

import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase_						=			'path-to-your-trained-model'
lowerCAmelCase_						=			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')

lowerCAmelCase_						=			'A photo of sks dog in a bucket'
lowerCAmelCase_						=			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('dog-bucket.png')





import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowerCAmelCase_						=			get_logger(__name__)


lowerCAmelCase_						=			Path(__file__).parent / 'model_card_template.md'
lowerCAmelCase_						=			uuida().hex
lowerCAmelCase_						=			os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase_						=			os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase_						=			HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'






def  snake_case(    __magic_name__ = None   )			->							str:





     '''simple docstring'''


     lowercase :       List[Any]       =			F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
     if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
          return ua + "; telemetry/off"
     if is_torch_available():
          ua += F"""; torch/{_torch_version}"""
     if is_flax_available():
          ua += F"""; jax/{_jax_version}"""
          ua += F"""; flax/{_flax_version}"""
     if is_onnx_available():
          ua += F"""; onnxruntime/{_onnxruntime_version}"""
     # CI will set this value to True
     if os.environ.get('''DIFFUSERS_IS_CI'''			,		''''''   ).upper() in ENV_VARS_TRUE_VALUES:
          ua += "; is_ci/true"
     if isinstance(__magic_name__			,		__magic_name__   ):
          ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()   )
     elif isinstance(__magic_name__			,		__magic_name__   ):
          ua += "; " + user_agent
     return ua






def  snake_case(    __magic_name__			,		__magic_name__ = None			,		__magic_name__ = None   )			->							Optional[Any]:





     '''simple docstring'''


     if token is None:
          lowercase :       int       =			HfFolder.get_token()
     if organization is None:
          lowercase :       List[str]       =			whoami(__magic_name__   )['''name''']
          return F"""{username}/{model_id}"""
     else:
          return F"""{organization}/{model_id}"""






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





     '''simple docstring'''


     if not is_jinja_available():
          raise ValueError(
              '''Modelcard rendering is based on Jinja templates.'''
              ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
              ''' To install it, please run `pip install Jinja2`.'''   )

     if hasattr(__magic_name__			,		'''local_rank'''   ) and args.local_rank not in [-1, 0]:
          return

     lowercase :       Optional[Any]       =			args.hub_token if hasattr(__magic_name__			,		'''hub_token'''   ) else None
     lowercase :       int       =			get_full_repo_name(__magic_name__			,		token=__magic_name__   )

     lowercase :       Dict       =			ModelCard.from_template(
         card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
             language='''en'''			,		license='''apache-2.0'''			,		library_name='''diffusers'''			,		tags=[]			,		datasets=args.dataset_name			,		metrics=[]			,		)			,		template_path=__magic_name__			,		model_name=__magic_name__			,		repo_name=__magic_name__			,		dataset_name=args.dataset_name if hasattr(__magic_name__			,		'''dataset_name'''   ) else None			,		learning_rate=args.learning_rate			,		train_batch_size=args.train_batch_size			,		eval_batch_size=args.eval_batch_size			,		gradient_accumulation_steps=(
             args.gradient_accumulation_steps if hasattr(__magic_name__			,		'''gradient_accumulation_steps'''   ) else None
         )			,		adam_betaa=args.adam_betaa if hasattr(__magic_name__			,		'''adam_beta1'''   ) else None			,		adam_betaa=args.adam_betaa if hasattr(__magic_name__			,		'''adam_beta2'''   ) else None			,		adam_weight_decay=args.adam_weight_decay if hasattr(__magic_name__			,		'''adam_weight_decay'''   ) else None			,		adam_epsilon=args.adam_epsilon if hasattr(__magic_name__			,		'''adam_epsilon'''   ) else None			,		lr_scheduler=args.lr_scheduler if hasattr(__magic_name__			,		'''lr_scheduler'''   ) else None			,		lr_warmup_steps=args.lr_warmup_steps if hasattr(__magic_name__			,		'''lr_warmup_steps'''   ) else None			,		ema_inv_gamma=args.ema_inv_gamma if hasattr(__magic_name__			,		'''ema_inv_gamma'''   ) else None			,		ema_power=args.ema_power if hasattr(__magic_name__			,		'''ema_power'''   ) else None			,		ema_max_decay=args.ema_max_decay if hasattr(__magic_name__			,		'''ema_max_decay'''   ) else None			,		mixed_precision=args.mixed_precision			,		)

     lowercase :       Any       =			os.path.join(args.output_dir			,		'''README.md'''   )
     model_card.save(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__ = None   )			->							int:





