





"""simple docstring"""



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


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


SCREAMING_SNAKE_CASE_					:	List[str]							  =						logging.get_logger(__name__)  # pylint: disable=invalid-name





def  _snake_case     (    ):
     A__										=  """https://pypi.org/pypi/diffusers/json"""
     A__										=  json.loads(request.urlopen(lowerCAmelCase__			).read()			)["""releases"""].keys()
     return sorted(lowerCAmelCase__					,   key=lambda UpperCAmelCase_			: version.Version(lowerCAmelCase__			)			)





def  _snake_case     (    ):
     # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
     if HF_MODULES_CACHE in sys.path:
          return

     sys.path.append(lowerCAmelCase__			)
     os.makedirs(lowerCAmelCase__					,   exist_ok=lowerCAmelCase__			)
     A__										=  Path(lowerCAmelCase__			) / """__init__.py"""
     if not init_path.exists():
          init_path.touch()





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]			):
     init_hf_modules()
     A__										=  Path(lowerCAmelCase__			) / 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(lowerCAmelCase__					,   exist_ok=lowerCAmelCase__			)
     A__										=  dynamic_module_path / """__init__.py"""
     if not init_path.exists():
          init_path.touch()





def  _snake_case     (    UpperCAmelCase_  :					Dict			):
     with open(lowerCAmelCase__					,   """r"""					,   encoding="""utf-8"""			) as f:
          A__										=  f.read()

     # Imports of the form `import .xxx`
     A__										=  re.findall("""^\s*import\s+\.(\S+)\s*$"""					,   lowerCAmelCase__					,   flags=re.MULTILINE			)
     # Imports of the form `from .xxx import yyy`
     relative_imports += re.findall("""^\s*from\s+\.(\S+)\s+import"""					,   lowerCAmelCase__					,   flags=re.MULTILINE			)
     # Unique-ify
     return list(set(lowerCAmelCase__			)			)





def  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]			):
     A__										=  False
     A__										=  [module_file]
     A__										=  []

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

          A__										=  Path(lowerCAmelCase__			).parent
          A__										=  [str(module_path / m			) for m in new_imports]
          A__										=  [f for f in new_import_files if f not in all_relative_imports]
          A__										=  [F"""{f}.py""" for f in new_import_files]

          A__										=  len(lowerCAmelCase__			) == 0
          all_relative_imports.extend(lowerCAmelCase__			)

     return all_relative_imports





def  _snake_case     (    UpperCAmelCase_  :					int			):
     with open(lowerCAmelCase__					,   """r"""					,   encoding="""utf-8"""			) as f:
          A__										=  f.read()

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

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

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

     return get_relative_imports(lowerCAmelCase__			)





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Optional[int]			):
     A__										=  module_path.replace(os.path.sep					,   """."""			)
     A__										=  importlib.import_module(lowerCAmelCase__			)

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





def  _snake_case     (    UpperCAmelCase_  :					List[Any]			):
     from ..pipelines import DiffusionPipeline

     A__										=  dict(inspect.getmembers(lowerCAmelCase__					,   inspect.isclass			)			)

     A__										=  None
     for cls_name, cls in cls_members.items():
          if (
              cls_name != DiffusionPipeline.__name__
              and issubclass(cls					,   lowerCAmelCase__			)
              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__										=  cls

     return pipeline_class





def  _snake_case     (    UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					List[str]					,   UpperCAmelCase_  :					Optional[Any] = None					,   UpperCAmelCase_  :					Any = False					,   UpperCAmelCase_  :					Union[str, Any] = False					,   UpperCAmelCase_  :					Union[str, Any] = None					,   UpperCAmelCase_  :					Any = None					,   UpperCAmelCase_  :					Union[str, Any] = None					,   UpperCAmelCase_  :					List[str] = False					,   ):
     A__										=  str(lowerCAmelCase__			)

     A__										=  os.path.join(lowerCAmelCase__					,   lowerCAmelCase__			)

     if os.path.isfile(lowerCAmelCase__			):
          A__										=  module_file_or_url
          A__										=  """local"""
     elif pretrained_model_name_or_path.count("""/"""			) == 0:
          A__										=  get_diffusers_versions()
          # cut ".dev0"
          A__										=  """v""" + """.""".join(__version__.split("""."""			)[:3]			)

