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






"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase     =							logging.get_logger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	None:
				warnings.warn(
				    "The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
				    " use DeiTImageProcessor instead."						,	SCREAMING_SNAKE_CASE__						,	)
				super().__init__(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)




