


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}'					)






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCAmelCase			=							'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__lowerCAmelCase			=							'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

__lowerCAmelCase			=							'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,	_KWARGS_DESCRIPTION				)
class   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        if version.parse(scb.__version__			) < version.parse('1.4.12'			):
               raise ImportWarning(
                   'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                   'You can install it with `pip install "sacrebleu>=1.4.12"`.'			)
        return datasets.MetricInfo(
            description=_DESCRIPTION    ,			citation=_CITATION    ,			homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf'    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
                {
                    'predictions': datasets.Value('string'    ,			id='sequence'			),
                    'references': datasets.Sequence(datasets.Value('string'    ,			id='sequence'			)    ,			id='references'			),
                }			)    ,			codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf']    ,			reference_urls=[
                'https://github.com/m-popovic/chrF',
            ]    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = CHRF.CHAR_ORDER    ,			lowerCAmelCase__ = CHRF.WORD_ORDER    ,			lowerCAmelCase__ = CHRF.BETA    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       len(references[0]			)
        if any(len(lowerCAmelCase__			) != references_per_prediction for refs in references			):
               raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
        lowercase__:       List[str]        =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__			)]

        lowercase__:       Union[str, Any]        =       CHRF(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       str        =       sb_chrf.corpus_score(lowerCAmelCase__    ,			lowerCAmelCase__			)

        return {
            "score": output.score,
            "char_order": output.char_order,
            "word_order": output.word_order,
            "beta": output.beta,
        }


