


'''simple docstring'''
import argparse
import os
import re

import packaging.version


UpperCamelCase__          = '''examples/'''
UpperCamelCase__          = {
    '''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'''),
}
UpperCamelCase__          = {
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
UpperCamelCase__          = '''README.md'''





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      List[Any]:




    with open(lowerCAmelCase__    ,   '''r'''    ,   encoding='''utf-8'''    ,   newline='''\n'''		) as f:
        UpperCAmelCase__     :						Dict						=							f.read()
    UpperCAmelCase__     :						Union[str, Any]						=							REPLACE_PATTERNS[pattern]
    UpperCAmelCase__     :						List[str]						=							replace.replace('''VERSION'''    ,   lowerCAmelCase__		)
    UpperCAmelCase__     :						str						=							re_pattern.sub(lowerCAmelCase__    ,   lowerCAmelCase__		)
    with open(lowerCAmelCase__    ,   '''w'''    ,   encoding='''utf-8'''    ,   newline='''\n'''		) as f:
        f.write(lowerCAmelCase__		)





def    a__			(   lowerCAmelCase__		)       ->      Tuple:




    for folder, directories, fnames in os.walk(lowerCAmelCase__		):
        # 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(lowerCAmelCase__    ,   lowerCAmelCase__		)    ,   lowerCAmelCase__    ,   pattern='''examples'''		)





def    a__			(   lowerCAmelCase__    ,   lowerCAmelCase__=False		)       ->      Dict:




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





def    a__			(   )       ->      Any:
    UpperCAmelCase__     :						int						=							"🤗 Transformers currently provides the following architectures"
    UpperCAmelCase__     :						Any						=							"1. Want to contribute a new model?"
    with open(lowerCAmelCase__    ,   '''r'''    ,   encoding='''utf-8'''    ,   newline='''\n'''		) as f:
        UpperCAmelCase__     :						List[str]						=							f.readlines()

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

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

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





def    a__			(   )       ->      Any:




    with open(REPLACE_FILES['''init''']    ,   '''r'''		) as f:
        UpperCAmelCase__     :						str						=							f.read()
    UpperCAmelCase__     :						Optional[int]						=							REPLACE_PATTERNS["init"][0].search(lowerCAmelCase__		).groups()[0]
    return packaging.version.parse(lowerCAmelCase__		)





def    a__			(   lowerCAmelCase__=False		)       ->      int:
    UpperCAmelCase__     :						str						=							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:
        UpperCAmelCase__     :						int						=							default_version.base_version
    elif patch:
        UpperCAmelCase__     :						Union[str, Any]						=							F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
    else:
        UpperCAmelCase__     :						List[str]						=							F"""{default_version.major}.{default_version.minor + 1}.0"""

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

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





def    a__			(   )       ->      List[Any]:
    UpperCAmelCase__     :						Optional[Any]						=							get_version()
    UpperCAmelCase__     :						Optional[Any]						=							F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
    UpperCAmelCase__     :						str						=							current_version.base_version

    # Check with the user we got that right.
    UpperCAmelCase__     :						Any						=							input(F"""Which version are we developing now? [{dev_version}]"""		)
    if len(lowerCAmelCase__		) == 0:
        UpperCAmelCase__     :						Optional[Any]						=							dev_version

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


if __name__ == "__main__":
  UpperCamelCase__          = 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.''')
  UpperCamelCase__          = 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()




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Dict=32):
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Dict				     =						UNetaDModel(sample_size=UpperCAmelCase   ,  in_channels=3   ,  out_channels=3)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.optim.SGD(model.parameters()   ,  lr=0.0001)
				return model, optimizer







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

				# train with a DDPM scheduler
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						ddpm_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))






