






'''simple docstring'''







import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

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


UpperCamelCase_					=				datasets.utils.logging.get_logger(__name__)




@dataclass
class 					__UpperCAmelCase (		datasets.BuilderConfig	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[datasets.Features]				       =					None
      SCREAMING_SNAKE_CASE				:    str				       =					"utf-8"
      SCREAMING_SNAKE_CASE				:    Optional[str]				       =					None
      SCREAMING_SNAKE_CASE				:    int				       =					1_0 << 2_0  # 10MB
      SCREAMING_SNAKE_CASE				:    bool				       =					False
      SCREAMING_SNAKE_CASE				:    str				       =					"line"




class 					__UpperCAmelCase (		datasets.ArrowBasedBuilder	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    str				       =					TextConfig




      def 							lowerCamelCase     (					self						):
             return datasets.DatasetInfo(features=self.config.features						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             if not self.config.data_files:
                    raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""						)
             UpperCAmelCase__	:   Tuple												=	dl_manager.download_and_extract(self.config.data_files						)
             if isinstance(_UpperCAmelCase  ,		(str, list, tuple)						):
                    UpperCAmelCase__	:   List[Any]												=	data_files
                    if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						):
                           UpperCAmelCase__	:   List[str]												=	[files]
                    UpperCAmelCase__	:   List[Any]												=	[dl_manager.iter_files(_UpperCAmelCase						) for file in files]
                    return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,		gen_kwargs={'''files''': files}						)]
             UpperCAmelCase__	:   Dict												=	[]
             for split_name, files in data_files.items():
                    if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						):
                           UpperCAmelCase__	:   List[str]												=	[files]
                    UpperCAmelCase__	:   Optional[int]												=	[dl_manager.iter_files(_UpperCAmelCase						) for file in files]
                    splits.append(datasets.SplitGenerator(name=_UpperCAmelCase  ,		gen_kwargs={'''files''': files}						)						)
             return splits




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             if self.config.features is not None:
                    UpperCAmelCase__	:   Optional[int]												=	self.config.features.arrow_schema
                    if all(not require_storage_cast(_UpperCAmelCase						) for feature in self.config.features.values()						):
                           # cheaper cast
                           UpperCAmelCase__	:   Optional[int]												=	pa_table.cast(_UpperCAmelCase						)
                    else:
                           # more expensive cast; allows str <-> int/float or str to Audio for example
                           UpperCAmelCase__	:   Union[str, Any]												=	table_cast(_UpperCAmelCase  ,		_UpperCAmelCase						)
                    return pa_table
             else:
                    return pa_table.cast(pa.schema({'''text''': pa.string()}						)						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             UpperCAmelCase__	:   Any												=	list(self.config.features						) if self.config.features is not None else ['''text''']
             for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase						)						):
                    # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
                    with open(_UpperCAmelCase  ,		encoding=self.config.encoding  ,		errors=self.config.errors						) as f:
                           if self.config.sample_by == "line":
                                  UpperCAmelCase__	:   Optional[Any]												=	0
                                  while True:
                                         UpperCAmelCase__	:   List[str]												=	f.read(self.config.chunksize						)
                                         if not batch:
                                                break
                                         batch += f.readline()  # finish current line
                                         # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                                         UpperCAmelCase__	:   Any												=	StringIO(_UpperCAmelCase						).readlines()
                                         if not self.config.keep_linebreaks:
                                                UpperCAmelCase__	:   int												=	[line.rstrip('''\n'''						) for line in batch]
                                         UpperCAmelCase__	:   Optional[int]												=	pa.Table.from_arrays([pa.array(_UpperCAmelCase						)]  ,		names=_UpperCAmelCase						)
                                         # Uncomment for debugging (will print the Arrow table size and elements)
                                         # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                         # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                         yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase						)
                                         batch_idx += 1
                           elif self.config.sample_by == "paragraph":
                                  UpperCAmelCase__	:   List[Any]												=	0
                                  UpperCAmelCase__	:   Any												=	''''''
                                  while True:
                                         UpperCAmelCase__	:   str												=	f.read(self.config.chunksize						)
                                         if not new_batch:
                                                break
                                         batch += new_batch
                                         batch += f.readline()  # finish current line
                                         UpperCAmelCase__	:   List[str]												=	batch.split('''\n\n'''						)
                                         UpperCAmelCase__	:   int												=	pa.Table.from_arrays(
                                             [pa.array([example for example in batch[:-1] if example]						)]  ,		names=_UpperCAmelCase						)
                                         # Uncomment for debugging (will print the Arrow table size and elements)
                                         # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                         # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                         yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase						)
                                         batch_idx += 1
                                         UpperCAmelCase__	:   Union[str, Any]												=	batch[-1]
                                  if batch:
                                         UpperCAmelCase__	:   List[Any]												=	pa.Table.from_arrays([pa.array([batch]						)]  ,		names=_UpperCAmelCase						)
                                         yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase						)
                           elif self.config.sample_by == "document":
                                  UpperCAmelCase__	:   int												=	f.read()
                                  UpperCAmelCase__	:   Tuple												=	pa.Table.from_arrays([pa.array([text]						)]  ,		names=_UpperCAmelCase						)
                                  yield file_idx, self._cast_table(_UpperCAmelCase						)







