


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_					(  snake_case = None					)  ->    str:
       lowercase__:       Dict        =       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(snake_case      ,							snake_case					):
              ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items()					)
       elif isinstance(snake_case      ,							snake_case					):
              ua += "; " + user_agent
       return ua




def        snake_case_					(  snake_case      ,							snake_case = None      ,							snake_case = None					)  ->    Tuple:
       if token is None:
              lowercase__:       Optional[Any]        =       HfFolder.get_token()
       if organization is None:
              lowercase__:       Any        =       whoami(snake_case					)['name']
              return f'{username}/{model_id}'
       else:
              return f'{organization}/{model_id}'




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Tuple:
       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(snake_case      ,							'local_rank'					) and args.local_rank not in [-1, 0]:
              return

       lowercase__:       List[str]        =       args.hub_token if hasattr(snake_case      ,							'hub_token'					) else None
       lowercase__:       Tuple        =       get_full_repo_name(snake_case      ,							token=snake_case					)

       lowercase__:       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=snake_case      ,							model_name=snake_case      ,							repo_name=snake_case      ,							dataset_name=args.dataset_name if hasattr(snake_case      ,							'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(snake_case      ,							'gradient_accumulation_steps'					) else None
           )      ,							adam_betaa=args.adam_betaa if hasattr(snake_case      ,							'adam_beta1'					) else None      ,							adam_betaa=args.adam_betaa if hasattr(snake_case      ,							'adam_beta2'					) else None      ,							adam_weight_decay=args.adam_weight_decay if hasattr(snake_case      ,							'adam_weight_decay'					) else None      ,							adam_epsilon=args.adam_epsilon if hasattr(snake_case      ,							'adam_epsilon'					) else None      ,							lr_scheduler=args.lr_scheduler if hasattr(snake_case      ,							'lr_scheduler'					) else None      ,							lr_warmup_steps=args.lr_warmup_steps if hasattr(snake_case      ,							'lr_warmup_steps'					) else None      ,							ema_inv_gamma=args.ema_inv_gamma if hasattr(snake_case      ,							'ema_inv_gamma'					) else None      ,							ema_power=args.ema_power if hasattr(snake_case      ,							'ema_power'					) else None      ,							ema_max_decay=args.ema_max_decay if hasattr(snake_case      ,							'ema_max_decay'					) else None      ,							mixed_precision=args.mixed_precision      ,							)

       lowercase__:       List[str]        =       os.path.join(args.output_dir      ,							'README.md'					)
       model_card.save(snake_case					)




def        snake_case_					(  snake_case      ,							snake_case = None					)  ->    Union[str, Any]:
       if resolved_file is None or commit_hash is not None:
              return commit_hash
       lowercase__:       int        =       str(Path(snake_case					).as_posix()					)
       lowercase__:       List[str]        =       re.search(R'snapshots/([^/]+)/'      ,							snake_case					)
       if search is None:
              return None
       lowercase__:       Dict        =       search.groups()[0]
       return commit_hash if REGEX_COMMIT_HASH.match(snake_case					) 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_					(  snake_case = None      ,							snake_case = None					)  ->    None:
       if new_cache_dir is None:
              lowercase__:       Any        =       DIFFUSERS_CACHE
       if old_cache_dir is None:
              lowercase__:       int        =       old_diffusers_cache

       lowercase__:       Dict        =       Path(snake_case					).expanduser()
       lowercase__:       int        =       Path(snake_case					).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__:       Union[str, Any]        =       new_cache_dir / old_blob_path.relative_to(snake_case					)
                     new_blob_path.parent.mkdir(parents=snake_case      ,							exist_ok=snake_case					)
                     os.replace(snake_case      ,							snake_case					)
                     try:
                            os.symlink(snake_case      ,							snake_case					)
                     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_					(  snake_case      ,							snake_case = None					)  ->    str:
       if variant is not None:
              lowercase__:       List[Any]        =       weights_name.split('.'					)
              lowercase__:       List[Any]        =       splits[:-1] + [variant] + splits[-1:]
              lowercase__:       Optional[Any]        =       '.'.join(snake_case					)

       return weights_name




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

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = False					)  ->    list[float]:
       if radian_mode:
              return [magnitude * cos(snake_case					), magnitude * sin(snake_case					)]
       return [magnitude * cos(radians(snake_case					)					), magnitude * sin(radians(snake_case					)					)]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = 10**-1					)  ->    bool:
       lowercase__:       NDArray[floataa]        =       cross(snake_case      ,							snake_case					)
       lowercase__:       float        =       sum(snake_case					)
       return abs(snake_case					) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowerCAmelCase			=							array(
     [
         polar_force(718.4, 1_80 - 30),
         polar_force(879.54, 45),
         polar_force(1_00, -90),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowerCAmelCase			=							array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(2_15, 1_80 - 45),
         polar_force(2_64, 90 - 30),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowerCAmelCase			=							array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

 __lowerCAmelCase			=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()


