
'''simple docstring'''

import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax
					import jax.numpy as jnp
					import numpy as np

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
					import torch


@require_flax
class 					a_					:




							def __init__(       self				,				snake_case_				,				snake_case_=1_4				,				snake_case_=7				,				snake_case_=True				,				snake_case_=True				,				snake_case_=True				,				snake_case_=9_9				,				snake_case_=3_2				,				snake_case_=5				,				snake_case_=4				,				snake_case_=3_7				,				snake_case_="gelu"				,				snake_case_=0.1				,				snake_case_=0.1				,				snake_case_=5_1_2				,				snake_case_=0.02				,				snake_case_=None				,				):
									_lowerCAmelCase    :					Any							=   parent
									_lowerCAmelCase    :					int							=   batch_size
									_lowerCAmelCase    :					List[str]							=   seq_length
									_lowerCAmelCase    :					Tuple							=   is_training
									_lowerCAmelCase    :					int							=   use_input_mask
									_lowerCAmelCase    :					str							=   use_labels
									_lowerCAmelCase    :					Tuple							=   vocab_size
									_lowerCAmelCase    :					Optional[Any]							=   d_model
									_lowerCAmelCase    :					int							=   num_hidden_layers
									_lowerCAmelCase    :					List[str]							=   num_attention_heads
									_lowerCAmelCase    :					Any							=   ffn_dim
									_lowerCAmelCase    :					List[Any]							=   activation_function
									_lowerCAmelCase    :					int							=   activation_dropout
									_lowerCAmelCase    :					List[Any]							=   attention_dropout
									_lowerCAmelCase    :					Dict							=   max_position_embeddings
									_lowerCAmelCase    :					int							=   initializer_range
									_lowerCAmelCase    :					List[str]							=   None
									_lowerCAmelCase    :					List[str]							=   0
									_lowerCAmelCase    :					Optional[int]							=   2
									_lowerCAmelCase    :					int							=   1




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Optional[Any]							=   np.clip(ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size					)				,				3				,				self.vocab_size					)

									_lowerCAmelCase    :					Union[str, Any]							=   None
									if self.use_input_mask:
											_lowerCAmelCase    :					List[str]							=   random_attention_mask([self.batch_size, self.seq_length]					)

									_lowerCAmelCase    :					Optional[int]							=   XGLMConfig(
									    vocab_size=self.vocab_size				,				d_model=self.hidden_size				,				num_layers=self.num_hidden_layers				,				attention_heads=self.num_attention_heads				,				ffn_dim=self.ffn_dim				,				activation_function=self.activation_function				,				activation_dropout=self.activation_dropout				,				attention_dropout=self.attention_dropout				,				max_position_embeddings=self.max_position_embeddings				,				initializer_range=self.initializer_range				,				use_cache=snake_case_				,				bos_token_id=self.bos_token_id				,				eos_token_id=self.eos_token_id				,				pad_token_id=self.pad_token_id				,				)

									return (config, input_ids, input_mask)




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Optional[Any]							=   self.prepare_config_and_inputs()
									_lowerCAmelCase    :					str							=   config_and_inputs
									_lowerCAmelCase    :					Dict							=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					int							=   self.prepare_config_and_inputs()

									_lowerCAmelCase    :					Union[str, Any]							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
									_lowerCAmelCase    :					str							=   ids_tensor([self.batch_size, self.seq_length]				,				vocab_size=2					)

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
									_lowerCAmelCase    :					str							=   2_0
									_lowerCAmelCase    :					Optional[Any]							=   model_class_name(snake_case_					)

									_lowerCAmelCase    :					Dict							=   model.init_cache(input_ids.shape[0]				,				snake_case_					)
									_lowerCAmelCase    :					int							=   jnp.ones((input_ids.shape[0], max_decoder_length)				,				dtype="""i4"""					)

									_lowerCAmelCase    :					List[Any]							=   jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]				,				(input_ids.shape[0], input_ids.shape[-1] - 1)					)
									_lowerCAmelCase    :					List[str]							=   model(
									    input_ids[:, :-1]				,				attention_mask=snake_case_				,				past_key_values=snake_case_				,				position_ids=snake_case_				,				)

									_lowerCAmelCase    :					Optional[Any]							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,				dtype="""i4"""					)
									_lowerCAmelCase    :					Tuple							=   model(
									    input_ids[:, -1:]				,				attention_mask=snake_case_				,				past_key_values=outputs_cache.past_key_values				,				position_ids=snake_case_				,				)

									_lowerCAmelCase    :					List[str]							=   model(snake_case_					)

