
"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class 						a__		( a_,     a_,     unittest.TestCase		):
   __lowerCAmelCase				=							IFPipeline
   __lowerCAmelCase				=							TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
   __lowerCAmelCase				=							TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase				=							PipelineTesterMixin.required_optional_params - {"""latents"""}
   def      __magic_name__     (     self							):
          return self._get_dummy_components()
   def      __magic_name__     (     self      ,       _a      ,       _a=0							):
          if str(_a							).startswith("mps"							):
                 lowercase       :							List[str]									=				torch.manual_seed(_a							)
          else:
                 lowercase       :							Dict									=				torch.Generator(device=_a							).manual_seed(_a							)

          lowercase       :							str									=				{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs
   def      __magic_name__     (     self							):
          self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != "cuda"      ,       reason="float16 requires CUDA"							)
   def      __magic_name__     (     self							):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1E-1							)
   def      __magic_name__     (     self							):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)
   def      __magic_name__     (     self							):
          self._test_save_load_local()
   def      __magic_name__     (     self							):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2      ,       )

   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()      ,       reason="XFormers attention is only available with CUDA and `xformers` installed"      ,       )
   def      __magic_name__     (     self							):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


@slow
@require_torch_gpu
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
   def      __magic_name__     (     self							):
          # if

          lowercase       :							Tuple									=				IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"      ,       variant="fp16"      ,       torch_dtype=torch.floataa							)

          lowercase       :							List[str]									=				IFSuperResolutionPipeline.from_pretrained(
              "DeepFloyd/IF-II-L-v1.0"      ,       variant="fp16"      ,       torch_dtype=torch.floataa      ,       text_encoder=_a      ,       tokenizer=_a							)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to("cuda"							)

          lowercase						,       lowercase       :							int									=				pipe_a.encode_prompt("anime turtle"      ,       device="cuda"							)

          del pipe_a.tokenizer
          del pipe_a.text_encoder
          gc.collect()

          lowercase       :							List[str]									=				None
          lowercase       :							Union[str, Any]									=				None

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          lowercase       :							Optional[int]									=				IFImgaImgPipeline(**pipe_a.components							)
          lowercase       :							Dict									=				IFImgaImgSuperResolutionPipeline(**pipe_a.components							)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if_imgaimg(_a      ,       _a      ,       _a      ,       _a							)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          lowercase       :							List[str]									=				IFInpaintingPipeline(**pipe_a.components							)
          lowercase       :							Optional[Any]									=				IFInpaintingSuperResolutionPipeline(**pipe_a.components							)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()							)

          self._test_if_inpainting(_a      ,       _a      ,       _a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Optional[Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Optional[Any]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

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

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          lowercase       :							List[Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							List[str]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Optional[int]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							Union[str, Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							Tuple									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Optional[int]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Union[str, Any]									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Optional[Any]									=				output.images[0]

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

          lowercase       :							Optional[Any]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          lowercase       :							Union[str, Any]									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Dict									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)

          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       original_image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							int									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							List[str]									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							int									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       _a							):
          # pipeline 1

          _start_torch_memory_measurement()

          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Dict									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)
          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       num_inference_steps=2      ,       generator=_a      ,       output_type="np"      ,       )

          lowercase       :							Dict									=				output.images[0]

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

          lowercase       :							int									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          lowercase       :							str									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)

          # pipeline 2

          _start_torch_memory_measurement()

          lowercase       :							Union[str, Any]									=				torch.Generator(device="cpu"							).manual_seed(0							)

          lowercase       :							Optional[Any]									=				floats_tensor((1, 3, 64, 64)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							Union[str, Any]									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(0							)							).to(_a							)
          lowercase       :							str									=				floats_tensor((1, 3, 256, 256)      ,       rng=random.Random(1							)							).to(_a							)

          lowercase       :							Dict									=				pipe_a(
              prompt_embeds=_a      ,       negative_prompt_embeds=_a      ,       image=_a      ,       mask_image=_a      ,       original_image=_a      ,       generator=_a      ,       num_inference_steps=2      ,       output_type="np"      ,       )

          lowercase       :							Union[str, Any]									=				output.images[0]

          assert image.shape == (256, 256, 3)

          lowercase       :							Tuple									=				torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          lowercase       :							Tuple									=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"							)
          assert_mean_pixel_difference(_a      ,       _a							)


def     __magic_name__      ( )					->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()




