






'''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 							__magic_name__     (	UpperCAmelCase_,						UpperCAmelCase_,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Dict        =  IFPipeline
	SCREAMING_SNAKE_CASE_					:      Any        =  TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
	SCREAMING_SNAKE_CASE_					:      List[str]        =  TEXT_TO_IMAGE_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_					:      Dict        =  PipelineTesterMixin.required_optional_params - {'latents'}
	def        snake_case__		(							self       )						->  Any:
							return self._get_dummy_components()
	def        snake_case__		(							self  ,      _lowercase  ,      _lowercase=0       )						->  Optional[int]:
							if str(_lowercase       ).startswith('mps'       ):
													lowercase_      :   List[Any]        =			torch.manual_seed(_lowercase       )
							else:
													lowercase_      :   Optional[Any]        =			torch.Generator(device=_lowercase       ).manual_seed(_lowercase       )

							lowercase_      :   Tuple        =			{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}

							return inputs
	def        snake_case__		(							self       )						->  str:
							self._test_save_load_optional_components()
	@unittest.skipIf(torch_device != 'cuda'  ,      reason='float16 requires CUDA'       )
	def        snake_case__		(							self       )						->  int:
							# 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        snake_case__		(							self       )						->  List[Any]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )
	def        snake_case__		(							self       )						->  Union[str, Any]:
							self._test_save_load_local()
	def        snake_case__		(							self       )						->  Tuple:
							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        snake_case__		(							self       )						->  List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )






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







	"""simple docstring"""



	def        snake_case__		(							self       )						->  Tuple:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	def        snake_case__		(							self       )						->  int:
							# if

							lowercase_      :   List[Any]        =			IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'  ,      variant='fp16'  ,      torch_dtype=torch.floataa       )

							lowercase_      :   Dict        =			IFSuperResolutionPipeline.from_pretrained(
							    'DeepFloyd/IF-II-L-v1.0'  ,      variant='fp16'  ,      torch_dtype=torch.floataa  ,      text_encoder=_lowercase  ,      tokenizer=_lowercase       )

							# pre compute text embeddings and remove T5 to save memory

							pipe_a.text_encoder.to('cuda'       )

							lowercase_      :   Union[str, Any]        =			pipe_a.encode_prompt('anime turtle'  ,      device='cuda'       )

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

							lowercase_      :   str        =			None
							lowercase_      :   Optional[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(_lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )

							pipe_a.remove_all_hooks()
							pipe_a.remove_all_hooks()

							# img2img

							lowercase_      :   Union[str, Any]        =			IFImgaImgPipeline(**pipe_a.components       )
							lowercase_      :   str        =			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(_lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )

							pipe_a.remove_all_hooks()
							pipe_a.remove_all_hooks()

							# inpainting

							lowercase_      :   int        =			IFInpaintingPipeline(**pipe_a.components       )
							lowercase_      :   Optional[int]        =			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(_lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )
	def        snake_case__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  List[Any]:
							# pipeline 1

							_start_torch_memory_measurement()

							lowercase_      :   int        =			torch.Generator(device='cpu'       ).manual_seed(0       )
							lowercase_      :   Any        =			pipe_a(
							    prompt_embeds=_lowercase  ,      negative_prompt_embeds=_lowercase  ,      num_inference_steps=2  ,      generator=_lowercase  ,      output_type='np'  ,      )

							lowercase_      :   int        =			output.images[0]

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

							lowercase_      :   Tuple        =			torch.cuda.max_memory_allocated()
							assert mem_bytes < 13 * 10**9

							lowercase_      :   Any        =			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'       )
							assert_mean_pixel_difference(_lowercase  ,      _lowercase       )

							# pipeline 2

							_start_torch_memory_measurement()

							lowercase_      :   Optional[Any]        =			torch.Generator(device='cpu'       ).manual_seed(0       )

							lowercase_      :   List[Any]        =			floats_tensor((1, 3, 64, 64)  ,      rng=random.Random(0       )       ).to(_lowercase       )

							lowercase_      :   Any        =			pipe_a(
							    prompt_embeds=_lowercase  ,      negative_prompt_embeds=_lowercase  ,      image=_lowercase  ,      generator=_lowercase  ,      num_inference_steps=2  ,      output_type='np'  ,      )

							lowercase_      :   Optional[int]        =			output.images[0]

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

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

							lowercase_      :   str        =			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'       )
							assert_mean_pixel_difference(_lowercase  ,      _lowercase       )
	def        snake_case__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Any:
							# pipeline 1

							_start_torch_memory_measurement()

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

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

							lowercase_      :   List[Any]        =			pipe_a(
							    prompt_embeds=_lowercase  ,      negative_prompt_embeds=_lowercase  ,      image=_lowercase  ,      num_inference_steps=2  ,      generator=_lowercase  ,      output_type='np'  ,      )

							lowercase_      :   Any        =			output.images[0]

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

							lowercase_      :   Any        =			torch.cuda.max_memory_allocated()
							assert mem_bytes < 10 * 10**9

							lowercase_      :   List[Any]        =			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'       )
							assert_mean_pixel_difference(_lowercase  ,      _lowercase       )

