



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       _lowercase				(  lowerCAmelCase, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				PaintByExamplePipeline
	__A		      =				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
	__A		      =				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__A		      =				frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						torch.manual_seed(0						)
						a   	=  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						,			)
						a   	=  PNDMScheduler(skip_prk_steps=lowerCamelCase_						)
						torch.manual_seed(0						)
						a   	=  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						)
						a   	=  CLIPVisionConfig(
						    hidden_size=32						,			projection_dim=32						,			intermediate_size=37						,			layer_norm_eps=1E-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			image_size=32						,			patch_size=4						,			)
						a   	=  PaintByExampleImageEncoder(lowerCamelCase_						,			proj_size=32						)
						a   	=  CLIPImageProcessor(crop_size=32						,			size=32						)

						a   	=  {
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "image_encoder": image_encoder,
						    "safety_checker": None,
						    "feature_extractor": feature_extractor,
						}
						return components




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_="cpu"						,			lowerCamelCase_=0						):
						"""simple docstring"""

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

						if str(lowerCamelCase_						).startswith("mps"						):
											a   	=  torch.manual_seed(lowerCamelCase_						)
						else:
											a   	=  torch.Generator(device=lowerCamelCase_						).manual_seed(lowerCamelCase_						)
						a   	=  {
						    "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						):
						"""simple docstring"""

						a   	=  self.get_dummy_components()

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

						a   	=  self.get_dummy_inputs()
						a   	=  pipe(**lowerCamelCase_						)
						a   	=  output.images

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

						assert image.shape == (1, 64, 64, 3)
						a   	=  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						):
						"""simple docstring"""

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

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

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

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

						a   	=  Image.fromarray(np.uinta(lowerCamelCase_						)						).convert("RGB"						)
						a   	=  Image.fromarray(np.uinta(lowerCamelCase_						)						).convert("RGB"						)

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

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




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





	"""simple docstring"""




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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

						a   	=  PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
						a   	=  pipe.to(lowerCamelCase_						)
						pipe.set_progress_bar_config(disable=lowerCamelCase_						)

						a   	=  torch.manual_seed(321						)
						a   	=  pipe(
						    image=lowerCamelCase_						,			mask_image=lowerCamelCase_						,			example_image=lowerCamelCase_						,			generator=lowerCamelCase_						,			guidance_scale=5.0						,			num_inference_steps=50						,			output_type="np"						,			)

						a   	=  output.images

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

						assert image.shape == (1, 512, 512, 3)
						a   	=  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





import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
				import sqlitea

				import sqlalchemy


_lowercase:					Optional[Any]      =						datasets.utils.logging.get_logger(__name__)



@dataclass
class       _lowercase				(  datasets.BuilderConfig      ):





	"""simple docstring"""
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				True
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				10000
	__A		      =				None




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if self.sql is None:
											raise ValueError("sql must be specified"						)
						if self.con is None:
											raise ValueError("con must be specified"						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  config_kwargs.copy()
						# We need to stringify the Selectable object to make its hash deterministic

						# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
						a   	=  config_kwargs["sql"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																import sqlalchemy

																if isinstance(lowerCamelCase_						,			sqlalchemy.sql.Selectable						):
																					a   	=  sqlalchemy.create_engine(config_kwargs["con"].split("://"						)[0] + "://"						)
																					a   	=  str(sql.compile(dialect=engine.dialect						)						)
																					a   	=  sql_str
																else:
																					raise TypeError(
																					    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
											else:
																raise TypeError(
																    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
						a   	=  config_kwargs["con"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											a   	=  id(lowerCamelCase_						)
											logger.info(
											    F'''SQL connection \'con\' of type {type(lowerCamelCase_						)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''						)

						return super().create_config_id(lowerCamelCase_						,			custom_features=lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "index_col": self.index_col,
						    "columns": self.columns,
						    "params": self.params,
						    "coerce_float": self.coerce_float,
						    "parse_dates": self.parse_dates,
						}
						return pd_read_sql_kwargs




class       _lowercase				(  datasets.ArrowBasedBuilder      ):





	"""simple docstring"""
	__A		      =				SqlConfig




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return datasets.DatasetInfo(features=self.config.features						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,			gen_kwargs={}						)]




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if self.config.features is not None:
											a   	=  self.config.features.arrow_schema
											if all(not require_storage_cast(lowerCamelCase_						) for feature in self.config.features.values()						):
																# cheaper cast
																a   	=  pa.Table.from_arrays([pa_table[field.name] for field in schema]						,			schema=lowerCamelCase_						)
											else:
																# more expensive cast; allows str <-> int/float or str to Audio for example
																a   	=  table_cast(lowerCamelCase_						,			lowerCamelCase_						)
						return pa_table




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.config.chunksize
						a   	=  pd.read_sql(
						    self.config.sql						,			self.config.con						,			chunksize=lowerCamelCase_						,			**self.config.pd_read_sql_kwargs						)
						a   	=  [sql_reader] if chunksize is None else sql_reader
						for chunk_idx, df in enumerate(lowerCamelCase_						):
											a   	=  pa.Table.from_pandas(lowerCamelCase_						)
											yield chunk_idx, self._cast_table(lowerCamelCase_						)