"""simple docstring"""




# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_A     :			List[str]  						=			get_logger(__name__)

class 						a__		:
   __lowerCAmelCase				=							"""dummy_data"""
   __lowerCAmelCase				=							"""datasets"""
   __lowerCAmelCase				=							False
   def __init__(     self      ,       _a      ,       _a      ,       _a      ,       _a = None      ,       _a = False      ,       _a = True      ,       _a = None      ,       ):
          lowercase       :							int									=				0
          lowercase       :							Optional[Any]									=				dataset_name
          lowercase       :							List[str]									=				cache_dir
          lowercase       :							Union[str, Any]									=				use_local_dummy_data
          lowercase       :							str									=				config
          # download_callbacks take a single url as input
          lowercase       :							List[Callable]									=				download_callbacks or []
          # if False, it doesn't load existing files and it returns the paths of the dummy files relative
          # to the dummy_data zip file root
          lowercase       :							List[Any]									=				load_existing_dummy_data

          # TODO(PVP, QL) might need to make this more general
          lowercase       :							Tuple									=				str(_a							)
          # to be downloaded
          lowercase       :							Tuple									=				None
          lowercase       :							List[Any]									=				None
   @property
   def      __magic_name__     (     self							):
          if self._dummy_file is None:
                 lowercase       :							Optional[int]									=				self.download_dummy_data()
          return self._dummy_file
   @property
   def      __magic_name__     (     self							):
          if self.config is not None:
                 # structure is dummy / config_name / version_name
                 return os.path.join("dummy"      ,       self.config.name      ,       self.version_name							)
          # structure is dummy / version_name
          return os.path.join("dummy"      ,       self.version_name							)
   @property
   def      __magic_name__     (     self							):
          return os.path.join(self.dummy_data_folder      ,       "dummy_data.zip"							)
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				(
              self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
          )

          lowercase       :							str									=				cached_path(
              _a      ,       cache_dir=self.cache_dir      ,       extract_compressed_file=_a      ,       force_extract=_a							)

          return os.path.join(_a      ,       self.dummy_file_name							)
   @property
   def      __magic_name__     (     self							):
          return os.path.join(self.datasets_scripts_dir      ,       self.dataset_name      ,       self.dummy_zip_file							)
   @property
   def      __magic_name__     (     self							):
          if self._bucket_url is None:
                 lowercase       :							Dict									=				hf_github_url(self.dataset_name      ,       self.dummy_zip_file.replace(os.sep      ,       "/"							)							)
          return self._bucket_url
   @property
   def      __magic_name__     (     self							):
          # return full path if its a dir
          if os.path.isdir(self.dummy_file							):
                 return self.dummy_file
          # else cut off path to file -> example `xsum`.
          return "/".join(self.dummy_file.replace(os.sep      ,       "/"							).split("/"							)[:-1]							)
   def      __magic_name__     (     self      ,       _a      ,       *_a							):
          if self.load_existing_dummy_data:
                 # dummy data is downloaded and tested
                 lowercase       :							Optional[int]									=				self.dummy_file
          else:
                 # dummy data cannot be downloaded and only the path to dummy file is returned
                 lowercase       :							Optional[Any]									=				self.dummy_file_name

