"""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


__SCREAMING_SNAKE_CASE             =get_logger(__name__)


__SCREAMING_SNAKE_CASE             =Path(__file__).parent / "model_card_template.md"
__SCREAMING_SNAKE_CASE             =uuida().hex
__SCREAMING_SNAKE_CASE             =os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
__SCREAMING_SNAKE_CASE             =os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
__SCREAMING_SNAKE_CASE             =HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int] = None ):
     lowercase_      :							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      lowercase__(			__SCREAMING_SNAKE_CASE				:	Dict	,     __SCREAMING_SNAKE_CASE				:	str = None	,     __SCREAMING_SNAKE_CASE				:	Optional[int] = None ):
     if token is None:
          lowercase_      :							str						=      HfFolder.get_token()
     if organization is None:
          lowercase_      :							Optional[Any]						=      whoami(A__ )["name"]
          return F'''{username}/{model_id}'''
     else:
          return F'''{organization}/{model_id}'''



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	str	,     __SCREAMING_SNAKE_CASE				:	Optional[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

     lowercase_      :							Tuple						=      args.hub_token if hasattr(A__	,     'hub_token' ) else None
     lowercase_      :							Union[str, Any]						=      get_full_repo_name(A__	,     token=A__ )

     lowercase_      :							Tuple						=      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	,     )

     lowercase_      :							Optional[Any]						=      os.path.join(args.output_dir	,     'README.md' )
     model_card.save(A__ )



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	int	,     __SCREAMING_SNAKE_CASE				:	Any = None ):
     if resolved_file is None or commit_hash is not None:
          return commit_hash
     lowercase_      :							List[str]						=      str(Path(A__ ).as_posix() )
     lowercase_      :							Tuple						=      re.search(R'snapshots/([^/]+)/'	,     A__ )
     if search is None:
          return None
     lowercase_      :							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.
__SCREAMING_SNAKE_CASE             =os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__SCREAMING_SNAKE_CASE             =os.path.join(hf_cache_home, "diffusers")



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Union[str, Any] = None	,     __SCREAMING_SNAKE_CASE				:	List[Any] = None ):
     if new_cache_dir is None:
          lowercase_      :							int						=      DIFFUSERS_CACHE
     if old_cache_dir is None:
          lowercase_      :							Dict						=      old_diffusers_cache

     lowercase_      :							Union[str, Any]						=      Path(A__ ).expanduser()
     lowercase_      :							Optional[int]						=      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():
               lowercase_      :							Union[str, Any]						=      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).


__SCREAMING_SNAKE_CASE             =os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
       __SCREAMING_SNAKE_CASE             =0
else:
       with open(cache_version_file) as f:
              try:
                     __SCREAMING_SNAKE_CASE             =int(f.read())
              except ValueError:
                     __SCREAMING_SNAKE_CASE             =0

if cache_version < 1:
       __SCREAMING_SNAKE_CASE             =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:
                     __SCREAMING_SNAKE_CASE             ="\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      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[Any]	,     __SCREAMING_SNAKE_CASE				:	List[Any] = None ):
     if variant is not None:
          lowercase_      :							Union[str, Any]						=      weights_name.split('.' )
          lowercase_      :							Union[str, Any]						=      splits[:-1] + [variant] + splits[-1:]
          lowercase_      :							Optional[Any]						=      ".".join(A__ )

     return weights_name







def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Any	,     *,
    __SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	Dict	,     __SCREAMING_SNAKE_CASE				:	Optional[int]	,     __SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	Tuple	,     __SCREAMING_SNAKE_CASE				:	Any	,     __SCREAMING_SNAKE_CASE				:	Union[str, Any]	,     __SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	Optional[Any]	,     __SCREAMING_SNAKE_CASE				:	Dict=None	,     ):
     lowercase_      :							str						=      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
               lowercase_      :							Dict						=      os.path.join(A__	,     A__ )
               return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(A__	,     A__	,     A__ ) ):
               lowercase_      :							str						=      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:
                    lowercase_      :							List[str]						=      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
               lowercase_      :							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 typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''informer'''
	__magic_name__       =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "student_t"      ,      lowerCAmelCase_   :					str = "nll"      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					List[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, bool]] = "mean"      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					int = 64      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					str = "gelu"      ,      lowerCAmelCase_   :					float = 0.0_5      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					int = 100      ,      lowerCAmelCase_   :					float = 0.0_2      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					str = "prob"      ,      lowerCAmelCase_   :					int = 5      ,      lowerCAmelCase_   :					bool = True      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				# time series specific configuration
				UpperCAmelCase_	:					str     	=   prediction_length
				UpperCAmelCase_	:					Tuple     	=   context_length or prediction_length
				UpperCAmelCase_	:					Any     	=   distribution_output
				UpperCAmelCase_	:					Union[str, Any]     	=   loss
				UpperCAmelCase_	:					Any     	=   input_size
				UpperCAmelCase_	:					int     	=   num_time_features
				UpperCAmelCase_	:					Union[str, Any]     	=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				UpperCAmelCase_	:					List[Any]     	=   scaling
				UpperCAmelCase_	:					List[str]     	=   num_dynamic_real_features
				UpperCAmelCase_	:					int     	=   num_static_real_features
				UpperCAmelCase_	:					Any     	=   num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Optional[Any]     	=   cardinality
				else:
							UpperCAmelCase_	:					Optional[int]     	=   [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Any     	=   embedding_dimension
				else:
							UpperCAmelCase_	:					Optional[Any]     	=   [min(50      ,      (cat + 1) // 2		) for cat in self.cardinality]

				UpperCAmelCase_	:					Dict     	=   num_parallel_samples

				# Transformer architecture configuration
				UpperCAmelCase_	:					Optional[Any]     	=   input_size * len(self.lags_sequence		) + self._number_of_features
				UpperCAmelCase_	:					List[Any]     	=   d_model
				UpperCAmelCase_	:					List[str]     	=   encoder_attention_heads
				UpperCAmelCase_	:					List[str]     	=   decoder_attention_heads
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_ffn_dim
				UpperCAmelCase_	:					Union[str, Any]     	=   decoder_ffn_dim
				UpperCAmelCase_	:					Tuple     	=   encoder_layers
				UpperCAmelCase_	:					List[Any]     	=   decoder_layers

				UpperCAmelCase_	:					List[str]     	=   dropout
				UpperCAmelCase_	:					Optional[Any]     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_layerdrop
				UpperCAmelCase_	:					Optional[int]     	=   decoder_layerdrop

				UpperCAmelCase_	:					Union[str, Any]     	=   activation_function
				UpperCAmelCase_	:					int     	=   init_std

				UpperCAmelCase_	:					Optional[Any]     	=   use_cache

				# Informer
				UpperCAmelCase_	:					int     	=   attention_type
				UpperCAmelCase_	:					List[str]     	=   sampling_factor
				UpperCAmelCase_	:					Union[str, Any]     	=   distil

				super().__init__(is_encoder_decoder=lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				return (
				    sum(self.embedding_dimension		)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)