     '''simple docstring'''


     if resolved_file is None or commit_hash is not None:
          return commit_hash
     lowercase :       Dict       =			str(Path(__magic_name__   ).as_posix()   )
     lowercase :       Any       =			re.search(r'''snapshots/([^/]+)/'''			,		__magic_name__   )
     if search is None:
          return None
     lowercase :       List[Any]       =			search.groups()[0]
     return commit_hash if REGEX_COMMIT_HASH.match(__magic_name__   ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCAmelCase_						=			os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
lowerCAmelCase_						=			os.path.join(hf_cache_home, 'diffusers')






def  snake_case(    __magic_name__ = None			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     if new_cache_dir is None:
          lowercase :       str       =			DIFFUSERS_CACHE
     if old_cache_dir is None:
          lowercase :       List[str]       =			old_diffusers_cache

     lowercase :       str       =			Path(__magic_name__   ).expanduser()
     lowercase :       Dict       =			Path(__magic_name__   ).expanduser()
     for old_blob_path in old_cache_dir.glob('''**/blobs/*'''   ):
          if old_blob_path.is_file() and not old_blob_path.is_symlink():
               lowercase :       List[Any]       =			new_cache_dir / old_blob_path.relative_to(__magic_name__   )
               new_blob_path.parent.mkdir(parents=__magic_name__			,		exist_ok=__magic_name__   )
               os.replace(__magic_name__			,		__magic_name__   )
               try:
                    os.symlink(__magic_name__			,		__magic_name__   )
               except OSError:
                    logger.warning(
                        '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''   )
     # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowerCAmelCase_						=			os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
 lowerCAmelCase_						=			0
else:
 with open(cache_version_file) as f:
  try:
   lowerCAmelCase_						=			int(f.read())
  except ValueError:
   lowerCAmelCase_						=			0

if cache_version < 1:
 lowerCAmelCase_						=			os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
 if old_cache_is_not_empty:
  logger.warning(
      'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
      'existing cached models. This is a one-time operation, you can interrupt it or run it '
      'later by calling `diffusers.utils.hub_utils.move_cache()`.'
  )
  try:
   move_cache()
  except Exception as e:
   lowerCAmelCase_						=			'\n'.join(traceback.format_tb(e.__traceback__))
   logger.error(
       f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
       'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
       'message and we will do our best to help.'
   )

if cache_version < 1:
 try:
  os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
  with open(cache_version_file, 'w') as f:
   f.write('1')
 except Exception:
  logger.warning(
      f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
      'the directory exists and can be written to.'
  )






def  snake_case(    __magic_name__			,		__magic_name__ = None   )			->							str:





     '''simple docstring'''


     if variant is not None:
          lowercase :       List[str]       =			weights_name.split('''.'''   )
          lowercase :       Any       =			splits[:-1] + [variant] + splits[-1:]
          lowercase :       Tuple       =			'''.'''.join(__magic_name__   )

     return weights_name






def  snake_case(    __magic_name__			,		*,
    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=None			,		)			->							Dict:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			str(__magic_name__   )
     if os.path.isfile(__magic_name__   ):
          return pretrained_model_name_or_path
     elif os.path.isdir(__magic_name__   ):
          if os.path.isfile(os.path.join(__magic_name__			,		__magic_name__   )   ):
               # Load from a PyTorch checkpoint
               lowercase :       Dict       =			os.path.join(__magic_name__			,		__magic_name__   )
               return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(__magic_name__			,		__magic_name__			,		__magic_name__   )   ):
               lowercase :       str       =			os.path.join(__magic_name__			,		__magic_name__			,		__magic_name__   )
               return model_file
          else:
               raise EnvironmentError(
                   F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""   )
     else:
          # 1. First check if deprecated way of loading from branches is used
          if (
              revision in DEPRECATED_REVISION_ARGS
              and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
              and version.parse(version.parse(__magic_name__   ).base_version   ) >= version.parse('''0.20.0'''   )
          ):
               try:
                    lowercase :       int       =			hf_hub_download(
                        __magic_name__			,		filename=_add_variant(__magic_name__			,		__magic_name__   )			,		cache_dir=__magic_name__			,		force_download=__magic_name__			,		proxies=__magic_name__			,		resume_download=__magic_name__			,		local_files_only=__magic_name__			,		use_auth_token=__magic_name__			,		user_agent=__magic_name__			,		subfolder=__magic_name__			,		revision=revision or commit_hash			,		)
                    warnings.warn(
                        F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""			,		__magic_name__			,		)
                    return model_file
               except:  # noqa: E722
                    warnings.warn(
                        F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__magic_name__			,		__magic_name__   )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(__magic_name__			,		__magic_name__   )}' so that the correct variant file can be added."""			,		__magic_name__			,		)
          try:
               # 2. Load model file as usual
               lowercase :       Dict       =			hf_hub_download(
                   __magic_name__			,		filename=__magic_name__			,		cache_dir=__magic_name__			,		force_download=__magic_name__			,		proxies=__magic_name__			,		resume_download=__magic_name__			,		local_files_only=__magic_name__			,		use_auth_token=__magic_name__			,		user_agent=__magic_name__			,		subfolder=__magic_name__			,		revision=revision or commit_hash			,		)
               return model_file

          except RepositoryNotFoundError:
               raise EnvironmentError(
                   F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
                   '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
                   '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
                   '''login`.'''   )
          except RevisionNotFoundError:
               raise EnvironmentError(
                   F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
                   '''this model name. Check the model page at '''
                   F"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""   )
          except EntryNotFoundError:
               raise EnvironmentError(
                   F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""   )
          except HTTPError as err:
               raise EnvironmentError(
                   F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""   )
          except ValueError:
               raise EnvironmentError(
                   F"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
                   F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
                   F""" directory containing a file named {weights_name} or"""
                   ''' \nCheckout your internet connection or see how to run the library in'''
                   ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''   )
          except EnvironmentError:
               raise EnvironmentError(
                   F"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
                   '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
                   F"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
                   F"""containing a file named {weights_name}"""   )