          # retrieve github version that matches
          if revision is None:
               A__										=  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__										=  F"""v{revision}"""
          elif revision == "main":
               A__										=  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__										=  COMMUNITY_PIPELINES_URL.format(revision=lowerCAmelCase__					,   pipeline=lowerCAmelCase__			)
          try:
               A__										=  cached_download(
                   lowerCAmelCase__					,   cache_dir=lowerCAmelCase__					,   force_download=lowerCAmelCase__					,   proxies=lowerCAmelCase__					,   resume_download=lowerCAmelCase__					,   local_files_only=lowerCAmelCase__					,   use_auth_token=lowerCAmelCase__					,   )
               A__										=  """git"""
               A__										=  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__										=  hf_hub_download(
                   lowerCAmelCase__					,   lowerCAmelCase__					,   cache_dir=lowerCAmelCase__					,   force_download=lowerCAmelCase__					,   proxies=lowerCAmelCase__					,   resume_download=lowerCAmelCase__					,   local_files_only=lowerCAmelCase__					,   use_auth_token=lowerCAmelCase__					,   )
               A__										=  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__										=  check_imports(lowerCAmelCase__			)

     # Now we move the module inside our cached dynamic modules.
     A__										=  DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
     create_dynamic_module(lowerCAmelCase__			)
     A__										=  Path(lowerCAmelCase__			) / 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(lowerCAmelCase__					,   submodule_path / module_file			)
          for module_needed in modules_needed:
               A__										=  F"""{module_needed}.py"""
               shutil.copy(os.path.join(lowerCAmelCase__					,   lowerCAmelCase__			)					,   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(lowerCAmelCase__					,   lowerCAmelCase__			):
               A__										=  use_auth_token
          elif use_auth_token is True:
               A__										=  HfFolder.get_token()
          else:
               A__										=  None

          A__										=  model_info(lowerCAmelCase__					,   revision=lowerCAmelCase__					,   token=lowerCAmelCase__			).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__										=  submodule_path / commit_hash
          A__										=  full_submodule + os.path.sep + commit_hash
          create_dynamic_module(lowerCAmelCase__			)

          if not (submodule_path / module_file).exists():
               shutil.copy(lowerCAmelCase__					,   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(
                        lowerCAmelCase__					,   F"""{module_needed}.py"""					,   cache_dir=lowerCAmelCase__					,   force_download=lowerCAmelCase__					,   resume_download=lowerCAmelCase__					,   proxies=lowerCAmelCase__					,   use_auth_token=lowerCAmelCase__					,   revision=lowerCAmelCase__					,   local_files_only=lowerCAmelCase__					,   )
     return os.path.join(lowerCAmelCase__					,   lowerCAmelCase__			)





def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Optional[int] = None					,   UpperCAmelCase_  :					Union[str, Any] = None					,   UpperCAmelCase_  :					Optional[Any] = False					,   UpperCAmelCase_  :					Optional[int] = False					,   UpperCAmelCase_  :					int = None					,   UpperCAmelCase_  :					int = None					,   UpperCAmelCase_  :					Dict = None					,   UpperCAmelCase_  :					Union[str, Any] = False					,   **UpperCAmelCase_  :					Optional[int]					,   ):
     A__										=  get_cached_module_file(
         lowerCAmelCase__					,   lowerCAmelCase__					,   cache_dir=lowerCAmelCase__					,   force_download=lowerCAmelCase__					,   resume_download=lowerCAmelCase__					,   proxies=lowerCAmelCase__					,   use_auth_token=lowerCAmelCase__					,   revision=lowerCAmelCase__					,   local_files_only=lowerCAmelCase__					,   )
     return get_class_in_module(lowerCAmelCase__					,   final_module.replace(""".py"""					,   """"""			)			)









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  UNetaDModel(
             block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,						)
         return model





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

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



@slow
@require_torch
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

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

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

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






