





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__  :     Any   			=get_logger(__name__)


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





def        _lowercase   (  _UpperCAmelCase = None			)  ->					str:
				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(_UpperCAmelCase     , _UpperCAmelCase			):
								ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()			)
				elif isinstance(_UpperCAmelCase     , _UpperCAmelCase			):
								ua += "; " + user_agent
				return ua





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = None			)  ->					Union[str, Any]:
				if token is None:
								lowerCamelCase			=HfFolder.get_token()
				if organization is None:
								lowerCamelCase			=whoami(_UpperCAmelCase			)["""name"""]
								return F"""{username}/{model_id}"""
				else:
								return F"""{organization}/{model_id}"""





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Union[str, 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(_UpperCAmelCase     , """local_rank"""			) and args.local_rank not in [-1, 0]:
								return

				lowerCamelCase			=args.hub_token if hasattr(_UpperCAmelCase     , """hub_token"""			) else None
				lowerCamelCase			=get_full_repo_name(_UpperCAmelCase     , token=_UpperCAmelCase			)

				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=_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     , )

				lowerCamelCase			=os.path.join(args.output_dir     , """README.md"""			)
				model_card.save(_UpperCAmelCase			)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase = None			)  ->					Tuple:
				if resolved_file is None or commit_hash is not None:
								return commit_hash
				lowerCamelCase			=str(Path(_UpperCAmelCase			).as_posix()			)
				lowerCamelCase			=re.search(r"""snapshots/([^/]+)/"""     , _UpperCAmelCase			)
				if search is None:
								return None
				lowerCamelCase			=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.
UpperCAmelCase__  :     str   			=os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
UpperCAmelCase__  :     List[Any]   			=os.path.join(hf_cache_home, '''diffusers''')





def        _lowercase   (  _UpperCAmelCase = None     , _UpperCAmelCase = None			)  ->					None:
				if new_cache_dir is None:
								lowerCamelCase			=DIFFUSERS_CACHE
				if old_cache_dir is None:
								lowerCamelCase			=old_diffusers_cache

				lowerCamelCase			=Path(_UpperCAmelCase			).expanduser()
				lowerCamelCase			=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():
												lowerCamelCase			=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).


UpperCAmelCase__  :     Optional[int]   			=os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
		UpperCAmelCase__  :     List[Any]   			=0
else:
		with open(cache_version_file) as f:
				try:
						UpperCAmelCase__  :     List[Any]   			=int(f.read())
				except ValueError:
						UpperCAmelCase__  :     Tuple   			=0

if cache_version < 1:
		UpperCAmelCase__  :     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:
						UpperCAmelCase__  :     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        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase = None			)  ->					str:
				if variant is not None:
								lowerCamelCase			=weights_name.split("""."""			)
								lowerCamelCase			=splits[:-1] + [variant] + splits[-1:]
								lowerCamelCase			=""".""".join(_UpperCAmelCase			)

				return weights_name





def        _lowercase   (  _UpperCAmelCase     , *,
    _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=None     , )  ->					Dict:
				lowerCamelCase			=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
												lowerCamelCase			=os.path.join(_UpperCAmelCase     , _UpperCAmelCase			)
												return model_file
								elif subfolder is not None and os.path.isfile(
								    os.path.join(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)			):
												lowerCamelCase			=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:
																lowerCamelCase			=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
												lowerCamelCase			=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 logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase__  :     Union[str, Any]   			=logging.getLogger(__name__)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:
				return (preds == labels).mean()

@dataclass
class  __A       :
				__A								=			field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,   )




@dataclass
class  __A       :
				__A								=			field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()      )}      )
				__A								=			field(metadata={"""help""": """Should contain the data files for the task."""}      )
				__A								=			field(
				    default=1_28      ,   metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }      ,   )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )





def        _lowercase   (  )  ->					Optional[int]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowerCamelCase			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
				lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir			)
				    and os.listdir(training_args.output_dir			)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    """ --overwrite_output_dir to overcome."""			)

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     , datefmt="""%m/%d/%Y %H:%M:%S"""     , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     , )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""     , training_args.local_rank     , training_args.device     , training_args.n_gpu     , bool(training_args.local_rank != -1			)     , training_args.fpaa     , )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank			):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""     , _UpperCAmelCase			)

				# Set seed
				set_seed(training_args.seed			)

				try:
								lowerCamelCase			=processors[data_args.task_name]()
								lowerCamelCase			=processor.get_labels()
								lowerCamelCase			=len(_UpperCAmelCase			)
				except KeyError:
								raise ValueError("""Task not found: %s""" % (data_args.task_name)			)

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowerCamelCase			=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path     , num_labels=_UpperCAmelCase     , finetuning_task=data_args.task_name     , cache_dir=model_args.cache_dir     , )
				lowerCamelCase			=AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     , cache_dir=model_args.cache_dir     , )
				lowerCamelCase			=AutoModelForMultipleChoice.from_pretrained(
				    model_args.model_name_or_path     , from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)     , config=_UpperCAmelCase     , cache_dir=model_args.cache_dir     , )

				# Get datasets
				lowerCamelCase			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , task=data_args.task_name     , max_seq_length=data_args.max_seq_length     , overwrite_cache=data_args.overwrite_cache     , mode=Split.train     , )
				    if training_args.do_train
				    else None
				)
				lowerCamelCase			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , task=data_args.task_name     , max_seq_length=data_args.max_seq_length     , overwrite_cache=data_args.overwrite_cache     , mode=Split.dev     , )
				    if training_args.do_eval
				    else None
				)

				def compute_metrics(_UpperCAmelCase			) -> Dict:
								lowerCamelCase			=np.argmax(p.predictions     , axis=1			)
								return {"acc": simple_accuracy(_UpperCAmelCase     , p.label_ids			)}

				# Data collator
				lowerCamelCase			=DataCollatorWithPadding(_UpperCAmelCase     , pad_to_multiple_of=8			) if training_args.fpaa else None

				# Initialize our Trainer
				lowerCamelCase			=Trainer(
				    model=_UpperCAmelCase     , args=_UpperCAmelCase     , train_dataset=_UpperCAmelCase     , eval_dataset=_UpperCAmelCase     , compute_metrics=_UpperCAmelCase     , data_collator=_UpperCAmelCase     , )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
				lowerCamelCase			={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""			)

								lowerCamelCase			=trainer.evaluate()

								lowerCamelCase			=os.path.join(training_args.output_dir     , """eval_results.txt"""			)
								if trainer.is_world_master():
												with open(_UpperCAmelCase     , """w"""			) as writer:
																logger.info("""***** Eval results *****"""			)
																for key, value in result.items():
																				logger.info("""  %s = %s"""     , _UpperCAmelCase     , _UpperCAmelCase			)
																				writer.write("""%s = %s\n""" % (key, value)			)

																results.update(_UpperCAmelCase			)

				return results





def        _lowercase   (  _UpperCAmelCase			)  ->					Union[str, Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()





