






"""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


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 (  A__ = None		):
			UpperCAmelCase_	:					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 							snake_case (  A__      ,A__ = None      ,A__ = None		):
			if token is None:
						UpperCAmelCase_	:					str     	=   HfFolder.get_token()
			if organization is None:
						UpperCAmelCase_	:					Optional[Any]     	=   whoami(A__		)["name"]
						return F"""{username}/{model_id}"""
			else:
						return F"""{organization}/{model_id}"""

def 							snake_case (  A__      ,A__		):
			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

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

			UpperCAmelCase_	:					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      ,)

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

def 							snake_case (  A__      ,A__ = None		):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			UpperCAmelCase_	:					List[str]     	=   str(Path(A__		).as_posix()		)
			UpperCAmelCase_	:					Tuple     	=   re.search(r"snapshots/([^/]+)/"      ,A__		)
			if search is None:
						return None
			UpperCAmelCase_	:					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.
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 (  A__ = None      ,A__ = None		):
			if new_cache_dir is None:
						UpperCAmelCase_	:					int     	=   DIFFUSERS_CACHE
			if old_cache_dir is None:
						UpperCAmelCase_	:					Dict     	=   old_diffusers_cache

			UpperCAmelCase_	:					Union[str, Any]     	=   Path(A__		).expanduser()
			UpperCAmelCase_	:					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():
									UpperCAmelCase_	:					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).


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 (  A__      ,A__ = None		):
			if variant is not None:
						UpperCAmelCase_	:					Union[str, Any]     	=   weights_name.split("."		)
						UpperCAmelCase_	:					Union[str, Any]     	=   splits[:-1] + [variant] + splits[-1:]
						UpperCAmelCase_	:					Optional[Any]     	=   ".".join(A__		)

			return weights_name




def 							snake_case (  A__      ,*,
    A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,A__=None      ,):
			UpperCAmelCase_	:					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
									UpperCAmelCase_	:					Dict     	=   os.path.join(A__      ,A__		)
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(A__      ,A__      ,A__		)		):
									UpperCAmelCase_	:					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:
												UpperCAmelCase_	:					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
									UpperCAmelCase_	:					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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






