



'''simple docstring'''






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:      Optional[int]		 =		get_logger(__name__)


lowerCAmelCase:      Optional[int]		 =		Path(__file__).parent / 'model_card_template.md'
lowerCAmelCase:      Dict		 =		uuida().hex
lowerCAmelCase:      int		 =		os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase:      Tuple		 =		os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase:      Optional[int]		 =		HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def    lowerCamelCase__ (		_A = None       ):
		a		:      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(_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       ):
		if token is None:
				a		:      List[Any]       =  HfFolder.get_token()
		if organization is None:
				a		:      List[str]       =  whoami(_A       )['name']
				return f"""{username}/{model_id}"""
		else:
				return f"""{organization}/{model_id}"""
def    lowerCamelCase__ (		_A				,    _A       ):
		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

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

		a		:      int       =  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				,    )

		a		:      Any       =  os.path.join(args.output_dir				,    'README.md'       )
		model_card.save(_A       )
def    lowerCamelCase__ (		_A				,    _A = None       ):
		if resolved_file is None or commit_hash is not None:
				return commit_hash
		a		:      Any       =  str(Path(_A       ).as_posix()       )
		a		:      List[str]       =  re.search(r'snapshots/([^/]+)/'				,    _A       )
		if search is None:
				return None
		a		:      List[str]       =  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.
lowerCAmelCase:      Tuple		 =		os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
lowerCAmelCase:      Optional[Any]		 =		os.path.join(hf_cache_home, 'diffusers')
def    lowerCamelCase__ (		_A = None				,    _A = None       ):
		if new_cache_dir is None:
				a		:      Optional[int]       =  DIFFUSERS_CACHE
		if old_cache_dir is None:
				a		:      str       =  old_diffusers_cache

		a		:      Union[str, Any]       =  Path(_A       ).expanduser()
		a		:      Dict       =  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():
						a		:      Tuple       =  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).


lowerCAmelCase:      List[str]		 =		os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
					lowerCAmelCase:      List[str]		 =		0
else:
					with open(cache_version_file) as f:
										try:
															lowerCAmelCase:      Optional[int]		 =		int(f.read())
										except ValueError:
															lowerCAmelCase:      List[Any]		 =		0

if cache_version < 1:
					lowerCAmelCase:      Optional[Any]		 =		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:      int		 =		'\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       ):
		if variant is not None:
				a		:      Any       =  weights_name.split('.'       )
				a		:      List[str]       =  splits[:-1] + [variant] + splits[-1:]
				a		:      Tuple       =  '.'.join(_A       )

		return weights_name

def    lowerCamelCase__ (		_A				,    *,
    _A				,    _A				,    _A				,    _A				,    _A				,    _A				,    _A				,    _A				,    _A				,    _A				,    _A=None				,    ):
		a		:      Tuple       =  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
						a		:      Optional[int]       =  os.path.join(_A				,    _A       )
						return model_file
				elif subfolder is not None and os.path.isfile(
				    os.path.join(_A				,    _A				,    _A       )       ):
						a		:      Union[str, Any]       =  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:
								a		:      Optional[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
						a		:      List[str]       =  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}"""       )




'''simple docstring'''






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase:      List[str]		 =		TypeVar('T')
def    lowerCamelCase__ (		_A       ):
  return (position - 1) // 2
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 1
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 2

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[Any]			):
      a		:      list[tuple[T, int]]       =  []
      a		:      dict[T, int]       =  {}
      a		:      int       =  0







    def __len__(   self				:						Union[str, Any]			):
      return self.elements







    def __repr__(   self				:						Any			):
      return str(self.heap			)







    def     lowercase_ (   self				:						Optional[int]			):
      # Check if the priority queue is empty
      return self.elements == 0







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)			)
      a		:      int       =  self.elements
      self.elements += 1
      self._bubble_up(__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
        self._swap_nodes(0		,    self.elements - 1			)
      a     ,			a		:      str       =  self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
        a     ,			a		:      List[Any]       =  self.heap[0]
        self._bubble_down(__snake_case			)
      return elem







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Update the weight of the given key
      a		:      Any       =  self.position_map[elem]
      a		:      Union[str, Any]       =  (elem, weight)
      if position > 0:
        a		:      Optional[Any]       =  get_parent_position(__snake_case			)
        a     ,			a		:      Union[str, Any]       =  self.heap[parent_position]
        if parent_weight > weight:
          self._bubble_up(__snake_case			)
        else:
          self._bubble_down(__snake_case			)
      else:
        self._bubble_down(__snake_case			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						T			):
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      a		:      Union[str, Any]       =  self.position_map[elem]
      if curr_pos == 0:
        return None
      a		:      Union[str, Any]       =  get_parent_position(__snake_case			)
      a     ,			a		:      Tuple       =  self.heap[curr_pos]
      a     ,			a		:      List[Any]       =  self.heap[parent_position]
      if parent_weight > weight:
        self._swap_nodes(__snake_case		,    __snake_case			)
        return self._bubble_up(__snake_case			)
      return None







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						T			):
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      a		:      str       =  self.position_map[elem]
      a     ,			a		:      List[Any]       =  self.heap[curr_pos]
      a		:      int       =  get_child_left_position(__snake_case			)
      a		:      Dict       =  get_child_right_position(__snake_case			)
      if child_left_position < self.elements and child_right_position < self.elements:
        a     ,			a		:      int       =  self.heap[child_left_position]
        a     ,			a		:      Optional[Any]       =  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      if child_left_position < self.elements:
        a     ,			a		:      Dict       =  self.heap[child_left_position]
        if child_left_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      else:
        return None
      if child_right_position < self.elements:
        a     ,			a		:      List[Any]       =  self.heap[child_right_position]
        if child_right_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      return None







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						int		,    __snake_case				:						int			):
      # Swap the nodes at the given positions
      a		:      Optional[int]       =  self.heap[nodea_pos][0]
      a		:      int       =  self.heap[nodea_pos][0]
      a     ,			a		:      List[str]       =  (
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      a		:      Tuple       =  nodea_pos
      a		:      Any       =  nodea_pos

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[int]			):
      a		:      dict[T, dict[T, int]]       =  {}
      a		:      int       =  0







    def __repr__(   self				:						List[str]			):
      return str(self.connections			)







    def __len__(   self				:						str			):
      return self.nodes







    def     lowercase_ (   self				:						int		,    __snake_case				:						T			):
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
        a		:      Optional[int]       =  {}
        self.nodes += 1







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						T		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an edge between 2 nodes in the graph
      self.add_node(__snake_case			)
      self.add_node(__snake_case			)
      a		:      Any       =  weight
      a		:      Dict       =  weight
def    lowerCamelCase__ (		_A				,    ):
  a		:      dict[T, int]       =  {node: maxsize for node in graph.connections}
  a		:      dict[T, T | None]       =  {node: None for node in graph.connections}

  a		:      MinPriorityQueue[T]       =  MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(_A				,    _A       )

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  a		:      int       =  priority_queue.extract_min()
  a		:      Dict       =  0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      a		:      Tuple       =  dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(_A				,    dist[neighbour]       )
      a		:      Dict       =  node

    # running prim's algorithm
  while not priority_queue.is_empty():
    a		:      List[str]       =  priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        a		:      int       =  dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(_A				,    dist[neighbour]       )
        a		:      Optional[int]       =  node
  return dist, parent