									_lowerCAmelCase    :					Optional[int]							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1E-3				,				msg=f'Max diff is {diff}'					)







							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
									_lowerCAmelCase    :					List[Any]							=   2_0
									_lowerCAmelCase    :					Optional[Any]							=   model_class_name(snake_case_					)

									_lowerCAmelCase    :					Tuple							=   jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])					)]				,				axis=-1				,				)

									_lowerCAmelCase    :					int							=   model.init_cache(input_ids.shape[0]				,				snake_case_					)
									_lowerCAmelCase    :					Any							=   jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1					)[None, :]				,				(input_ids.shape[0], input_ids.shape[-1] - 1)					)

									_lowerCAmelCase    :					Any							=   model(
									    input_ids[:, :-1]				,				attention_mask=snake_case_				,				past_key_values=snake_case_				,				position_ids=snake_case_				,				)
									_lowerCAmelCase    :					str							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,				dtype="""i4"""					)
									_lowerCAmelCase    :					List[str]							=   model(
									    input_ids[:, -1:]				,				past_key_values=outputs_cache.past_key_values				,				attention_mask=snake_case_				,				position_ids=snake_case_				,				)

									_lowerCAmelCase    :					Tuple							=   model(snake_case_				,				attention_mask=snake_case_					)
									_lowerCAmelCase    :					Tuple							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
									self.parent.assertTrue(diff < 1E-3				,				msg=f'Max diff is {diff}'					)



@require_sentencepiece
@require_flax
class 					a_					(_a							,     _a							,     unittest.TestCase      ):
							__lowerCAmelCase      :			Optional[Any]      = (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
							__lowerCAmelCase      :			Tuple      = (FlaxXGLMForCausalLM,) if is_flax_available() else ()




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Union[str, Any]							=   FlaxXGLMModelTester(self					)




							def 							__UpperCamelCase    (       self					):
									for model_class_name in self.all_model_classes:
											_lowerCAmelCase    :					List[Any]							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					)




							def 							__UpperCamelCase    (       self					):
									for model_class_name in self.all_model_classes:
											_lowerCAmelCase    :					str							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					)




							@slow
							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Optional[int]							=   XGLMTokenizer.from_pretrained("""XGLM"""				,				padding_side="""left"""					)
									_lowerCAmelCase    :					Any							=   tokenizer(["""Hello this is a long string""", """Hey"""]				,				return_tensors="""np"""				,				padding=snake_case_				,				truncation=snake_case_					)

									_lowerCAmelCase    :					List[str]							=   FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""					)
									_lowerCAmelCase    :					List[Any]							=   1
									_lowerCAmelCase    :					Any							=   False

									_lowerCAmelCase    :					List[str]							=   jax.jit(model.generate					)

									_lowerCAmelCase    :					Any							=   jit_generate(inputs["""input_ids"""]				,				attention_mask=inputs["""attention_mask"""]					).sequences

									_lowerCAmelCase    :					Tuple							=   tokenizer.batch_decode(snake_case_				,				skip_special_tokens=snake_case_					)

									_lowerCAmelCase    :					Optional[int]							=   [
									    """Hello this is a long string of questions, but I'm not sure if I'm""",
									    """Hey, I'm a newbie to the forum and I'""",
									]

									self.assertListEqual(snake_case_				,				snake_case_					)




							@is_pt_flax_cross_test
							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Optional[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__					):
													# prepare inputs
													_lowerCAmelCase    :					Union[str, Any]							=   self._prepare_for_class(snake_case_				,				snake_case_					)
													_lowerCAmelCase    :					Tuple							=   {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													_lowerCAmelCase    :					Optional[Any]							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
													_lowerCAmelCase    :					Dict							=   getattr(snake_case_				,				snake_case_					)

													_lowerCAmelCase    :					Tuple							=   pt_inputs["""input_ids"""].shape
													_lowerCAmelCase    :					Tuple							=   np.random.randint(0				,				seq_length - 1				,				size=(batch_size,)					)
													for batch_idx, start_index in enumerate(snake_case_					):
															_lowerCAmelCase    :					Dict							=   0
															_lowerCAmelCase    :					Optional[int]							=   1
															_lowerCAmelCase    :					List[str]							=   0
															_lowerCAmelCase    :					List[str]							=   1
													_lowerCAmelCase    :					Any							=   pt_model_class(snake_case_					).eval()
													# Flax models don't use the `use_cache` option and cache is not returned as a default.
													# So we disable `use_cache` here for PyTorch model.
													_lowerCAmelCase    :					Any							=   False
													_lowerCAmelCase    :					Optional[Any]							=   model_class(snake_case_				,				dtype=jnp.floataa					)
													_lowerCAmelCase    :					Dict							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,				snake_case_					)
													_lowerCAmelCase    :					str							=   fx_state

													with torch.no_grad():
															_lowerCAmelCase    :					Tuple							=   pt_model(**snake_case_					).to_tuple()

