

'''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   lowerCAmelCase     (      A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= PaintByExamplePipeline
       lowerCAmelCase_		= IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       lowerCAmelCase_		= IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       lowerCAmelCase_		= frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            torch.manual_seed(0      )
            __lowercase					=UNetaDConditionModel(
                block_out_channels=(32, 64)						,      layers_per_block=2						,      sample_size=32						,      in_channels=9						,      out_channels=4						,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,      cross_attention_dim=32						,      )
            __lowercase					=PNDMScheduler(skip_prk_steps=__lowercase      )
            torch.manual_seed(0      )
            __lowercase					=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						,      )
            torch.manual_seed(0      )
            __lowercase					=CLIPVisionConfig(
                hidden_size=32						,      projection_dim=32						,      intermediate_size=37						,      layer_norm_eps=1E-05						,      num_attention_heads=4						,      num_hidden_layers=5						,      image_size=32						,      patch_size=4						,      )
            __lowercase					=PaintByExampleImageEncoder(__lowercase						,      proj_size=32      )
            __lowercase					=CLIPImageProcessor(crop_size=32						,      size=32      )

            __lowercase					={
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'image_encoder': image_encoder,
                'safety_checker': None,
                'feature_extractor': feature_extractor,
            }
            return components







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=np.array(image.convert('RGB'      )      )
            __lowercase					=image[None].transpose(0						,      3						,      1						,      2      )
            __lowercase					=torch.from_numpy(__lowercase      ).to(dtype=torch.floataa      ) / 127.5 - 1.0
            return image







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: List[Any]="cpu"						,      __lowercase			: int=0      ):







            """simple docstring"""







            __lowercase					=floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
            __lowercase					=image.cpu().permute(0						,      2						,      3						,      1      )[0]
            __lowercase					=Image.fromarray(np.uinta(__lowercase      )      ).convert('RGB'      ).resize((64, 64)      )
            __lowercase					=Image.fromarray(np.uinta(image + 4      )      ).convert('RGB'      ).resize((64, 64)      )
            __lowercase					=Image.fromarray(np.uinta(__lowercase      )      ).convert('RGB'      ).resize((32, 32)      )

            if str(__lowercase      ).startswith('mps'      ):
                 __lowercase					=torch.manual_seed(__lowercase      )
            else:
                 __lowercase					=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
            __lowercase					={
                '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 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=self.get_dummy_components()

            # make sure here that pndm scheduler skips prk
            __lowercase					=PaintByExamplePipeline(**__lowercase      )
            __lowercase					=pipe.to('cpu'      )
            pipe.set_progress_bar_config(disable=__lowercase      )

            __lowercase					=self.get_dummy_inputs()
            __lowercase					=pipe(**__lowercase      )
            __lowercase					=output.images

            __lowercase					=image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            __lowercase					=np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]      )

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







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					='cpu'
            __lowercase					=self.get_dummy_inputs()
            inputs.pop('mask_image'      )
            __lowercase					=self.convert_to_pt(inputs.pop('image'      )      )
            __lowercase					=image.clamp(0						,      1      ) / 2

            # make sure here that pndm scheduler skips prk
            __lowercase					=PaintByExamplePipeline(**self.get_dummy_components()      )
            __lowercase					=pipe.to(__lowercase      )
            pipe.set_progress_bar_config(disable=__lowercase      )

            __lowercase					=pipe(image=__lowercase						,      mask_image=mask_image[:, 0]						,      **__lowercase      )
            __lowercase					=output.images

            __lowercase					=image.cpu().permute(0						,      2						,      3						,      1      )[0]
            __lowercase					=mask_image.cpu().permute(0						,      2						,      3						,      1      )[0]

            __lowercase					=Image.fromarray(np.uinta(__lowercase      )      ).convert('RGB'      )
            __lowercase					=Image.fromarray(np.uinta(__lowercase      )      ).convert('RGB'      )

            __lowercase					=pipe(**self.get_dummy_inputs()      )
            __lowercase					=output.images

            assert out_a.shape == (1, 64, 64, 3)
            assert np.abs(out_a.flatten() - out_a.flatten()      ).max() < 5E-2







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            __lowercase					=load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/paint_by_example/dog_in_bucket.png'      )
            __lowercase					=load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/paint_by_example/mask.png'      )
            __lowercase					=load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/paint_by_example/panda.jpg'      )

            __lowercase					=PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'      )
            __lowercase					=pipe.to(__lowercase      )
            pipe.set_progress_bar_config(disable=__lowercase      )

