import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   TextToVideoSDPipeline
    __a						=   TEXT_TO_IMAGE_PARAMS
    __a						=   TEXT_TO_IMAGE_BATCH_PARAMS
    # No `output_type`.
    __a						=   frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback",
            "callback_steps",
        ]     )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: List[str]=   UNetaDConditionModel(
         block_out_channels=(32, 64, 64, 64)			,			layers_per_block=2			,			sample_size=32			,			in_channels=4			,			out_channels=4			,			down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')			,			up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')			,			cross_attention_dim=32			,			attention_head_dim=4			,			)
     SCREAMING_SNAKE_CASE__: Optional[int]=   DDIMScheduler(
         beta_start=0.00085			,			beta_end=0.012			,			beta_schedule='''scaled_linear'''			,			clip_sample=lowerCAmelCase			,			set_alpha_to_one=lowerCAmelCase			,			)
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: 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			,			sample_size=128			,			)
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: str=   CLIPTextConfig(
         bos_token_id=0			,			eos_token_id=2			,			hidden_size=32			,			intermediate_size=37			,			layer_norm_eps=1e-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1000			,			hidden_act='''gelu'''			,			projection_dim=512			,			)
     SCREAMING_SNAKE_CASE__: Dict=   CLIPTextModel(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: int=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

     SCREAMING_SNAKE_CASE__: Tuple=   {
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
     }
     return components
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase=0 )							->       Dict:
     if str(lowerCAmelCase ).startswith('''mps''' ):
      SCREAMING_SNAKE_CASE__: Any=   torch.manual_seed(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: Any=   torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   {
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 6.0,
         '''output_type''': '''pt''',
     }
     return inputs
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: str=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__: List[Any]=   self.get_dummy_components()
     SCREAMING_SNAKE_CASE__: List[Any]=   TextToVideoSDPipeline(**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   sd_pipe.to(lowerCAmelCase )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Dict=   self.get_dummy_inputs(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Any=   '''np'''
     SCREAMING_SNAKE_CASE__: Optional[int]=   sd_pipe(**lowerCAmelCase ).frames
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   frames[0][-3:, -3:, -1]

     assert frames[0].shape == (64, 64, 3)
     SCREAMING_SNAKE_CASE__: Any=   np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
    def     UpperCamelCase_					(	self )							->       int:
     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCAmelCase			,			expected_max_diff=3e-3 )
    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()			,			reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,			)
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase			,			expected_max_diff=1e-2 )
    @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
    def     UpperCamelCase_					(	self )							->       List[str]:
     pass
    @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     pass
    @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
    def     UpperCamelCase_					(	self )							->       Dict:
     pass



    def     UpperCamelCase_					(	self )							->       Dict:
     return super().test_progress_bar()






@slow
@skip_mps
class 		_lowerCamelCase		( unittest.TestCase     ):
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[int]=   load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''' )

     SCREAMING_SNAKE_CASE__: Any=   TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
     SCREAMING_SNAKE_CASE__: int=   pipe.to('''cuda''' )

     SCREAMING_SNAKE_CASE__: List[str]=   '''Spiderman is surfing'''
     SCREAMING_SNAKE_CASE__: Tuple=   torch.Generator(device='''cpu''' ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__: Optional[int]=   pipe(lowerCAmelCase			,			generator=lowerCAmelCase			,			num_inference_steps=25			,			output_type='''pt''' ).frames
     SCREAMING_SNAKE_CASE__: List[str]=   video_frames.cpu().numpy()

     assert np.abs(expected_video - video ).mean() < 5e-2



    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: List[str]=   load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''' )

     SCREAMING_SNAKE_CASE__: List[Any]=   TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
     SCREAMING_SNAKE_CASE__: Any=   pipe.to('''cuda''' )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   '''Spiderman is surfing'''
     SCREAMING_SNAKE_CASE__: Tuple=   torch.Generator(device='''cpu''' ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__: Dict=   pipe(lowerCAmelCase			,			generator=lowerCAmelCase			,			num_inference_steps=2			,			output_type='''pt''' ).frames
     SCREAMING_SNAKE_CASE__: Dict=   video_frames.cpu().numpy()

     assert np.abs(expected_video - video ).mean() < 5e-2









from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)