													_lowerCAmelCase    :					str							=   fx_model(**snake_case_					).to_tuple()
													self.assertEqual(len(snake_case_					)				,				len(snake_case_					)				,				"""Output lengths differ between Flax and PyTorch"""					)
													for fx_output, pt_output in zip(snake_case_				,				snake_case_					):
															self.assert_almost_equals(fx_output[:, -1]				,				pt_output[:, -1].numpy()				,				4E-2					)

													with tempfile.TemporaryDirectory() as tmpdirname:
															pt_model.save_pretrained(snake_case_					)
															_lowerCAmelCase    :					Union[str, Any]							=   model_class.from_pretrained(snake_case_				,				from_pt=snake_case_					)

													_lowerCAmelCase    :					int							=   fx_model_loaded(**snake_case_					).to_tuple()
													self.assertEqual(
													    len(snake_case_					)				,				len(snake_case_					)				,				"""Output lengths differ between Flax and PyTorch"""					)
													for fx_output_loaded, pt_output in zip(snake_case_				,				snake_case_					):
															self.assert_almost_equals(fx_output_loaded[:, -1]				,				pt_output[:, -1].numpy()				,				4E-2					)




							@is_pt_flax_cross_test
							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Optional[int]							=   self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__					):
													# prepare inputs
													_lowerCAmelCase    :					List[Any]							=   self._prepare_for_class(snake_case_				,				snake_case_					)
													_lowerCAmelCase    :					List[Any]							=   {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													_lowerCAmelCase    :					int							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
													_lowerCAmelCase    :					Dict							=   getattr(snake_case_				,				snake_case_					)

													_lowerCAmelCase    :					List[str]							=   pt_model_class(snake_case_					).eval()
													_lowerCAmelCase    :					List[Any]							=   False
													_lowerCAmelCase    :					Tuple							=   model_class(snake_case_				,				dtype=jnp.floataa					)

													_lowerCAmelCase    :					str							=   load_flax_weights_in_pytorch_model(snake_case_				,				fx_model.params					)
													_lowerCAmelCase    :					List[Any]							=   pt_inputs["""input_ids"""].shape
													_lowerCAmelCase    :					int							=   np.random.randint(0				,				seq_length - 1				,				size=(batch_size,)					)
													for batch_idx, start_index in enumerate(snake_case_					):
															_lowerCAmelCase    :					Tuple							=   0
															_lowerCAmelCase    :					Optional[int]							=   1
															_lowerCAmelCase    :					Optional[int]							=   0
															_lowerCAmelCase    :					Union[str, Any]							=   1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
															_lowerCAmelCase    :					int							=   pt_model(**snake_case_					).to_tuple()

													_lowerCAmelCase    :					Union[str, Any]							=   fx_model(**snake_case_					).to_tuple()
													self.assertEqual(len(snake_case_					)				,				len(snake_case_					)				,				"""Output lengths differ between Flax and PyTorch"""					)
													for fx_output, pt_output in zip(snake_case_				,				snake_case_					):
															self.assert_almost_equals(fx_output[:, -1]				,				pt_output[:, -1].numpy()				,				4E-2					)

													with tempfile.TemporaryDirectory() as tmpdirname:
															fx_model.save_pretrained(snake_case_					)
															_lowerCAmelCase    :					int							=   pt_model_class.from_pretrained(snake_case_				,				from_flax=snake_case_					)

													with torch.no_grad():
															_lowerCAmelCase    :					List[str]							=   pt_model_loaded(**snake_case_					).to_tuple()

													self.assertEqual(
													    len(snake_case_					)				,				len(snake_case_					)				,				"""Output lengths differ between Flax and PyTorch"""					)
													for fx_output, pt_output in zip(snake_case_				,				snake_case_					):
															self.assert_almost_equals(fx_output[:, -1]				,				pt_output[:, -1].numpy()				,				4E-2					)







							@slow
							def 							__UpperCamelCase    (       self					):
									for model_class_name in self.all_model_classes:
											_lowerCAmelCase    :					Dict							=   model_class_name.from_pretrained("""facebook/xglm-564M"""					)
											_lowerCAmelCase    :					int							=   model(np.ones((1, 1)					)					)
											self.assertIsNotNone(snake_case_					)




'''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


