

import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A_ :				Optional[Any]						 =    logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__=768    ):
						super().__init__(A__    )
						A__							:					Tuple       =	proj_size

						A__							:					int       =	CLIPVisionModel(A__    )
						A__							:					Union[str, Any]       =	PaintByExampleMapper(A__    )
						A__							:					Any       =	nn.LayerNorm(config.hidden_size    )
						A__							:					List[str]       =	nn.Linear(config.hidden_size					, self.proj_size    )

						# uncondition for scaling
						A__							:					str       =	nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )






					def 						__A    (				self					, A__					, A__=False    ):
						A__							:					Optional[int]       =	self.model(pixel_values=A__    )
						A__							:					Dict       =	clip_output.pooler_output
						A__							:					Union[str, Any]       =	self.mapper(latent_states[:, None]    )
						A__							:					Optional[Any]       =	self.final_layer_norm(A__    )
						A__							:					Any       =	self.proj_out(A__    )
						if return_uncond_vector:
							return latent_states, self.uncond_vector

						return latent_states



class 					_a		(nn.Module       ):


					'''simple docstring'''






					def __init__(				self					, A__    ):
						super().__init__()
						A__							:					Optional[int]       =	(config.num_hidden_layers + 1) // 5
						A__							:					List[Any]       =	config.hidden_size
						A__							:					Optional[int]       =	1
						A__							:					Tuple       =	nn.ModuleList(
						    [
						        BasicTransformerBlock(A__					, A__					, A__					, activation_fn="""gelu"""					, attention_bias=A__    )
						        for _ in range(A__    )
						    ]    )






					def 						__A    (				self					, A__    ):
						for block in self.blocks:
							A__							:					str       =	block(A__    )

						return hidden_states






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 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= TextToVideoSDPipeline
					UpperCAmelCase__:     Any													= TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__:     Optional[Any]													= TEXT_TO_IMAGE_BATCH_PARAMS
					# No `output_type`.
					UpperCAmelCase__:     Optional[int]													= frozenset(
					    [
					        '''num_inference_steps''',
					        '''generator''',
					        '''latents''',
					        '''return_dict''',
					        '''callback''',
					        '''callback_steps''',
					    ]       )






					def 						__A    (				self    ):
						torch.manual_seed(0    )
						A__							:					Optional[int]       =	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					, )
						A__							:					Optional[int]       =	DDIMScheduler(
						    beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule="""scaled_linear"""					, clip_sample=A__					, set_alpha_to_one=A__					, )
						torch.manual_seed(0    )
						A__							:					Union[str, 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					, sample_size=128					, )
						torch.manual_seed(0    )
						A__							:					Union[str, Any]       =	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					, )
						A__							:					Union[str, Any]       =	CLIPTextModel(A__    )
						A__							:					Tuple       =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

						A__							:					Dict       =	{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						}
						return components






					def 						__A    (				self					, A__					, A__=0    ):
						if str(A__    ).startswith("""mps"""    ):
							A__							:					Tuple       =	torch.manual_seed(A__    )
						else:
							A__							:					List[str]       =	torch.Generator(device=A__    ).manual_seed(A__    )
						A__							:					List[str]       =	{
						    """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 						__A    (				self    ):
						A__							:					List[str]       =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__							:					Union[str, Any]       =	self.get_dummy_components()
						A__							:					Union[str, Any]       =	TextToVideoSDPipeline(**A__    )
						A__							:					int       =	sd_pipe.to(A__    )
						sd_pipe.set_progress_bar_config(disable=A__    )

						A__							:					int       =	self.get_dummy_inputs(A__    )
						A__							:					int       =	"""np"""
						A__							:					Any       =	sd_pipe(**A__    ).frames
						A__							:					Dict       =	frames[0][-3:, -3:, -1]

						assert frames[0].shape == (64, 64, 3)
						A__							:					Optional[Any]       =	np.array([1_5_8.0, 1_6_0.0, 1_5_3.0, 1_2_5.0, 1_0_0.0, 1_2_1.0, 1_1_1.0, 9_3.0, 1_1_3.0]    )

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






					def 						__A    (				self    ):
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A__					, 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 						__A    (				self    ):
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A__					, expected_max_diff=1e-2    )






					@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""    )
					def 						__A    (				self    ):
						pass






					@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""    )
					def 						__A    (				self    ):
						pass






					@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""    )
					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						return super().test_progress_bar()



@slow
@skip_mps
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""    )

						A__							:					Tuple       =	TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""    )
						A__							:					Any       =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
						A__							:					int       =	pipe.to("""cuda"""    )

						A__							:					Optional[Any]       =	"""Spiderman is surfing"""
						A__							:					List[str]       =	torch.Generator(device="""cpu"""    ).manual_seed(0    )

						A__							:					Optional[Any]       =	pipe(A__					, generator=A__					, num_inference_steps=25					, output_type="""pt"""    ).frames
						A__							:					Dict       =	video_frames.cpu().numpy()

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






					def 						__A    (				self    ):
						A__							:					List[Any]       =	load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""    )

						A__							:					Optional[int]       =	TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""    )
						A__							:					List[str]       =	pipe.to("""cuda"""    )

						A__							:					Dict       =	"""Spiderman is surfing"""
						A__							:					Union[str, Any]       =	torch.Generator(device="""cpu"""    ).manual_seed(0    )

						A__							:					Optional[int]       =	pipe(A__					, generator=A__					, num_inference_steps=2					, output_type="""pt"""    ).frames
						A__							:					Optional[int]       =	video_frames.cpu().numpy()

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



