
'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_            =    {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase_            =    ["""CamembertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase_            =    ["""CamembertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase_            =    [
					    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CamembertForCausalLM""",
					    """CamembertForMaskedLM""",
					    """CamembertForMultipleChoice""",
					    """CamembertForQuestionAnswering""",
					    """CamembertForSequenceClassification""",
					    """CamembertForTokenClassification""",
					    """CamembertModel""",
					    """CamembertPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase_            =    [
					    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFCamembertForCausalLM""",
					    """TFCamembertForMaskedLM""",
					    """TFCamembertForMultipleChoice""",
					    """TFCamembertForQuestionAnswering""",
					    """TFCamembertForSequenceClassification""",
					    """TFCamembertForTokenClassification""",
					    """TFCamembertModel""",
					    """TFCamembertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase_            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




'''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