            __lowercase					=torch.manual_seed(321      )
            __lowercase					=pipe(
                image=__lowercase						,      mask_image=__lowercase						,      example_image=__lowercase						,      generator=__lowercase						,      guidance_scale=5.0						,      num_inference_steps=50						,      output_type='np'						,      )

            __lowercase					=output.images

            __lowercase					=image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            __lowercase					=np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]      )

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





'''simple docstring'''

import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
       from transformers.models.dpr.modeling_dpr import (
           DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
           DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
           DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


class   lowerCAmelCase     :







       def __init__(							self			: List[str]						,      __lowercase			: List[Any]						,      __lowercase			: Union[str, Any]=13						,      __lowercase			: Optional[int]=7						,      __lowercase			: Union[str, Any]=False						,      __lowercase			: str=True						,      __lowercase			: Dict=True						,      __lowercase			: Optional[Any]=True						,      __lowercase			: int=99						,      __lowercase			: Optional[Any]=32						,      __lowercase			: Optional[int]=5						,      __lowercase			: List[str]=4						,      __lowercase			: Any=37						,      __lowercase			: str="gelu"						,      __lowercase			: str=0.1						,      __lowercase			: str=0.1						,      __lowercase			: str=512						,      __lowercase			: Dict=16						,      __lowercase			: Dict=2						,      __lowercase			: List[str]=0.0_2						,      __lowercase			: Union[str, Any]=3						,      __lowercase			: List[Any]=4						,      __lowercase			: List[str]=None						,      __lowercase			: List[Any]=0						,      ):







            """simple docstring"""







            __lowercase					=parent
            __lowercase					=batch_size
            __lowercase					=seq_length
            __lowercase					=is_training
            __lowercase					=use_input_mask
            __lowercase					=use_token_type_ids
            __lowercase					=use_labels
            __lowercase					=vocab_size
            __lowercase					=hidden_size
            __lowercase					=num_hidden_layers
            __lowercase					=num_attention_heads
            __lowercase					=intermediate_size
            __lowercase					=hidden_act
            __lowercase					=hidden_dropout_prob
            __lowercase					=attention_probs_dropout_prob
            __lowercase					=max_position_embeddings
            __lowercase					=type_vocab_size
            __lowercase					=type_sequence_label_size
            __lowercase					=initializer_range
            __lowercase					=num_labels
            __lowercase					=num_choices
            __lowercase					=scope
            __lowercase					=projection_dim







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size      )

            __lowercase					=None
            if self.use_input_mask:
                 __lowercase					=random_attention_mask([self.batch_size, self.seq_length]      )

            __lowercase					=None
            if self.use_token_type_ids:
                 __lowercase					=ids_tensor([self.batch_size, self.seq_length]						,      self.type_vocab_size      )

            __lowercase					=None
            __lowercase					=None
            __lowercase					=None
            if self.use_labels:
                 __lowercase					=ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )
                 __lowercase					=ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels      )
                 __lowercase					=ids_tensor([self.batch_size]						,      self.num_choices      )

            __lowercase					=self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            return DPRConfig(
                projection_dim=self.projection_dim						,      vocab_size=self.vocab_size						,      hidden_size=self.hidden_size						,      num_hidden_layers=self.num_hidden_layers						,      num_attention_heads=self.num_attention_heads						,      intermediate_size=self.intermediate_size						,      hidden_act=self.hidden_act						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      max_position_embeddings=self.max_position_embeddings						,      type_vocab_size=self.type_vocab_size						,      initializer_range=self.initializer_range						,      )







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: int						,      __lowercase			: Tuple						,      __lowercase			: Any						,      __lowercase			: Union[str, Any]						,      __lowercase			: List[Any]						,      __lowercase			: Tuple						,      __lowercase			: List[str]      ):







            """simple docstring"""







            __lowercase					=DPRContextEncoder(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase						,      attention_mask=__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.pooler_output.shape						,      (self.batch_size, self.projection_dim or self.hidden_size)      )







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: Optional[int]						,      __lowercase			: Optional[int]						,      __lowercase			: Union[str, Any]						,      __lowercase			: Any						,      __lowercase			: Optional[Any]						,      __lowercase			: Tuple						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=DPRQuestionEncoder(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase						,      attention_mask=__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase						,      token_type_ids=__lowercase      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.pooler_output.shape						,      (self.batch_size, self.projection_dim or self.hidden_size)      )







       def 			snake_case   (							self			: List[str]						,      __lowercase			: Optional[Any]						,      __lowercase			: Tuple						,      __lowercase			: str						,      __lowercase			: Tuple						,      __lowercase			: str						,      __lowercase			: List[Any]						,      __lowercase			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=DPRReader(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(
                __lowercase						,      attention_mask=__lowercase						,      )

            self.parent.assertEqual(result.start_logits.shape						,      (self.batch_size, self.seq_length)      )
            self.parent.assertEqual(result.end_logits.shape						,      (self.batch_size, self.seq_length)      )
            self.parent.assertEqual(result.relevance_logits.shape						,      (self.batch_size,)      )







