



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


__A     =    get_logger(__name__)


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





def 					lowerCAmelCase_			(	__a = None		)					->		str:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =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(__a     ,   __a		):
    ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()		)
  elif isinstance(__a     ,   __a		):
    ua += "; " + user_agent
  return ua





def 					lowerCAmelCase_			(	__a     ,   __a = None     ,   __a = None		)					->		Optional[int]:







  """simple docstring"""





  if token is None:
    lowerCamelCase__:				str        =HfFolder.get_token()
  if organization is None:
    lowerCamelCase__:				Tuple        =whoami(__a		)["name"]
    return F"""{username}/{model_id}"""
  else:
    return F"""{organization}/{model_id}"""





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Optional[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(__a     ,   "local_rank"		) and args.local_rank not in [-1, 0]:
    return

  lowerCamelCase__:				Optional[Any]        =args.hub_token if hasattr(__a     ,   "hub_token"		) else None
  lowerCamelCase__:				List[Any]        =get_full_repo_name(__a     ,   token=__a		)

  lowerCamelCase__:				List[str]        =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=__a     ,   model_name=__a     ,   repo_name=__a     ,   dataset_name=args.dataset_name if hasattr(__a     ,   "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(__a     ,   "gradient_accumulation_steps"		) else None
      )     ,   adam_betaa=args.adam_betaa if hasattr(__a     ,   "adam_beta1"		) else None     ,   adam_betaa=args.adam_betaa if hasattr(__a     ,   "adam_beta2"		) else None     ,   adam_weight_decay=args.adam_weight_decay if hasattr(__a     ,   "adam_weight_decay"		) else None     ,   adam_epsilon=args.adam_epsilon if hasattr(__a     ,   "adam_epsilon"		) else None     ,   lr_scheduler=args.lr_scheduler if hasattr(__a     ,   "lr_scheduler"		) else None     ,   lr_warmup_steps=args.lr_warmup_steps if hasattr(__a     ,   "lr_warmup_steps"		) else None     ,   ema_inv_gamma=args.ema_inv_gamma if hasattr(__a     ,   "ema_inv_gamma"		) else None     ,   ema_power=args.ema_power if hasattr(__a     ,   "ema_power"		) else None     ,   ema_max_decay=args.ema_max_decay if hasattr(__a     ,   "ema_max_decay"		) else None     ,   mixed_precision=args.mixed_precision     ,   )

  lowerCamelCase__:				str        =os.path.join(args.output_dir     ,   "README.md"		)
  model_card.save(__a		)





def 					lowerCAmelCase_			(	__a     ,   __a = None		)					->		Optional[int]:







  """simple docstring"""





  if resolved_file is None or commit_hash is not None:
    return commit_hash
  lowerCamelCase__:				Any        =str(Path(__a		).as_posix()		)
  lowerCamelCase__:				Optional[Any]        =re.search(R"snapshots/([^/]+)/"     ,   __a		)
  if search is None:
    return None
  lowerCamelCase__:				Optional[int]        =search.groups()[0]
  return commit_hash if REGEX_COMMIT_HASH.match(__a		) 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.
__A     =    os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__A     =    os.path.join(hf_cache_home, "diffusers")





def 					lowerCAmelCase_			(	__a = None     ,   __a = None		)					->		None:







  """simple docstring"""





  if new_cache_dir is None:
    lowerCamelCase__:				Dict        =DIFFUSERS_CACHE
  if old_cache_dir is None:
    lowerCamelCase__:				Optional[Any]        =old_diffusers_cache

  lowerCamelCase__:				str        =Path(__a		).expanduser()
  lowerCamelCase__:				Optional[int]        =Path(__a		).expanduser()
  for old_blob_path in old_cache_dir.glob("**/blobs/*"		):
    if old_blob_path.is_file() and not old_blob_path.is_symlink():
      lowerCamelCase__:				Union[str, Any]        =new_cache_dir / old_blob_path.relative_to(__a		)
      new_blob_path.parent.mkdir(parents=__a     ,   exist_ok=__a		)
      os.replace(__a     ,   __a		)
      try:
        os.symlink(__a     ,   __a		)
      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).


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

if cache_version < 1:
     __A     =    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:
               __A     =    "\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 					lowerCAmelCase_			(	__a     ,   __a = None		)					->		str:







  """simple docstring"""





  if variant is not None:
    lowerCamelCase__:				Optional[int]        =weights_name.split("."		)
    lowerCamelCase__:				Union[str, Any]        =splits[:-1] + [variant] + splits[-1:]
    lowerCamelCase__:				str        =".".join(__a		)

