
"""simple docstring"""





import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



@skip_mps
class __lowerCAmelCase				(					__a     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	     =				VideoToVideoSDPipeline
			snake_case	     =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}      ) - {"image", "width", "height"}
			snake_case	     =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}      ) - {"image"}
			snake_case	     =				PipelineTesterMixin.required_optional_params - {"latents"}
			snake_case	     =				False

			# No `output_type`.
			snake_case	     =				frozenset(
			    [
			        "num_inference_steps",
			        "generator",
			        "latents",
			        "return_dict",
			        "callback",
			        "callback_steps",
			    ]      )




			def      lowerCamelCase__     (				self						:					str	)      -> Dict:







								"""simple docstring"""
								torch.manual_seed(0	)
								A_				 =						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_				 =						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_				 =						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_				 =						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=1_000       ,   hidden_act="gelu"       ,   projection_dim=512       ,   )
								A_				 =						CLIPTextModel(a_	)
								A_				 =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Tuple       ,   _snake_case						:					List[Any]=0	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						floats_tensor((1, 3, 3, 32, 32)       ,   rng=random.Random(a_	)	).to(a_	)

								if str(a_	).startswith("mps"	):
													A_				 =						torch.manual_seed(a_	)
								else:
													A_				 =						torch.Generator(device=a_	).manual_seed(a_	)
								A_				 =						{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """video""": video,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """pt""",
								}
								return inputs




			def      lowerCamelCase__     (				self						:					str	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								A_				 =						self.get_dummy_components()
								A_				 =						VideoToVideoSDPipeline(**a_	)
								A_				 =						sd_pipe.to(a_	)
								sd_pipe.set_progress_bar_config(disable=a_	)

								A_				 =						self.get_dummy_inputs(a_	)
								A_				 =						"""np"""
								A_				 =						sd_pipe(**a_	).frames
								A_				 =						frames[0][-3:, -3:, -1]

								assert frames[0].shape == (32, 32, 3)
								A_				 =						np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]	)

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




			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()       ,   reason="XFormers attention is only available with CUDA and `xformers` installed"       ,   )
			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a_       ,   expected_max_diff=5e-3	)




			@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."	)
			def      lowerCamelCase__     (				self						:					Any	)      -> Dict:







								"""simple docstring"""
								pass




			@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline."	)
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Union[str, Any]:







								"""simple docstring"""
								pass




			@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline."	)
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								pass






			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								return super().test_progress_bar()





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




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Dict:







								"""simple docstring"""
								A_				 =						VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL"       ,   torch_dtype=torch.floataa	)
								pipe.enable_model_cpu_offload()

								# 10 frames
								A_				 =						torch.Generator(device="cpu"	).manual_seed(0	)
								A_				 =						torch.randn((1, 10, 3, 1_024, 576)       ,   generator=a_	)
								A_				 =						video.to("cuda"	)

								A_				 =						"""Spiderman is surfing"""

								A_				 =						pipe(a_       ,   video=a_       ,   generator=a_       ,   num_inference_steps=3       ,   output_type="pt"	).frames

								A_				 =						np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]	)
								assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array	).sum() < 1e-2






"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_					:						str				= {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_					:						Union[str, Any]				= ['''TimmBackbone''']


if TYPE_CHECKING:
		from .configuration_timm_backbone import TimmBackboneConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_timm_backbone import TimmBackbone

else:
		import sys

		UpperCamelCase_					:						List[Any]				= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




