

import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


a__												=						(
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


a__												=						logging.get_logger(__name__)  # pylint: disable=invalid-name



def   __UpperCAmelCase					(      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Any   =						'''https://pypi.org/pypi/diffusers/json'''
					_a      :       Optional[int]   =						json.loads(request.urlopen(__a      ).read()      )['''releases'''].keys()
					return sorted(__a						,key=lambda __a      : version.Version(__a      )      )



def   __UpperCAmelCase					(      )     ->	List[str]:

					"""simple docstring"""

					if HF_MODULES_CACHE in sys.path:
										return

					sys.path.append(__a      )
					os.makedirs(__a						,exist_ok=__a      )
					_a      :       int   =						Path(__a      ) / '''__init__.py'''
					if not init_path.exists():
										init_path.touch()



def   __UpperCAmelCase					(      __a :     Union[str, os.PathLike]      )     ->	Optional[Any]:

					"""simple docstring"""

					init_hf_modules()
					_a      :       List[Any]   =						Path(__a      ) / name
					# If the parent module does not exist yet, recursively create it.
					if not dynamic_module_path.parent.exists():
										create_dynamic_module(dynamic_module_path.parent      )
					os.makedirs(__a						,exist_ok=__a      )
					_a      :       Any   =						dynamic_module_path / '''__init__.py'''
					if not init_path.exists():
										init_path.touch()



def   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					with open(__a						,'''r'''						,encoding='''utf-8'''      ) as f:
										_a      :       Optional[Any]   =						f.read()

					# Imports of the form `import .xxx`
					_a      :       List[str]   =						re.findall('''^\s*import\s+\.(\S+)\s*$'''						,__a						,flags=re.MULTILINE      )
					# Imports of the form `from .xxx import yyy`
					relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import'''						,__a						,flags=re.MULTILINE      )
					# Unique-ify
					return list(set(__a      )      )



def   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						False
					_a      :       str   =						[module_file]
					_a      :       Dict   =						[]

					# Let's recurse through all relative imports
					while not no_change:
										_a      :       Tuple   =						[]
										for f in files_to_check:
															new_imports.extend(get_relative_imports(__a      )      )

										_a      :       Any   =						Path(__a      ).parent
										_a      :       int   =						[str(module_path / m      ) for m in new_imports]
										_a      :       str   =						[f for f in new_import_files if f not in all_relative_imports]
										_a      :       int   =						[F"""{f}.py""" for f in new_import_files]

										_a      :       int   =						len(__a      ) == 0
										all_relative_imports.extend(__a      )

					return all_relative_imports



def   __UpperCAmelCase					(      __a :     str      )     ->	Any:

					"""simple docstring"""

					with open(__a						,'''r'''						,encoding='''utf-8'''      ) as f:
										_a      :       List[str]   =						f.read()

					# Imports of the form `import xxx`
					_a      :       List[str]   =						re.findall('''^\s*import\s+(\S+)\s*$'''						,__a						,flags=re.MULTILINE      )
					# Imports of the form `from xxx import yyy`
					imports += re.findall('''^\s*from\s+(\S+)\s+import'''						,__a						,flags=re.MULTILINE      )
					# Only keep the top-level module
					_a      :       int   =						[imp.split('''.'''      )[0] for imp in imports if not imp.startswith('''.'''      )]

					# Unique-ify and test we got them all
					_a      :       List[str]   =						list(set(__a      )      )
					_a      :       Tuple   =						[]
					for imp in imports:
										try:
															importlib.import_module(__a      )
										except ImportError:
															missing_packages.append(__a      )

					if len(__a      ) > 0:
										raise ImportError(
										    '''This modeling file requires the following packages that were not found in your environment: '''
										    F"""{', '.join(__a      )}. Run `pip install {' '.join(__a      )}`"""      )

					return get_relative_imports(__a      )



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     Optional[Any]      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Any   =						module_path.replace(os.path.sep						,'''.'''      )
					_a      :       Any   =						importlib.import_module(__a      )

					if class_name is None:
										return find_pipeline_class(__a      )
					return getattr(__a						,__a      )



def   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

					"""simple docstring"""

					from ..pipelines import DiffusionPipeline

					_a      :       Tuple   =						dict(inspect.getmembers(__a						,inspect.isclass      )      )

					_a      :       Optional[int]   =						None
					for cls_name, cls in cls_members.items():
										if (
										    cls_name != DiffusionPipeline.__name__
										    and issubclass(cls						,__a      )
										    and cls.__module__.split('''.'''      )[0] != "diffusers"
										):
															if pipeline_class is not None:
																				raise ValueError(
																				    F"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
																				    F""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
																				    F""" {loaded_module}."""      )
															_a      :       Optional[int]   =						cls

					return pipeline_class



def   __UpperCAmelCase					(      __a :     Union[str, os.PathLike]						,__a :     str						,__a :     Optional[Union[str, os.PathLike]] = None						,__a :     bool = False						,__a :     bool = False						,__a :     Optional[Dict[str, str]] = None						,__a :     Optional[Union[bool, str]] = None						,__a :     Optional[str] = None						,__a :     bool = False						,)     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       List[Any]   =						str(__a      )