  return weights_name






def 					lowerCAmelCase_			(	__a     ,   *,
    __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a=None     ,   )					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				str        =str(__a		)
  if os.path.isfile(__a		):
    return pretrained_model_name_or_path
  elif os.path.isdir(__a		):
    if os.path.isfile(os.path.join(__a     ,   __a		)		):
      # Load from a PyTorch checkpoint
      lowerCamelCase__:				Any        =os.path.join(__a     ,   __a		)
      return model_file
    elif subfolder is not None and os.path.isfile(
        os.path.join(__a     ,   __a     ,   __a		)		):
      lowerCamelCase__:				Dict        =os.path.join(__a     ,   __a     ,   __a		)
      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(__a		).base_version		) >= version.parse("0.20.0"		)
    ):
      try:
        lowerCamelCase__:				Union[str, Any]        =hf_hub_download(
            __a     ,   filename=_add_variant(__a     ,   __a		)     ,   cache_dir=__a     ,   force_download=__a     ,   proxies=__a     ,   resume_download=__a     ,   local_files_only=__a     ,   use_auth_token=__a     ,   user_agent=__a     ,   subfolder=__a     ,   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."""     ,   __a     ,   )
        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(__a     ,   __a		)} 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(__a     ,   __a		)}' so that the correct variant file can be added."""     ,   __a     ,   )
    try:
      # 2. Load model file as usual
      lowerCamelCase__:				Tuple        =hf_hub_download(
          __a     ,   filename=__a     ,   cache_dir=__a     ,   force_download=__a     ,   proxies=__a     ,   resume_download=__a     ,   local_files_only=__a     ,   use_auth_token=__a     ,   user_agent=__a     ,   subfolder=__a     ,   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}"""		)








from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       int	,			UpperCAmelCase_    :       Distribution	,			UpperCAmelCase_    :       Dict=None	,			UpperCAmelCase_    :       Tuple=None	,			UpperCAmelCase_    :       Tuple=0)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =1.0 if scale is None else scale
   lowerCamelCase__:				List[Any]        =0.0 if loc is None else loc

   super().__init__(UpperCAmelCase_	,			[AffineTransform(loc=self.loc	,			scale=self.scale	,			event_dim=UpperCAmelCase_)])

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->int:



   '''simple docstring'''



   return self.base_dist.mean * self.scale + self.loc

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   return self.base_dist.variance * self.scale**2







 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Optional[Any]:



   '''simple docstring'''



   return self.variance.sqrt()



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Dict[str, int]	,			UpperCAmelCase_    :       Callable[..., Tuple[torch.Tensor]]	,			**UpperCAmelCase_    :       Dict)	->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =args_dim
   lowerCamelCase__:				Any        =nn.ModuleList([nn.Linear(UpperCAmelCase_	,			UpperCAmelCase_) for dim in args_dim.values()])
   lowerCamelCase__:				Any        =domain_map







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor)	->Tuple[torch.Tensor]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =[proj(UpperCAmelCase_) for proj in self.proj]

   return self.domain_map(*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     nn.Module   ):







 '''simple docstring'''

 def __init__(self    :       str	,			UpperCAmelCase_    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:				Any        =function







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			*UpperCAmelCase_    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   return self.function(UpperCAmelCase_	,			*UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42

 def __init__(self    :       str	,			UpperCAmelCase_    :       int = 1)	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =dim
   lowerCamelCase__:				int        ={k: dim * self.args_dim[k] for k in self.args_dim}

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       str)	->Optional[Any]:



   '''simple docstring'''



   if self.dim == 1:
     return self.distribution_class(*UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(*UpperCAmelCase_)	,			1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self._base_distribution(UpperCAmelCase_)
   if loc is None and scale is None:
     return distr
   else:
     return AffineTransformed(UpperCAmelCase_	,			loc=UpperCAmelCase_	,			scale=UpperCAmelCase_	,			event_dim=self.event_dim)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Tuple:



   '''simple docstring'''



   return () if self.dim == 1 else (self.dim,)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->int:



   '''simple docstring'''



   return len(self.event_shape)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->float:



   '''simple docstring'''



   return 0.0

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       int)	->nn.Module:



   '''simple docstring'''



   return ParameterProjection(
       in_features=UpperCAmelCase_	,			args_dim=self.args_dim	,			domain_map=LambdaLayer(self.domain_map)	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			*UpperCAmelCase_    :       torch.Tensor)	->Any:



   '''simple docstring'''



   raise NotImplementedError()







 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       torch.Tensor)	->torch.Tensor:



   '''simple docstring'''



   return (x + torch.sqrt(torch.square(UpperCAmelCase_) + 4.0)) / 2.0



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"df": 1, "loc": 1, "scale": 1}
 lowercase_		=      StudentT

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				str        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   lowerCamelCase__:				Any        =2.0 + cls.squareplus(UpperCAmelCase_)
   return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"loc": 1, "scale": 1}
 lowercase_		=      Normal

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[Any]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =cls.squareplus(UpperCAmelCase_).clamp_min(torch.finfo(scale.dtype).eps)
   return loc.squeeze(-1), scale.squeeze(-1)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      {"total_count": 1, "logits": 1}
 lowercase_		=      NegativeBinomial

 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Optional[int]	,			UpperCAmelCase_    :       torch.Tensor	,			UpperCAmelCase_    :       torch.Tensor)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =cls.squareplus(UpperCAmelCase_)
   return total_count.squeeze(-1), logits.squeeze(-1)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Tuple)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =distr_args
   if self.dim == 1:
     return self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)
   else:
     return Independent(self.distribution_class(total_count=UpperCAmelCase_	,			logits=UpperCAmelCase_)	,			1)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None	,			UpperCAmelCase_    :       Optional[torch.Tensor] = None)	->Distribution:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =distr_args

   if scale is not None:
     # See scaling property of Gamma.
     logits += scale.log()

   return self._base_distribution((total_count, logits))

