





"""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


_UpperCamelCase							=      get_logger(__name__)


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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[Dict, str, None] = None   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, 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(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
														ua += "; " + "; ".join(F'{k}/{v}' for k, v in user_agent.items()   )
							elif isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
														ua += "; " + user_agent
							return ua



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None   ):


							'''simple docstring'''







							if token is None:
														__lowerCamelCase      :      Tuple         =HfFolder.get_token()
							if organization is None:
														__lowerCamelCase      :      Any         =whoami(SCREAMING_SNAKE_CASE   )['''name''']
														return F'{username}/{model_id}'
							else:
														return F'{organization}/{model_id}'



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Tuple   ):


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

							__lowerCamelCase      :      Any         =args.hub_token if hasattr(SCREAMING_SNAKE_CASE    ,						'''hub_token'''   ) else None
							__lowerCamelCase      :      str         =get_full_repo_name(SCREAMING_SNAKE_CASE    ,						token=SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Union[str, Any]         =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=SCREAMING_SNAKE_CASE    ,						model_name=SCREAMING_SNAKE_CASE    ,						repo_name=SCREAMING_SNAKE_CASE    ,						dataset_name=args.dataset_name if hasattr(SCREAMING_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(SCREAMING_SNAKE_CASE    ,						'''gradient_accumulation_steps'''   ) else None
							    )    ,						adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE    ,						'''adam_beta1'''   ) else None    ,						adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE    ,						'''adam_beta2'''   ) else None    ,						adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE    ,						'''adam_weight_decay'''   ) else None    ,						adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE    ,						'''adam_epsilon'''   ) else None    ,						lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE    ,						'''lr_scheduler'''   ) else None    ,						lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE    ,						'''lr_warmup_steps'''   ) else None    ,						ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE    ,						'''ema_inv_gamma'''   ) else None    ,						ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE    ,						'''ema_power'''   ) else None    ,						ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE    ,						'''ema_max_decay'''   ) else None    ,						mixed_precision=args.mixed_precision    ,						)

							__lowerCamelCase      :      Any         =os.path.join(args.output_dir    ,						'''README.md'''   )
							model_card.save(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[str]    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None   ):


							'''simple docstring'''







							if resolved_file is None or commit_hash is not None:
														return commit_hash
							__lowerCamelCase      :      str         =str(Path(SCREAMING_SNAKE_CASE   ).as_posix()   )
							__lowerCamelCase      :      List[str]         =re.search(r'''snapshots/([^/]+)/'''    ,						SCREAMING_SNAKE_CASE   )
							if search is None:
														return None
							__lowerCamelCase      :      Optional[int]         =search.groups()[0]
							return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_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.
_UpperCamelCase							=      os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
_UpperCamelCase							=      os.path.join(hf_cache_home, 'diffusers')



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[str] = None    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None   ):


							'''simple docstring'''







							if new_cache_dir is None:
														__lowerCamelCase      :      Dict         =DIFFUSERS_CACHE
							if old_cache_dir is None:
														__lowerCamelCase      :      Any         =old_diffusers_cache

							__lowerCamelCase      :      str         =Path(SCREAMING_SNAKE_CASE   ).expanduser()
							__lowerCamelCase      :      List[str]         =Path(SCREAMING_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():
																					__lowerCamelCase      :      Any         =new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE   )
																					new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE    ,						exist_ok=SCREAMING_SNAKE_CASE   )
																					os.replace(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
																					try:
																												os.symlink(SCREAMING_SNAKE_CASE    ,						SCREAMING_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).


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

if cache_version < 1:
							_UpperCamelCase							=      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:
																					_UpperCamelCase							=      '\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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None   ):


							'''simple docstring'''







							if variant is not None:
														__lowerCamelCase      :      int         =weights_name.split('''.'''   )
														__lowerCamelCase      :      str         =splits[:-1] + [variant] + splits[-1:]
														__lowerCamelCase      :      List[Any]         ='''.'''.join(SCREAMING_SNAKE_CASE   )

							return weights_name



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


							'''simple docstring'''







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











"""simple docstring"""





from typing import List, Optional, Union

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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""time_series_transformer"""
				__snake_case   :       Any	 =		{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}




				def __init__(     self  :List[Any]						,    __lowercase  :Optional[int] = None						,    __lowercase  :Optional[int] = None						,    __lowercase  :str = "student_t"						,    __lowercase  :str = "nll"						,    __lowercase  :int = 1						,    __lowercase  :List[int] = [1, 2, 3, 4, 5, 6, 7]						,    __lowercase  :Optional[Union[str, bool]] = "mean"						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :int = 0						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :Optional[List[int]] = None						,    __lowercase  :int = 32						,    __lowercase  :int = 32						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    __lowercase  :bool = True						,    __lowercase  :str = "gelu"						,    __lowercase  :int = 64						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :float = 0.1						,    __lowercase  :int = 100						,    __lowercase  :float = 0.02						,    __lowercase  :int=True						,    **__lowercase  :Optional[Any]						,    ):
											# time series specific configuration
											__lowerCamelCase      :      Tuple         =prediction_length
											__lowerCamelCase      :      List[Any]         =context_length or prediction_length
											__lowerCamelCase      :      Dict         =distribution_output
											__lowerCamelCase      :      str         =loss
											__lowerCamelCase      :      Tuple         =input_size
											__lowerCamelCase      :      int         =num_time_features
											__lowerCamelCase      :      int         =lags_sequence
											__lowerCamelCase      :      Optional[int]         =scaling
											__lowerCamelCase      :      str         =num_dynamic_real_features
											__lowerCamelCase      :      Optional[Any]         =num_static_real_features
											__lowerCamelCase      :      List[Any]         =num_static_categorical_features
											if cardinality and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      Optional[int]         =cardinality
											else:
																		__lowerCamelCase      :      str         =[0]
											if embedding_dimension and num_static_categorical_features > 0:
																		if len(__lowercase			) != num_static_categorical_features:
																									raise ValueError(
																									    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''			)
																		__lowerCamelCase      :      int         =embedding_dimension
											else:
																		__lowerCamelCase      :      Optional[int]         =[min(50						,    (cat + 1) // 2			) for cat in self.cardinality]
											__lowerCamelCase      :      List[str]         =num_parallel_samples

											# Transformer architecture configuration
											__lowerCamelCase      :      str         =input_size * len(__lowercase			) + self._number_of_features
											__lowerCamelCase      :      Union[str, Any]         =d_model
											__lowerCamelCase      :      int         =encoder_attention_heads
											__lowerCamelCase      :      int         =decoder_attention_heads
											__lowerCamelCase      :      Dict         =encoder_ffn_dim
											__lowerCamelCase      :      int         =decoder_ffn_dim
											__lowerCamelCase      :      List[Any]         =encoder_layers
											__lowerCamelCase      :      int         =decoder_layers

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

											__lowerCamelCase      :      Tuple         =activation_function
											__lowerCamelCase      :      str         =init_std

											__lowerCamelCase      :      Dict         =use_cache

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




				@property
				def 					__lowercase  (     self  :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
											)




