

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    :Dict      =			get_logger(__name__)


__a    :int      =			Path(__file__).parent / 'model_card_template.md'
__a    :int      =			uuida().hex
__a    :Optional[Any]      =			os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
__a    :List[Any]      =			os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
__a    :List[Any]      =			HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'

def       __snake_case						( __UpperCamelCase	: Union[Dict, str, None] = None    ):



      """simple docstring"""

      A_						  =				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(__UpperCamelCase				,__UpperCamelCase    ):
            ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()    )
      elif isinstance(__UpperCamelCase				,__UpperCamelCase    ):
            ua += "; " + user_agent
      return ua

def       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: Optional[str] = None				,__UpperCamelCase	: Optional[str] = None    ):



      """simple docstring"""

      if token is None:
            A_						  =				HfFolder.get_token()
      if organization is None:
            A_						  =				whoami(__UpperCamelCase    )["name"]
            return f'''{username}/{model_id}'''
      else:
            return f'''{organization}/{model_id}'''

def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: Dict    ):



      """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(__UpperCamelCase				,"local_rank"    ) and args.local_rank not in [-1, 0]:
            return

      A_						  =				args.hub_token if hasattr(__UpperCamelCase				,"hub_token"    ) else None
      A_						  =				get_full_repo_name(__UpperCamelCase				,token=__UpperCamelCase    )

      A_						  =				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=__UpperCamelCase				,model_name=__UpperCamelCase				,repo_name=__UpperCamelCase				,dataset_name=args.dataset_name if hasattr(__UpperCamelCase				,"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(__UpperCamelCase				,"gradient_accumulation_steps"    ) else None
          )				,adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase				,"adam_beta1"    ) else None				,adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase				,"adam_beta2"    ) else None				,adam_weight_decay=args.adam_weight_decay if hasattr(__UpperCamelCase				,"adam_weight_decay"    ) else None				,adam_epsilon=args.adam_epsilon if hasattr(__UpperCamelCase				,"adam_epsilon"    ) else None				,lr_scheduler=args.lr_scheduler if hasattr(__UpperCamelCase				,"lr_scheduler"    ) else None				,lr_warmup_steps=args.lr_warmup_steps if hasattr(__UpperCamelCase				,"lr_warmup_steps"    ) else None				,ema_inv_gamma=args.ema_inv_gamma if hasattr(__UpperCamelCase				,"ema_inv_gamma"    ) else None				,ema_power=args.ema_power if hasattr(__UpperCamelCase				,"ema_power"    ) else None				,ema_max_decay=args.ema_max_decay if hasattr(__UpperCamelCase				,"ema_max_decay"    ) else None				,mixed_precision=args.mixed_precision				,)

      A_						  =				os.path.join(args.output_dir				,"README.md"    )
      model_card.save(__UpperCamelCase    )

def       __snake_case						( __UpperCamelCase	: Optional[str]				,__UpperCamelCase	: Optional[str] = None    ):



      """simple docstring"""

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

def       __snake_case						( __UpperCamelCase	: Optional[str] = None				,__UpperCamelCase	: Optional[str] = None    ):



      """simple docstring"""

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

      A_						  =				Path(__UpperCamelCase    ).expanduser()
      A_						  =				Path(__UpperCamelCase    ).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_						  =				new_cache_dir / old_blob_path.relative_to(__UpperCamelCase    )
                  new_blob_path.parent.mkdir(parents=__UpperCamelCase				,exist_ok=__UpperCamelCase    )
                  os.replace(__UpperCamelCase				,__UpperCamelCase    )
                  try:
                        os.symlink(__UpperCamelCase				,__UpperCamelCase    )
                  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    :str      =			os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
      __a    :Dict      =			0
else:
      with open(cache_version_file) as f:
            try:
                  __a    :str      =			int(f.read())
            except ValueError:
                  __a    :List[str]      =			0

if cache_version < 1:
      __a    :Tuple      =			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    :List[Any]      =			'\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						( __UpperCamelCase	: str				,__UpperCamelCase	: Optional[str] = None    ):



      """simple docstring"""

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

      return weights_name



def       __snake_case						( __UpperCamelCase	: Any				,*,
    __UpperCamelCase	: List[Any]				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Any				,__UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Optional[Any]				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Dict				,__UpperCamelCase	: Tuple=None				,):



      """simple docstring"""

