
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   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_MAPPING


__lowerCamelCase         =			auto_class_update(FlaxAutoModel)


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_PRETRAINING_MAPPING


__lowerCamelCase         =			auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


__lowerCamelCase         =			auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_MASKED_LM_MAPPING


__lowerCamelCase         =			auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')


class   snake_case_ (_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   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


__lowerCamelCase         =			auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


__lowerCamelCase         =			auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


__lowerCamelCase         =			auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


__lowerCamelCase         =			auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


__lowerCamelCase         =			auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


__lowerCamelCase         =			auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)


class   snake_case_ (_BaseAutoModelClass  ):




       """simple docstring"""






       _lowerCamelCase			    =				FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


__lowerCamelCase         =			auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')


class   snake_case_ (_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'''
)





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				AltDiffusionPipeline
       _lowerCamelCase			    =				TEXT_TO_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_BATCH_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS



       def  A_   (     self):

             """simple docstring"""





             torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[Any]				      =		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     ,)
             UpperCAmelCase_  :	Any				      =		DDIMScheduler(
                 beta_start=0.0_0085     ,beta_end=0.012     ,beta_schedule="scaled_linear"     ,clip_sample=lowercase     ,set_alpha_to_one=lowercase     ,)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		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     ,)

             # TODO: address the non-deterministic text encoder (fails for save-load tests)
             # torch.manual_seed(0)
             # text_encoder_config = RobertaSeriesConfig(
             #     hidden_size=32,
             #     project_dim=32,
             #     intermediate_size=37,
             #     layer_norm_eps=1e-05,
             #     num_attention_heads=4,
             #     num_hidden_layers=5,
             #     vocab_size=5002,
             # )
             # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

             torch.manual_seed(0)
             UpperCAmelCase_  :	Tuple				      =		CLIPTextConfig(
                 bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,projection_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=5002     ,)
             UpperCAmelCase_  :	Dict				      =		CLIPTextModel(lowercase)

             UpperCAmelCase_  :	List[Any]				      =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
             UpperCAmelCase_  :	List[str]				      =		77

             UpperCAmelCase_  :	Union[str, Any]				      =		{
                 "unet": unet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
             return components



       def  A_   (     self     ,lowercase     ,lowercase=0):

             """simple docstring"""





             if str(lowercase).startswith("mps"):
                   UpperCAmelCase_  :	Any				      =		torch.manual_seed(lowercase)
             else:
                   UpperCAmelCase_  :	Dict				      =		torch.Generator(device=lowercase).manual_seed(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		{
                 "prompt": "A painting of a squirrel eating a burger",
                 "generator": generator,
                 "num_inference_steps": 2,
                 "guidance_scale": 6.0,
                 "output_type": "numpy",
             }
             return inputs



       def  A_   (     self):

             """simple docstring"""





             super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)



       def  A_   (     self):

             """simple docstring"""





             super().test_inference_batch_single_identical(expected_max_diff=3E-3)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	Any				      =		self.get_dummy_components()
             torch.manual_seed(0)
             UpperCAmelCase_  :	List[Any]				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Any				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		text_encoder

             UpperCAmelCase_  :	List[Any]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Tuple				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		"A photo of an astronaut"
             UpperCAmelCase_  :	str				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	List[str]				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Optional[Any]				      =		np.array(
                 [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	int				      =		self.get_dummy_components()
             UpperCAmelCase_  :	Optional[Any]				      =		PNDMScheduler(skip_prk_steps=lowercase)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Union[str, Any]				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Dict				      =		text_encoder
             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	List[Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	int				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Tuple				      =		np.array(
                 [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

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


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




       """simple docstring"""






       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,safety_checker=lowercase)
             UpperCAmelCase_  :	Any				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Any				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Any				      =		torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[int]				      =		alt_pipe([prompt]     ,generator=lowercase     ,guidance_scale=6.0     ,num_inference_steps=20     ,output_type="np")

             UpperCAmelCase_  :	Dict				      =		output.images

             UpperCAmelCase_  :	List[str]				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	Dict				      =		np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		DDIMScheduler.from_pretrained("BAAI/AltDiffusion"     ,subfolder="scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,scheduler=lowercase     ,safety_checker=lowercase)
             UpperCAmelCase_  :	List[str]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Optional[Any]				      =		torch.manual_seed(0)

             UpperCAmelCase_  :	List[str]				      =		alt_pipe([prompt]     ,generator=lowercase     ,num_inference_steps=2     ,output_type="numpy")
             UpperCAmelCase_  :	int				      =		output.images

             UpperCAmelCase_  :	str				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	List[Any]				      =		np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

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