



'''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 __snake_case(				_lowerCAmelCase		,							unittest.TestCase ):


      '''simple docstring'''



      UpperCAmelCase							:   Tuple									=		VideoToVideoSDPipeline
      UpperCAmelCase							:   str									=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"} ) - {"image", "width", "height"}
      UpperCAmelCase							:   int									=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"} ) - {"image"}
      UpperCAmelCase							:   str									=		PipelineTesterMixin.required_optional_params - {"latents"}
      UpperCAmelCase							:   Any									=		False

      # No `output_type`.
      UpperCAmelCase							:   List[str]									=		frozenset(
          [
              "num_inference_steps",
              "generator",
              "latents",
              "return_dict",
              "callback",
              "callback_steps",
          ] )


      def     __snake_case   ( self				)     ->     Any:
       torch.manual_seed(0				)
       lowerCAmelCase   =     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     ,	)
       lowerCAmelCase   =     DDIMScheduler(
           beta_start=0.0_0_0_8_5     ,	beta_end=0.0_1_2     ,	beta_schedule="""scaled_linear"""     ,	clip_sample=lowerCamelCase_     ,	set_alpha_to_one=lowerCamelCase_     ,	)
       torch.manual_seed(0				)
       lowerCAmelCase   =     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				)
       lowerCAmelCase   =     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     ,	)
       lowerCAmelCase   =     CLIPTextModel(lowerCamelCase_				)
       lowerCAmelCase   =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

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


      def     __snake_case   ( self     ,	A_     ,	A_=0				)     ->     Optional[Any]:
       # 3 frames
       lowerCAmelCase   =     floats_tensor((1, 3, 3, 32, 32)     ,	rng=random.Random(lowerCamelCase_				)				).to(lowerCamelCase_				)

       if str(lowerCamelCase_				).startswith("""mps"""				):
        lowerCAmelCase   =     torch.manual_seed(lowerCamelCase_				)
       else:
        lowerCAmelCase   =     torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
       lowerCAmelCase   =     {
           """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     __snake_case   ( self				)     ->     Any:
       lowerCAmelCase   =     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       lowerCAmelCase   =     self.get_dummy_components()
       lowerCAmelCase   =     VideoToVideoSDPipeline(**lowerCamelCase_				)
       lowerCAmelCase   =     sd_pipe.to(lowerCamelCase_				)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

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

       assert frames[0].shape == (32, 32, 3)
       lowerCAmelCase   =     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     __snake_case   ( self				)     ->     List[str]:
       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase_     ,	expected_max_diff=5e-3				)


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


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


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




      def     __snake_case   ( self				)     ->     Optional[Any]:
       return super().test_progress_bar()







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


      '''simple docstring'''



      def     __snake_case   ( self				)     ->     List[str]:
       lowerCAmelCase   =     VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL"""     ,	torch_dtype=torch.floataa				)
       pipe.enable_model_cpu_offload()

       # 10 frames
       lowerCAmelCase   =     torch.Generator(device="""cpu"""				).manual_seed(0				)
       lowerCAmelCase   =     torch.randn((1, 10, 3, 1024, 576)     ,	generator=lowerCamelCase_				)
       lowerCAmelCase   =     video.to("""cuda"""				)

       lowerCAmelCase   =     """Spiderman is surfing"""

       lowerCAmelCase   =     pipe(lowerCamelCase_     ,	video=lowerCamelCase_     ,	generator=lowerCamelCase_     ,	num_inference_steps=3     ,	output_type="""pt"""				).frames

       lowerCAmelCase   =     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'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)