
'''simple docstring'''

from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase_            =    [
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

UpperCamelCase_            =    get_logger(__name__)


UpperCamelCase_            =    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def 		_UpperCAmelCase			(					_lowerCamelCase  :    dict						)      ->  Optional[TaskTemplate]:
		_lowerCAmelCase    :					str							=   task_template_dict.get("""task"""						)
		if task_name is None:
				logger.warning(f'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_lowerCamelCase						)}'						)
				return None
		_lowerCAmelCase    :					Optional[Any]							=   NAMEaTEMPLATE.get(_lowerCamelCase						)
		return template.from_dict(_lowerCamelCase						)




'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Union[str, Any]      = PaintByExamplePipeline
       __lowerCAmelCase      :			int      = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       __lowerCAmelCase      :			str      = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __lowerCAmelCase      :			Any      = frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




       def 							__UpperCamelCase    (       self					):
         torch.manual_seed(0					)
         _lowerCAmelCase    :					Dict							=   UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)				,				layers_per_block=2				,				sample_size=3_2				,				in_channels=9				,				out_channels=4				,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,				cross_attention_dim=3_2				,				)
         _lowerCAmelCase    :					List[Any]							=   PNDMScheduler(skip_prk_steps=snake_case_					)
         torch.manual_seed(0					)
         _lowerCAmelCase    :					List[Any]							=   AutoencoderKL(
             block_out_channels=[3_2, 6_4]				,				in_channels=3				,				out_channels=3				,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,				latent_channels=4				,				)
         torch.manual_seed(0					)
         _lowerCAmelCase    :					List[str]							=   CLIPVisionConfig(
             hidden_size=3_2				,				projection_dim=3_2				,				intermediate_size=3_7				,				layer_norm_eps=1E-05				,				num_attention_heads=4				,				num_hidden_layers=5				,				image_size=3_2				,				patch_size=4				,				)
         _lowerCAmelCase    :					Optional[int]							=   PaintByExampleImageEncoder(snake_case_				,				proj_size=3_2					)
         _lowerCAmelCase    :					Optional[int]							=   CLIPImageProcessor(crop_size=3_2				,				size=3_2					)

         _lowerCAmelCase    :					Union[str, Any]							=   {
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """image_encoder""": image_encoder,
             """safety_checker""": None,
             """feature_extractor""": feature_extractor,
         }
         return components




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					Optional[int]							=   np.array(image.convert("""RGB"""					)					)
         _lowerCAmelCase    :					Any							=   image[None].transpose(0				,				3				,				1				,				2					)
         _lowerCAmelCase    :					List[str]							=   torch.from_numpy(snake_case_					).to(dtype=torch.floataa					) / 127.5 - 1.0
         return image




       def 							__UpperCamelCase    (       self				,				snake_case_="cpu"				,				snake_case_=0					):
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         _lowerCAmelCase    :					Tuple							=   floats_tensor((1, 3, 3_2, 3_2)				,				rng=random.Random(snake_case_					)					).to(snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   image.cpu().permute(0				,				2				,				3				,				1					)[0]
         _lowerCAmelCase    :					List[Any]							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					).resize((6_4, 6_4)					)
         _lowerCAmelCase    :					List[str]							=   Image.fromarray(np.uinta(image + 4					)					).convert("""RGB"""					).resize((6_4, 6_4)					)
         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					).resize((3_2, 3_2)					)

         if str(snake_case_					).startswith("""mps"""					):
           _lowerCAmelCase    :					str							=   torch.manual_seed(snake_case_					)
         else:
           _lowerCAmelCase    :					Tuple							=   torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
         _lowerCAmelCase    :					str							=   {
             """example_image""": example_image,
             """image""": init_image,
             """mask_image""": mask_image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.get_dummy_components()

         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					Optional[Any]							=   PaintByExamplePipeline(**snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   pipe.to("""cpu"""					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         _lowerCAmelCase    :					List[str]							=   pipe(**snake_case_					)
         _lowerCAmelCase    :					Any							=   output.images

         _lowerCAmelCase    :					Optional[int]							=   image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         _lowerCAmelCase    :					Optional[Any]							=   np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]					)

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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   """cpu"""
         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         inputs.pop("""mask_image"""					)
         _lowerCAmelCase    :					Optional[int]							=   self.convert_to_pt(inputs.pop("""image"""					)					)
         _lowerCAmelCase    :					int							=   image.clamp(0				,				1					) / 2

         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					List[Any]							=   PaintByExamplePipeline(**self.get_dummy_components()					)
         _lowerCAmelCase    :					Optional[int]							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   pipe(image=snake_case_				,				mask_image=mask_image[:, 0]				,				**snake_case_					)
         _lowerCAmelCase    :					str							=   output.images

         _lowerCAmelCase    :					List[str]							=   image.cpu().permute(0				,				2				,				3				,				1					)[0]
         _lowerCAmelCase    :					List[str]							=   mask_image.cpu().permute(0				,				2				,				3				,				1					)[0]

         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)
         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)

         _lowerCAmelCase    :					str							=   pipe(**self.get_dummy_inputs()					)
         _lowerCAmelCase    :					int							=   output.images

         assert out_a.shape == (1, 6_4, 6_4, 3)
         assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5E-2







       def 							__UpperCamelCase    (       self					):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



@slow
@require_torch_gpu
class 					a_					(unittest.TestCase      ):




       def 							__UpperCamelCase    (       self					):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







       def 							__UpperCamelCase    (       self					):
         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					Union[str, Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/dog_in_bucket.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/mask.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/panda.jpg"""					)

         _lowerCAmelCase    :					str							=   PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""					)
         _lowerCAmelCase    :					int							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   torch.manual_seed(3_2_1					)
         _lowerCAmelCase    :					Optional[int]							=   pipe(
             image=snake_case_				,				mask_image=snake_case_				,				example_image=snake_case_				,				generator=snake_case_				,				guidance_scale=5.0				,				num_inference_steps=5_0				,				output_type="""np"""				,				)

         _lowerCAmelCase    :					List[str]							=   output.images

         _lowerCAmelCase    :					List[str]							=   image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase    :					Any							=   np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]					)

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