'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class 					__UpperCAmelCase (		UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				unittest.TestCase	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					StableDiffusionModelEditingPipeline
      SCREAMING_SNAKE_CASE				:    str				       =					TEXT_TO_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					TEXT_TO_IMAGE_BATCH_PARAMS
      SCREAMING_SNAKE_CASE				:    Dict				       =					TEXT_TO_IMAGE_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					TEXT_TO_IMAGE_IMAGE_PARAMS




      def 							lowerCamelCase     (					self						):
             torch.manual_seed(0						)
             UpperCAmelCase__	:   List[Any]												=	UNetaDConditionModel(
                 block_out_channels=(32, 64)  ,		layers_per_block=2  ,		sample_size=32  ,		in_channels=4  ,		out_channels=4  ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,		cross_attention_dim=32  ,		)
             UpperCAmelCase__	:   List[str]												=	DDIMScheduler()
             torch.manual_seed(0						)
             UpperCAmelCase__	:   str												=	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						)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextConfig(
                 bos_token_id=0  ,		eos_token_id=2  ,		hidden_size=32  ,		intermediate_size=37  ,		layer_norm_eps=1E-05  ,		num_attention_heads=4  ,		num_hidden_layers=5  ,		pad_token_id=1  ,		vocab_size=1000  ,		)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextModel(_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

             UpperCAmelCase__	:   List[str]												=	{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Tuple												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 # Setting height and width to None to prevent OOMs on CPU.
                 '''height''': None,
                 '''width''': None,
                 '''num_inference_steps''': 2,
                 '''guidance_scale''': 6.0,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Optional[int]												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]
             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   int												=	np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[str]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	'''french fries'''
             UpperCAmelCase__	:   Any												=	sd_pipe(**_UpperCAmelCase  ,		negative_prompt=_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	output.images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   str												=	np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   str												=	EulerAncestralDiscreteScheduler(
                 beta_start=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''						)
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[Any]												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   int												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   Tuple												=	np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   str												=	self.get_dummy_components()
             UpperCAmelCase__	:   List[Any]												=	PNDMScheduler()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Tuple												=	self.get_dummy_inputs(_UpperCAmelCase						)
             # the pipeline does not expect pndm so test if it raises error.
             with self.assertRaises(_UpperCAmelCase						):
                    UpperCAmelCase__	:   List[Any]												=	sd_pipe(**_UpperCAmelCase						).images




      def 							lowerCamelCase     (					self						):
             super().test_inference_batch_single_identical(expected_max_diff=5E-3						)




      def 							lowerCamelCase     (					self						):
             super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)




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

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Dict												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   List[str]												=	StableDiffusionModelEditingPipeline.from_pretrained(_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing()

             UpperCAmelCase__	:   Union[str, Any]												=	self.get_inputs()
             UpperCAmelCase__	:   Union[str, Any]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Optional[int]												=	image[0, -3:, -3:, -1].flatten()

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

             UpperCAmelCase__	:   List[str]												=	np.array(
                 [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

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

             # make sure image changes after editing
             pipe.edit_model('''A pack of roses'''  ,		'''A pack of blue roses'''						)

             UpperCAmelCase__	:   Optional[int]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1].flatten()

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

             assert np.abs(expected_slice - image_slice						).max() > 1E-1




      def 							lowerCamelCase     (					self						):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase__	:   Dict												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   int												=	DDIMScheduler.from_pretrained(_UpperCAmelCase  ,		subfolder='''scheduler'''						)
             UpperCAmelCase__	:   int												=	StableDiffusionModelEditingPipeline.from_pretrained(
                 _UpperCAmelCase  ,		scheduler=_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             UpperCAmelCase__	:   Tuple												=	pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing(1						)
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase__	:   Any												=	self.get_inputs()
             UpperCAmelCase__	:   Any												=	pipe(**_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	torch.cuda.max_memory_allocated()
             # make sure that less than 4.4 GB is allocated
             assert mem_bytes < 4.4 * 10**9