							# pipeline 2

							_start_torch_memory_measurement()

							lowercase_      :   Any        =			torch.Generator(device='cpu'       ).manual_seed(0       )

							lowercase_      :   Any        =			floats_tensor((1, 3, 256, 256)  ,      rng=random.Random(0       )       ).to(_lowercase       )
							lowercase_      :   List[str]        =			floats_tensor((1, 3, 64, 64)  ,      rng=random.Random(0       )       ).to(_lowercase       )

							lowercase_      :   Any        =			pipe_a(
							    prompt_embeds=_lowercase  ,      negative_prompt_embeds=_lowercase  ,      image=_lowercase  ,      original_image=_lowercase  ,      generator=_lowercase  ,      num_inference_steps=2  ,      output_type='np'  ,      )

							lowercase_      :   Dict        =			output.images[0]

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

							lowercase_      :   int        =			torch.cuda.max_memory_allocated()
							assert mem_bytes < 4 * 10**9

							lowercase_      :   Any        =			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(_lowercase  ,      _lowercase       )







	def        snake_case__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# pipeline 1

							_start_torch_memory_measurement()

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

							lowercase_      :   List[str]        =			torch.Generator(device='cpu'       ).manual_seed(0       )
							lowercase_      :   List[str]        =			pipe_a(
							    prompt_embeds=_lowercase  ,      negative_prompt_embeds=_lowercase  ,      image=_lowercase  ,      mask_image=_lowercase  ,      num_inference_steps=2  ,      generator=_lowercase  ,      output_type='np'  ,      )

							lowercase_      :   List[str]        =			output.images[0]

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

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

							lowercase_      :   Optional[int]        =			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'       )
							assert_mean_pixel_difference(_lowercase  ,      _lowercase       )

							# pipeline 2

							_start_torch_memory_measurement()

							lowercase_      :   Any        =			torch.Generator(device='cpu'       ).manual_seed(0       )

							lowercase_      :   Any        =			floats_tensor((1, 3, 64, 64)  ,      rng=random.Random(0       )       ).to(_lowercase       )
							lowercase_      :   str        =			floats_tensor((1, 3, 256, 256)  ,      rng=random.Random(0       )       ).to(_lowercase       )
							lowercase_      :   Tuple        =			floats_tensor((1, 3, 256, 256)  ,      rng=random.Random(1       )       ).to(_lowercase       )

							lowercase_      :   str        =			pipe_a(
							    prompt_embeds=_lowercase  ,      negative_prompt_embeds=_lowercase  ,      image=_lowercase  ,      mask_image=_lowercase  ,      original_image=_lowercase  ,      generator=_lowercase  ,      num_inference_steps=2  ,      output_type='np'  ,      )

							lowercase_      :   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_      :   Any        =			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(_lowercase  ,      _lowercase       )






def 	_UpperCAmelCase    (  )			->			Any:





						"""simple docstring"""
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()








'''simple docstring'''

from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  ''
	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  'hf-legacy'  # "hf://"" is reserved for hffs
	def __init__(							self  ,      _lowercase = None  ,      _lowercase = None  ,      **_lowercase  ,      )						->  Optional[Any]:
							super().__init__(self  ,      **_lowercase       )
							lowercase_      :   int        =			repo_info
							lowercase_      :   List[Any]        =			token
							lowercase_      :   Union[str, Any]        =			None
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							if self.dir_cache is None:
													lowercase_      :   Optional[Any]        =			{}
													for hf_file in self.repo_info.siblings:
																			# TODO(QL): add sizes
																			lowercase_      :   str        =			{
																			    'name': hf_file.rfilename,
																			    'size': None,
																			    'type': 'file',
																			}
																			self.dir_cache.update(
																			    {
																			        str(_lowercase       ): {'name': str(_lowercase       ), 'size': None, 'type': 'directory'}
																			        for d in list(PurePosixPath(hf_file.rfilename       ).parents       )[:-1]
																			    }       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = "rb"  ,      **_lowercase  ,      )						->  Dict:
							if not isinstance(self.repo_info  ,      _lowercase       ):
													raise NotImplementedError(f"Open is only implemented for dataset repositories, but got {self.repo_info}"       )
							lowercase_      :   Optional[int]        =			hf_hub_url(self.repo_info.id  ,      _lowercase  ,      revision=self.repo_info.sha       )
							return fsspec.open(
							    _lowercase  ,      mode=_lowercase  ,      headers=get_authentication_headers_for_url(_lowercase  ,      use_auth_token=self.token       )  ,      client_kwargs={'trust_env': True}  ,      ).open()
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      **_lowercase       )						->  Tuple:
							self._get_dirs()
							lowercase_      :   str        =			self._strip_protocol(_lowercase       )
							if path in self.dir_cache:
													return self.dir_cache[path]
							else:
													raise FileNotFoundError(_lowercase       )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=False  ,      **_lowercase       )						->  List[str]:
							self._get_dirs()
							lowercase_      :   List[str]        =			PurePosixPath(path.strip('/'       )       )
							lowercase_      :   List[str]        =			{}
							for p, f in self.dir_cache.items():
													lowercase_      :   Tuple        =			PurePosixPath(p.strip('/'       )       )
													lowercase_      :   Optional[int]        =			p.parent
													if root == path:
																			lowercase_      :   List[str]        =			f
							lowercase_      :   List[str]        =			list(paths.values()       )
							if detail:
													return out
							else:
													return sorted(f['name'] for f in out       )
