
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  =	get_logger(__name__)


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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[Dict, str, None] = None							):




			"""simple docstring"""


			lowerCAmelCase_         =      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(__lowerCAmelCase       ,				__lowerCAmelCase							):
						ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()							)
			elif isinstance(__lowerCAmelCase       ,				__lowerCAmelCase							):
						ua += "; " + user_agent
			return ua






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Optional[str] = None       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


			if token is None:
						lowerCAmelCase_         =      HfFolder.get_token()
			if organization is None:
						lowerCAmelCase_         =      whoami(__lowerCAmelCase							)["name"]
						return F"""{username}/{model_id}"""
			else:
						return F"""{organization}/{model_id}"""






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    List[str]							):




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

			lowerCAmelCase_         =      args.hub_token if hasattr(__lowerCAmelCase       ,				"hub_token"							) else None
			lowerCAmelCase_         =      get_full_repo_name(__lowerCAmelCase       ,				token=__lowerCAmelCase							)

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

			lowerCAmelCase_         =      os.path.join(args.output_dir       ,				"README.md"							)
			model_card.save(__lowerCAmelCase							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[str]       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


			if resolved_file is None or commit_hash is not None:
						return commit_hash
			lowerCAmelCase_         =      str(Path(__lowerCAmelCase							).as_posix()							)
			lowerCAmelCase_         =      re.search(r"snapshots/([^/]+)/"       ,				__lowerCAmelCase							)
			if search is None:
						return None
			lowerCAmelCase_         =      search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(__lowerCAmelCase							) 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  =	os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_A  =	os.path.join(hf_cache_home, "diffusers")






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[str] = None       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


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

			lowerCAmelCase_         =      Path(__lowerCAmelCase							).expanduser()
			lowerCAmelCase_         =      Path(__lowerCAmelCase							).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_         =      new_cache_dir / old_blob_path.relative_to(__lowerCAmelCase							)
									new_blob_path.parent.mkdir(parents=__lowerCAmelCase       ,				exist_ok=__lowerCAmelCase							)
									os.replace(__lowerCAmelCase       ,				__lowerCAmelCase							)
									try:
												os.symlink(__lowerCAmelCase       ,				__lowerCAmelCase							)
									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  =	os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
			_A  =	0
else:
			with open(cache_version_file) as f:
						try:
									_A  =	int(f.read())
						except ValueError:
									_A  =	0

if cache_version < 1:
			_A  =	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  =	"\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__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Optional[str] = None							):




			"""simple docstring"""


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

			return weights_name






def   lowerCamelCase__							(							__lowerCAmelCase    :    int       ,				*,
    __lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    str=None       ,				):




			"""simple docstring"""


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





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