          # special case when data_url is a dict
          if isinstance(_a      ,       _a							):
                 return self.create_dummy_data_dict(_a      ,       _a							)
          elif isinstance(_a      ,       (list, tuple)							):
                 return self.create_dummy_data_list(_a      ,       _a							)
          else:
                 return self.create_dummy_data_single(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       *_a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       *_a      ,       **_a							):
          return path
   def      __magic_name__     (     self							):
          return {}
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				{}
          for key, single_urls in data_url.items():
                 for download_callback in self.download_callbacks:
                        if isinstance(_a      ,       _a							):
                               for single_url in single_urls:
                                      download_callback(_a							)
                        else:
                               lowercase       :							Union[str, Any]									=				single_urls
                               download_callback(_a							)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 if isinstance(_a      ,       _a							):
                        lowercase       :							Any									=				[os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							) for x in single_urls]
                 else:
                        lowercase       :							int									=				single_urls
                        lowercase       :							Tuple									=				os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							)
                 lowercase       :							List[str]									=				value

          # make sure that values are unique
          if all(isinstance(_a      ,       _a							) for i in dummy_data_dict.values()							) and len(set(dummy_data_dict.values()							)							) < len(
              dummy_data_dict.values()							):
                 # append key to value to make its name unique
                 lowercase       :							str									=				{key: value + key for key, value in dummy_data_dict.items()}

          return dummy_data_dict
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Union[str, Any]									=				[]
          # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
          lowercase       :							Any									=				all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"      ,       _a							)							) for url in data_url							)
          lowercase       :							List[Any]									=				all(
              url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"							) for url in data_url							)
          if data_url and (is_tf_records or is_pubmed_records):
                 lowercase       :							Tuple									=				[data_url[0]] * len(_a							)
          for single_url in data_url:
                 for download_callback in self.download_callbacks:
                        download_callback(_a							)
                 # we force the name of each key to be the last file / folder name of the url path
                 # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 lowercase       :							Union[str, Any]									=				os.path.join(_a      ,       urllib.parse.quote_plus(single_url.split("/"							)[-1]							)							)
                 dummy_data_list.append(_a							)
          return dummy_data_list
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          for download_callback in self.download_callbacks:
                 download_callback(_a							)
          # we force the name of each key to be the last file / folder name of the url path
          # if the url has arguments, we need to encode them with urllib.parse.quote_plus
          lowercase       :							Tuple									=				os.path.join(_a      ,       urllib.parse.quote_plus(data_url.split("/"							)[-1]							)							)
          if os.path.exists(_a							) or not self.load_existing_dummy_data:
                 return value
          else:
                 # Backward compatibility, maybe deprecate at one point.
                 # For many datasets with single url calls to dl_manager.download_and_extract,
                 # the dummy_data.zip file is actually the zipped downloaded file
                 # while now we expected the dummy_data.zip file to be a directory containing
                 # the downloaded file.
                 return path_to_dummy_data
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self      ,       _a							):
          def _iter_archive_members(_a							):
                 # this preserves the order of the members inside the ZIP archive
                 lowercase       :							Optional[int]									=				Path(self.dummy_file							).parent
                 lowercase       :							List[str]									=				path.relative_to(_a							)
                 with ZipFile(self.local_path_to_dummy_data							) as zip_file:
                        lowercase       :							List[str]									=				zip_file.namelist()
                 for member in members:
                        if member.startswith(relative_path.as_posix()							):
                               yield dummy_parent_path.joinpath(_a							)

          lowercase       :							Union[str, Any]									=				Path(_a							)
          lowercase       :							List[Any]									=				_iter_archive_members(_a							) if self.use_local_dummy_data else path.rglob("*"							)
          for file_path in file_paths:
                 if file_path.is_file() and not file_path.name.startswith((".", "__")							):
                        yield file_path.relative_to(_a							).as_posix(), file_path.open("rb"							)

   def      __magic_name__     (     self      ,       _a							):
          if not isinstance(_a      ,       _a							):
                 lowercase       :							Any									=				[paths]
          for path in paths:
                 if os.path.isfile(_a							):
                        if os.path.basename(_a							).startswith((".", "__")							):
                               return
                        yield path
                 else:
                        for dirpath, dirnames, filenames in os.walk(_a							):
                               if os.path.basename(_a							).startswith((".", "__")							):
                                      continue
                               dirnames.sort()
                               for filename in sorted(_a							):
                                      if filename.startswith((".", "__")							):
                                             continue
                                      yield os.path.join(_a      ,       _a							)