					_a      :       Tuple   =						os.path.join(__a						,__a      )

					if os.path.isfile(__a      ):
										_a      :       List[Any]   =						module_file_or_url
										_a      :       str   =						'''local'''
					elif pretrained_model_name_or_path.count('''/'''      ) == 0:
										_a      :       int   =						get_diffusers_versions()
										# cut ".dev0"
										_a      :       Optional[Any]   =						'''v''' + '''.'''.join(__version__.split('''.'''      )[:3]      )

										# retrieve github version that matches
										if revision is None:
															_a      :       Union[str, Any]   =						latest_version if latest_version[1:] in available_versions else '''main'''
															logger.info(F"""Defaulting to latest_version: {revision}."""      )
										elif revision in available_versions:
															_a      :       Dict   =						F"""v{revision}"""
										elif revision == "main":
															_a      :       Dict   =						revision
										else:
															raise ValueError(
															    F"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
															    F""" {', '.join(available_versions + ['main']      )}."""      )

										# community pipeline on GitHub
										_a      :       Union[str, Any]   =						COMMUNITY_PIPELINES_URL.format(revision=__a						,pipeline=__a      )
										try:
															_a      :       int   =						cached_download(
															    __a						,cache_dir=__a						,force_download=__a						,proxies=__a						,resume_download=__a						,local_files_only=__a						,use_auth_token=__a						,)
															_a      :       Dict   =						'''git'''
															_a      :       str   =						pretrained_model_name_or_path + '''.py'''
										except EnvironmentError:
															logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
															raise
					else:
										try:
															# Load from URL or cache if already cached
															_a      :       Optional[Any]   =						hf_hub_download(
															    __a						,__a						,cache_dir=__a						,force_download=__a						,proxies=__a						,resume_download=__a						,local_files_only=__a						,use_auth_token=__a						,)
															_a      :       Union[str, Any]   =						os.path.join('''local'''						,'''--'''.join(pretrained_model_name_or_path.split('''/'''      )      )      )
										except EnvironmentError:
															logger.error(F"""Could not locate the {module_file} inside {pretrained_model_name_or_path}."""      )
															raise

    # Check we have all the requirements in our environment
					_a      :       Dict   =						check_imports(__a      )

					# Now we move the module inside our cached dynamic modules.
					_a      :       int   =						DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
					create_dynamic_module(__a      )
					_a      :       Tuple   =						Path(__a      ) / full_submodule
					if submodule == "local" or submodule == "git":
										# We always copy local files (we could hash the file to see if there was a change, and give them the name of
										# that hash, to only copy when there is a modification but it seems overkill for now).
										# The only reason we do the copy is to avoid putting too many folders in sys.path.
										shutil.copy(__a						,submodule_path / module_file      )
										for module_needed in modules_needed:
															_a      :       Any   =						F"""{module_needed}.py"""
															shutil.copy(os.path.join(__a						,__a      )						,submodule_path / module_needed      )
					else:
										# Get the commit hash
										# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
										if isinstance(__a						,__a      ):
															_a      :       List[str]   =						use_auth_token
										elif use_auth_token is True:
															_a      :       Optional[int]   =						HfFolder.get_token()
										else:
															_a      :       str   =						None

										_a      :       List[Any]   =						model_info(__a						,revision=__a						,token=__a      ).sha

										# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
										# benefit of versioning.
										_a      :       List[str]   =						submodule_path / commit_hash
										_a      :       Tuple   =						full_submodule + os.path.sep + commit_hash
										create_dynamic_module(__a      )

										if not (submodule_path / module_file).exists():
															shutil.copy(__a						,submodule_path / module_file      )
										# Make sure we also have every file with relative
										for module_needed in modules_needed:
															if not (submodule_path / module_needed).exists():
																				get_cached_module_file(
																				    __a						,F"""{module_needed}.py"""						,cache_dir=__a						,force_download=__a						,resume_download=__a						,proxies=__a						,use_auth_token=__a						,revision=__a						,local_files_only=__a						,)
					return os.path.join(__a						,__a      )







def   __UpperCAmelCase					(      __a :     Union[str, os.PathLike]						,__a :     str						,__a :     Optional[str] = None						,__a :     Optional[Union[str, os.PathLike]] = None						,__a :     bool = False						,__a :     bool = False						,__a :     Optional[Dict[str, str]] = None						,__a :     Optional[Union[bool, str]] = None						,__a :     Optional[str] = None						,__a :     bool = False						,**__a :     Tuple						,)     ->	int:

					"""simple docstring"""

					_a      :       Any   =						get_cached_module_file(
					    __a						,__a						,cache_dir=__a						,force_download=__a						,resume_download=__a						,proxies=__a						,use_auth_token=__a						,revision=__a						,local_files_only=__a						,)
					return get_class_in_module(__a						,final_module.replace('''.py'''						,''''''      )      )