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=self.prepare_config_and_inputs()
            (
                (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     
            )					=config_and_inputs
            __lowercase					={'input_ids': input_ids}
            return config, inputs_dict






@require_torch
class   lowerCAmelCase     (      A   ,			A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= (
           (
               DPRContextEncoder,
               DPRQuestionEncoder,
               DPRReader,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_		= {"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}

       lowerCAmelCase_		= False
       lowerCAmelCase_		= False  # why?
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=DPRModelTester(self      )
            __lowercase					=ConfigTester(self						,      config_class=__lowercase						,      hidden_size=37      )







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_context_encoder(*__lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_question_encoder(*__lowercase      )







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_reader(*__lowercase      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()[0]

            __lowercase					=DPRQuestionEncoder(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 model.save_pretrained(__lowercase      )
                 __lowercase					=DPRQuestionEncoder.from_pretrained(__lowercase						,      projection_dim=512      )

            self.assertIsNotNone(__lowercase      )







       @slow
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=DPRContextEncoder.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )

            for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=DPRContextEncoder.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )

            for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=DPRQuestionEncoder.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )

            for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=DPRReader.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )






@require_torch
class   lowerCAmelCase     (      unittest.TestCase   ):







       @slow
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'						,      return_dict=__lowercase      )
            model.to(__lowercase      )

            __lowercase					=torch.tensor(
                [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]						,      dtype=torch.long						,      device=__lowercase      )  # [CLS] hello, is my dog cute? [SEP]
            __lowercase					=model(__lowercase      )[0]  # embedding shape = (1, 768)
            # compare the actual values for a slice.
            __lowercase					=torch.tensor(
                [
                    [
                        0.0_3_2_3_6_2_5_3,
                        0.1_2_7_5_3_3_3_5,
                        0.1_6_8_1_8_5_0_9,
                        0.0_0_2_7_9_7_8_6,
                        0.3_8_9_6_9_3_3,
                        0.2_4_2_6_4_9_4_5,
                        0.2_1_7_8_9_7_1,
                        -0.0_2_3_3_5_2_2_7,
                        -0.0_8_4_8_1_9_5_9,
                        -0.1_4_3_2_4_1_1_7,
                    ]
                ]						,      dtype=torch.float						,      device=__lowercase						,      )
            self.assertTrue(torch.allclose(output[:, :10]						,      __lowercase						,      atol=1E-4      )      )







       @slow
       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					=DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base'      )
            __lowercase					=DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base'      )
            model.to(__lowercase      )

            __lowercase					=tokenizer(
                questions='What is love ?'						,      titles='Haddaway'						,      texts='What Is Love is a song recorded by the artist Haddaway'						,      padding=__lowercase						,      return_tensors='pt'						,      )
            encoded_inputs.to(__lowercase      )

            __lowercase					=model(**__lowercase      )

            # compare the actual values for a slice.
            __lowercase					=torch.tensor(
                [[-1_0.3_0_0_5, -1_0.7_7_6_5, -1_1.4_8_7_2, -1_1.6_8_4_1, -1_1.9_3_1_2, -1_0.3_0_0_2, -9.8_5_4_4, -1_1.7_3_7_8, -1_2.0_8_2_1, -1_0.2_9_7_5]]						,      dtype=torch.float						,      device=__lowercase						,      )

            __lowercase					=torch.tensor(
                [[-1_1.0_6_8_4, -1_1.7_0_4_1, -1_1.5_3_9_7, -1_0.3_4_6_5, -1_0.8_7_9_1, -6.8_4_4_3, -1_1.9_9_5_9, -1_1.0_3_6_4, -1_0.0_0_9_6, -6.8_4_0_5]]						,      dtype=torch.float						,      device=__lowercase						,      )
            self.assertTrue(torch.allclose(outputs.start_logits[:, :10]						,      __lowercase						,      atol=1E-4      )      )
            self.assertTrue(torch.allclose(outputs.end_logits[:, :10]						,      __lowercase						,      atol=1E-4      )      )


