



"""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      =		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      a__			(   lowerCAmelCase__ = None   )     ->   str:
      UpperCAmelCase_    				=							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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__ = None			,    lowerCAmelCase__ = None   )     ->   str:






      if token is None:
            UpperCAmelCase_    				=							HfFolder.get_token()
      if organization is None:
            UpperCAmelCase_    				=							whoami(a_   )['''name''']
            return f"""{username}/{model_id}"""
      else:
            return f"""{organization}/{model_id}"""
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   )     ->   List[Any]:






      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

      UpperCAmelCase_    				=							args.hub_token if hasattr(a_			,    "hub_token"   ) else None
      UpperCAmelCase_    				=							get_full_repo_name(a_			,    token=a_   )

      UpperCAmelCase_    				=							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			,    )

      UpperCAmelCase_    				=							os.path.join(args.output_dir			,    "README.md"   )
      model_card.save(a_   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__ = None   )     ->   int:






      if resolved_file is None or commit_hash is not None:
            return commit_hash
      UpperCAmelCase_    				=							str(Path(a_   ).as_posix()   )
      UpperCAmelCase_    				=							re.search(r"snapshots/([^/]+)/"			,    a_   )
      if search is None:
            return None
      UpperCAmelCase_    				=							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      =		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      a__			(   lowerCAmelCase__ = None			,    lowerCAmelCase__ = None   )     ->   None:






      if new_cache_dir is None:
            UpperCAmelCase_    				=							DIFFUSERS_CACHE
      if old_cache_dir is None:
            UpperCAmelCase_    				=							old_diffusers_cache

      UpperCAmelCase_    				=							Path(a_   ).expanduser()
      UpperCAmelCase_    				=							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():
                  UpperCAmelCase_    				=							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      =		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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__ = None   )     ->   str:






      if variant is not None:
            UpperCAmelCase_    				=							weights_name.split("."   )
            UpperCAmelCase_    				=							splits[:-1] + [variant] + splits[-1:]
            UpperCAmelCase_    				=							'''.'''.join(a_   )

      return weights_name


def      a__			(   lowerCAmelCase__			,    *,
    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    )     ->   List[str]:
      UpperCAmelCase_    				=							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
                  UpperCAmelCase_    				=							os.path.join(a_			,    a_   )
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(a_			,    a_			,    a_   )   ):
                  UpperCAmelCase_    				=							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:
                        UpperCAmelCase_    				=							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
                  UpperCAmelCase_    				=							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 decimal import Decimal

from numpy import array
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(lowerCAmelCase__   ) == 2 and len(matrix[0]   ) == 2 and len(matrix[1]   ) == 2:
            # Calculate the determinant of the matrix
            UpperCAmelCase_    				=							float(
                d(matrix[0][0]   ) * d(matrix[1][1]   ) - d(matrix[1][0]   ) * d(matrix[0][1]   )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creates a copy of the matrix with swapped positions of the elements
            UpperCAmelCase_    				=							[[0.0, 0.0], [0.0, 0.0]]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							matrix[1][1], matrix[0][0]
            UpperCAmelCase_				,					UpperCAmelCase_    				=							-matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(lowerCAmelCase__   )   ) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(lowerCAmelCase__   ) == 3
          and len(matrix[0]   ) == 3
          and len(matrix[1]   ) == 3
          and len(matrix[2]   ) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            UpperCAmelCase_    				=							float(
                (
                    (d(matrix[0][0]   ) * d(matrix[1][1]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][2]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][2]   ) * d(matrix[1][0]   ) * d(matrix[2][1]   ))
                )
                - (
                    (d(matrix[0][2]   ) * d(matrix[1][1]   ) * d(matrix[2][0]   ))
                    + (d(matrix[0][1]   ) * d(matrix[1][0]   ) * d(matrix[2][2]   ))
                    + (d(matrix[0][0]   ) * d(matrix[1][2]   ) * d(matrix[2][1]   ))
                )   )
            if determinant == 0:
                  raise ValueError("This matrix has no inverse."   )

            # Creating cofactor matrix
            UpperCAmelCase_    				=							[
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
                [d(0.0   ), d(0.0   ), d(0.0   )],
            ]
            UpperCAmelCase_    				=							(d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
                d(matrix[1][2]   ) * d(matrix[2][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
                d(matrix[1][1]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[2][0]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
                d(matrix[0][2]   ) * d(matrix[1][1]   )
            )
            UpperCAmelCase_    				=							-(
                (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
            )
            UpperCAmelCase_    				=							(d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
                d(matrix[0][1]   ) * d(matrix[1][0]   )
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        UpperCAmelCase_    				=							cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            UpperCAmelCase_    				=							array(lowerCAmelCase__   )
            for i in range(3   ):
                  for j in range(3   ):
                        inverse_matrix[i][j] /= d(lowerCAmelCase__   )

        # Calculate the inverse of the matrix
            return [[float(d(lowerCAmelCase__   )   ) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError("Please provide a matrix of size 2x2 or 3x3."   )




