpperCamelCase_		:     str          =		auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       Dict       							=		FLAX_MODEL_FOR_MASKED_LM_MAPPING







UpperCamelCase_		:     List[Any]          =		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







UpperCamelCase_		:     List[str]          =		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       str       							=		FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







UpperCamelCase_		:     List[str]          =		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       str       							=		FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING







UpperCamelCase_		:     Tuple          =		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING







UpperCamelCase_		:     Union[str, Any]          =		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING







UpperCamelCase_		:     Any          =		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING







UpperCamelCase_		:     Union[str, Any]          =		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







UpperCamelCase_		:     int          =		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       Dict       							=		FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING







UpperCamelCase_		:     List[Any]          =		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')

class   _a							(		_BaseAutoModelClass				):
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING







UpperCamelCase_		:     int          =		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase    :		Any		      =    False


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						pass


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(snake_case	)
																SCREAMING_SNAKE_CASE							:     Any						=				VersatileDiffusionPipeline.from_pretrained(snake_case       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				generator.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=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    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cyberpunk 2077'
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pipe.dual_guided(
											    prompt=snake_case       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'       ,	).images

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

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

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

											SCREAMING_SNAKE_CASE							:     Any						=				'A painting of a squirrel eating a burger '
											SCREAMING_SNAKE_CASE							:     Any						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				pipe.text_to_image(
											    prompt=snake_case       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'	).images

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

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe.image_variation(snake_case       ,	generator=snake_case       ,	output_type='numpy'	).images

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

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

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