'''simple docstring'''




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=sys.maxsize):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			'bilinear'
          __A		:					int						=			max_size
          __A		:					Optional[Any]						=			short_edge_length


     def __call__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			[]
          for img in imgs:
               __A					,__A		:					Dict						=			img.shape[:2]
               # later: provide list and randomly choose index for resize
               __A		:					List[Any]						=			np.random.randint(self.short_edge_length[0]		,      self.short_edge_length[1] + 1)
               if size == 0:
                    return img
               __A		:					Tuple						=			size * 1.0 / min(_UpperCAmelCase		,      _UpperCAmelCase)
               if h < w:
                    __A					,__A		:					Optional[Any]						=			size, scale * w
               else:
                    __A					,__A		:					Optional[Any]						=			scale * h, size
               if max(_UpperCAmelCase		,      _UpperCAmelCase) > self.max_size:
                    __A		:					Tuple						=			self.max_size * 1.0 / max(_UpperCAmelCase		,      _UpperCAmelCase)
                    __A		:					Tuple						=			newh * scale
                    __A		:					Dict						=			neww * scale
               __A		:					Dict						=			int(neww + 0.5)
               __A		:					Optional[int]						=			int(newh + 0.5)

               if img.dtype == np.uinta:
                    __A		:					int						=			Image.fromarray(_UpperCAmelCase)
                    __A		:					Optional[int]						=			pil_image.resize((neww, newh)		,      PILImageResampling.BILINEAR)
                    __A		:					Dict						=			np.asarray(_UpperCAmelCase)
               else:
                    __A		:					Optional[Any]						=			img.permute(2		,      0		,      1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
                    __A		:					Dict						=			nn.functional.interpolate(
                        _UpperCAmelCase		,      (newh, neww)		,      mode=self.interp_method		,      align_corners=_UpperCAmelCase).squeeze(0)
               img_augs.append(_UpperCAmelCase)

          return img_augs




class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[Any]						=			ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,      cfg.INPUT.MAX_SIZE_TEST)
          __A		:					List[Any]						=			cfg.INPUT.FORMAT
          __A		:					Dict						=			cfg.SIZE_DIVISIBILITY
          __A		:					str						=			cfg.PAD_VALUE
          __A		:					Union[str, Any]						=			cfg.INPUT.MAX_SIZE_TEST
          __A		:					int						=			cfg.MODEL.DEVICE
          __A		:					Tuple						=			torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)		,      1		,      1)
          __A		:					Union[str, Any]						=			torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)		,      1		,      1)
          __A		:					int						=			lambda _UpperCAmelCase: (x - self.pixel_mean) / self.pixel_std


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[Any]						=			tuple(max(_UpperCAmelCase) for s in zip(*[img.shape for img in images]))
          __A		:					Dict						=			[im.shape[-2:] for im in images]
          __A		:					Optional[int]						=			[
              nn.functional.pad(
                  _UpperCAmelCase		,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,      value=self.pad_value		,      )
              for size, im in zip(_UpperCAmelCase		,      _UpperCAmelCase)
          ]

          return torch.stack(_UpperCAmelCase), torch.tensor(_UpperCAmelCase)


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          with torch.no_grad():
               if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					int						=			[images]
               if single_image:
                    assert len(_UpperCAmelCase) == 1
               for i in range(len(_UpperCAmelCase)):
                    if isinstance(images[i]		,      torch.Tensor):
                         images.insert(_UpperCAmelCase		,      images.pop(_UpperCAmelCase).to(self.device).float())
                    elif not isinstance(images[i]		,      torch.Tensor):
                         images.insert(
                             _UpperCAmelCase		,      torch.as_tensor(img_tensorize(images.pop(_UpperCAmelCase)		,      input_format=self.input_format))
                             .to(self.device)
                             .float()		,      )
            # resize smallest edge
               __A		:					str						=			torch.tensor([im.shape[:2] for im in images])
               __A		:					List[str]						=			self.aug(_UpperCAmelCase)
               # transpose images and convert to torch tensors
               # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
               # now normalize before pad to avoid useless arithmetic
               __A		:					Any						=			[self.normalizer(_UpperCAmelCase) for x in images]
               # now pad them to do the following operations
               __A					,__A		:					Any						=			self.pad(_UpperCAmelCase)
               # Normalize

               if self.size_divisibility > 0:
                    raise NotImplementedError()
               # pad
               __A		:					str						=			torch.true_divide(_UpperCAmelCase		,      _UpperCAmelCase)
               if single_image:
                    return images[0], sizes[0], scales_yx[0]
               else:
                    return images, sizes, scales_yx






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					str     )	->	Dict:
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes







def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					Tuple[int, int]     )	->	int:
     assert torch.isfinite(__snake_case     ).all(), "Box tensor contains infinite or NaN!"
     __A					,__A		:					int						=			box_size
     tensor[:, 0].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 1].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 2].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 3].clamp_(min=0					,				max=__snake_case     )