      A_						  =				str(__UpperCamelCase    )
      if os.path.isfile(__UpperCamelCase    ):
            return pretrained_model_name_or_path
      elif os.path.isdir(__UpperCamelCase    ):
            if os.path.isfile(os.path.join(__UpperCamelCase				,__UpperCamelCase    )    ):
                  # Load from a PyTorch checkpoint
                  A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase    )
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase    )    ):
                  A_						  =				os.path.join(__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase    )
                  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(__UpperCamelCase    ).base_version    ) >= version.parse("0.20.0"    )
            ):
                  try:
                        A_						  =				hf_hub_download(
                            __UpperCamelCase				,filename=_add_variant(__UpperCamelCase				,__UpperCamelCase    )				,cache_dir=__UpperCamelCase				,force_download=__UpperCamelCase				,proxies=__UpperCamelCase				,resume_download=__UpperCamelCase				,local_files_only=__UpperCamelCase				,use_auth_token=__UpperCamelCase				,user_agent=__UpperCamelCase				,subfolder=__UpperCamelCase				,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.'''				,__UpperCamelCase				,)
                        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(__UpperCamelCase				,__UpperCamelCase    )} 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(__UpperCamelCase				,__UpperCamelCase    )}\' so that the correct variant file can be added.'''				,__UpperCamelCase				,)
            try:
                  # 2. Load model file as usual
                  A_						  =				hf_hub_download(
                      __UpperCamelCase				,filename=__UpperCamelCase				,cache_dir=__UpperCamelCase				,force_download=__UpperCamelCase				,proxies=__UpperCamelCase				,resume_download=__UpperCamelCase				,local_files_only=__UpperCamelCase				,use_auth_token=__UpperCamelCase				,user_agent=__UpperCamelCase				,subfolder=__UpperCamelCase				,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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_pegasus import PegasusTokenizer
else:
	A			:						List[Any]        =  None


A			:						Optional[Any]        =  logging.get_logger(__name__)


A			:						str        =  '▁'

A			:						Optional[Any]        =  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A			:						Any        =  {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

A			:						Optional[int]        =  {
    'google/pegasus-xsum': 5_1_2,
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					PRETRAINED_VOCAB_FILES_MAP
					A__     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					PegasusTokenizer
					A__     =					['''input_ids''', '''attention_mask''']

					def __init__(self       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any]=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		Union[str, Any]="<pad>" ,   _UpperCAmelCase       :		int="</s>" ,   _UpperCAmelCase       :		int="<unk>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_2>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_1>" ,   _UpperCAmelCase       :		Optional[Any]=None ,   _UpperCAmelCase       :		Dict=103 ,   **_UpperCAmelCase       :		Dict ,   )		->  Dict:




							"""simple docstring"""


							lowercase__	     =						offset

							if additional_special_tokens is not None:
									if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											raise TypeError(
											    f'''additional_special_tokens should be of type {type(_UpperCAmelCase						)}, but is'''
											    f''' {type(_UpperCAmelCase						)}'''						)

									lowercase__	     =						(
									    ([mask_token_sent] + additional_special_tokens)
									    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
									    else additional_special_tokens
									)
									# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
									additional_special_tokens_extended += [
									    f'''<unk_{i}>''' for i in range(len(_UpperCAmelCase						) ,   self.offset - 1						)
									]

									if len(set(_UpperCAmelCase						)						) != len(_UpperCAmelCase						):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''						)
									lowercase__	     =						additional_special_tokens_extended
							else:
									lowercase__	     =						[mask_token_sent] if mask_token_sent is not None else []
									additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 ,   self.offset						)]

							super().__init__(
							    _UpperCAmelCase ,   tokenizer_file=_UpperCAmelCase ,   pad_token=_UpperCAmelCase ,   eos_token=_UpperCAmelCase ,   unk_token=_UpperCAmelCase ,   mask_token=_UpperCAmelCase ,   mask_token_sent=_UpperCAmelCase ,   offset=_UpperCAmelCase ,   additional_special_tokens=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						vocab_file
							lowercase__	     =						False if not self.vocab_file else True

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Tuple						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						set(self.all_special_ids						)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special

							if all_special_ids != set(range(len(self.additional_special_tokens						) + 3						)						):
									raise ValueError(
									    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
									    f''' {len(self.additional_special_tokens						)} additional_special_tokens, but got {all_special_ids}'''						)

							return [1 if x in all_special_ids else 0 for x in seq]

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List ,   _UpperCAmelCase       :		Optional[List] = None ,   _UpperCAmelCase       :		bool = False						)		->  List[int]:




							"""simple docstring"""


							if already_has_special_tokens:
									return self._special_token_mask(_UpperCAmelCase						)
							elif token_ids_a is None:
									return self._special_token_mask(_UpperCAmelCase						) + [1]
							else:
									return self._special_token_mask(token_ids_a + token_ids_a						) + [1]

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any]=None						)		->  List[int]:




							"""simple docstring"""


							if token_ids_a is None:
									return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]


					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
									    """tokenizer."""						)

							if not os.path.isdir(_UpperCAmelCase						):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
									return
							lowercase__	     =						os.path.join(
							    _UpperCAmelCase ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
									copyfile(self.vocab_file ,   _UpperCAmelCase						)

							return (out_vocab_file,)

