






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					(KDPMaDiscreteScheduler,)
     SCREAMING_SNAKE_CASE     =					10




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						**UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						{
             """num_train_timesteps""": 1_100,
             """beta_start""": 0.0001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
         }

         config.update(**UpperCamelCase_					)
         return config




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]			,						[0.0002, 0.002, 0.02]					):
             self.check_over_configs(beta_start=UpperCamelCase_			,						beta_end=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						self.scheduler_classes[0]
         __A       =						self.get_scheduler_config(prediction_type="""v_prediction"""					)
         __A       =						scheduler_class(**UpperCamelCase_					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A       =						self.dummy_model()
         __A       =						self.dummy_sample_deter * scheduler.init_noise_sigma
         __A       =						sample.to(UpperCamelCase_					)

         for i, t in enumerate(scheduler.timesteps					):
             __A       =						scheduler.scale_model_input(UpperCamelCase_			,						UpperCamelCase_					)

             __A       =						model(UpperCamelCase_			,						UpperCamelCase_					)

             __A       =						scheduler.step(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
             __A       =						output.prev_sample

         __A       =						torch.sum(torch.abs(UpperCamelCase_					)					)
         __A       =						torch.mean(torch.abs(UpperCamelCase_					)					)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.69_34e-07					) < 1e-2
             assert abs(result_mean.item() - 6.11_12e-10					) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07					) < 1e-2
             assert abs(result_mean.item() - 0.0002					) < 1e-3




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         if torch_device == "mps":
             return
         __A       =						self.scheduler_classes[0]
         __A       =						self.get_scheduler_config()
         __A       =						scheduler_class(**UpperCamelCase_					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A       =						self.dummy_model()
         __A       =						self.dummy_sample_deter * scheduler.init_noise_sigma
         __A       =						sample.to(UpperCamelCase_					)

         for i, t in enumerate(scheduler.timesteps					):
             __A       =						scheduler.scale_model_input(UpperCamelCase_			,						UpperCamelCase_					)

             __A       =						model(UpperCamelCase_			,						UpperCamelCase_					)

             __A       =						scheduler.step(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
             __A       =						output.prev_sample

         __A       =						torch.sum(torch.abs(UpperCamelCase_					)					)
         __A       =						torch.mean(torch.abs(UpperCamelCase_					)					)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125					) < 1e-2
             assert abs(result_mean.item() - 0.0266					) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4125					) < 1e-2
             assert abs(result_mean.item() - 0.0266					) < 1e-3





     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         if torch_device == "mps":
             return
         __A       =						self.scheduler_classes[0]
         __A       =						self.get_scheduler_config()
         __A       =						scheduler_class(**UpperCamelCase_					)

         scheduler.set_timesteps(self.num_inference_steps			,						device=UpperCamelCase_					)

         __A       =						self.dummy_model()
         __A       =						self.dummy_sample_deter.to(UpperCamelCase_					) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             __A       =						scheduler.scale_model_input(UpperCamelCase_			,						UpperCamelCase_					)

             __A       =						model(UpperCamelCase_			,						UpperCamelCase_					)

             __A       =						scheduler.step(UpperCamelCase_			,						UpperCamelCase_			,						UpperCamelCase_					)
             __A       =						output.prev_sample

         __A       =						torch.sum(torch.abs(UpperCamelCase_					)					)
         __A       =						torch.mean(torch.abs(UpperCamelCase_					)					)

         if str(UpperCamelCase_					).startswith("""cpu"""					):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125					) < 1e-2
             assert abs(result_mean.item() - 0.0266					) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4125					) < 1e-2
             assert abs(result_mean.item() - 0.0266					) < 1e-3












import argparse
import os
import re

import packaging.version


__a   :							Tuple						= "examples/"
__a   :							Any						= {
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__a   :							Tuple						= {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__a   :							List[str]						= "README.md"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      List[str]      ,					__lowercase :      Optional[Any]     )    ->   List[Any]:





    """simple docstring"""

    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        __A       =						f.read()
    __A     ,						__A       =						REPLACE_PATTERNS[pattern]
    __A       =						replace.replace("""VERSION"""      ,					__lowercase     )
    __A       =						re_pattern.sub(__lowercase      ,					__lowercase     )
    with open(__lowercase      ,					"""w"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        f.write(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   Dict:





    """simple docstring"""

    for folder, directories, fnames in os.walk(__lowercase     ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("""research_projects"""     )
        if "legacy" in directories:
            directories.remove("""legacy"""     )
        for fname in fnames:
            if fname.endswith(""".py"""     ):
                update_version_in_file(os.path.join(__lowercase      ,					__lowercase     )      ,					__lowercase      ,					pattern="""examples"""     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Optional[int]=False     )    ->   Dict:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(__lowercase      ,					__lowercase      ,					__lowercase     )
    if not patch:
        update_version_in_examples(__lowercase     )



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





    """simple docstring"""

    __A       =						"""🤗 Transformers currently provides the following architectures"""
    __A       =						"""1. Want to contribute a new model?"""
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        __A       =						f.readlines()

    # Find the start of the list.
    __A       =						0
    while not lines[start_index].startswith(_start_prompt     ):
        start_index += 1
    start_index += 1

    __A       =						start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt     ):
        if lines[index].startswith("""1."""     ):
            __A       =						lines[index].replace(
                """https://huggingface.co/docs/transformers/main/model_doc"""      ,					"""https://huggingface.co/docs/transformers/model_doc"""      ,					)
        index += 1

    with open(__lowercase      ,					"""w"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        f.writelines(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]      ,					"""r"""     ) as f:
        __A       =						f.read()
    __A       =						REPLACE_PATTERNS["""init"""][0].search(__lowercase     ).groups()[0]
    return packaging.version.parse(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str=False     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""     )
    if default_version.is_devrelease:
        __A       =						default_version.base_version
    elif patch:
        __A       =						f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
    else:
        __A       =						f"{default_version.major}.{default_version.minor + 1}.0"

    # Now let's ask nicely if that's the right one.
    __A       =						input(f"Which version are you releasing? [{default_version}]"     )
    if len(__lowercase     ) == 0:
        __A       =						default_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase      ,					patch=__lowercase     )
    if not patch:
        print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
        clean_main_ref_in_model_list()



def       _SCREAMING_SNAKE_CASE    (			)    ->   List[str]:





    """simple docstring"""

    __A       =						get_version()
    __A       =						f"{current_version.major}.{current_version.minor + 1}.0.dev0"
    __A       =						current_version.base_version

    # Check with the user we got that right.
    __A       =						input(f"Which version are we developing now? [{dev_version}]"     )
    if len(__lowercase     ) == 0:
        __A       =						dev_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase     )
    print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
    clean_main_ref_in_model_list()


if __name__ == "__main__":
  __a   :							str						= argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  __a   :							Union[str, Any]						= parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()




