



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 							A			(  _UpperCAmelCase       :     Union[Dict, str, None] = None					)       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Optional[str] = None							,     _UpperCAmelCase       :     Optional[str] = None					)       ->				Optional[Any]:







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





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     str					)       ->				List[Any]:







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

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

			_UpperCAmelCase	 =     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							,     )

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





def 							A			(  _UpperCAmelCase       :     Optional[str]							,     _UpperCAmelCase       :     Optional[str] = None					)       ->				List[Any]:







			'''simple docstring'''
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			_UpperCAmelCase	 =     str(Path(_UpperCAmelCase					).as_posix()					)
			_UpperCAmelCase	 =     re.search(R'snapshots/([^/]+)/'							,     _UpperCAmelCase					)
			if search is None:
						return None
			_UpperCAmelCase	 =     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__     = 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 							A			(  _UpperCAmelCase       :     Optional[str] = None							,     _UpperCAmelCase       :     Optional[str] = None					)       ->				None:







			'''simple docstring'''
			if new_cache_dir is None:
						_UpperCAmelCase	 =     DIFFUSERS_CACHE
			if old_cache_dir is None:
						_UpperCAmelCase	 =     old_diffusers_cache

			_UpperCAmelCase	 =     Path(_UpperCAmelCase					).expanduser()
			_UpperCAmelCase	 =     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():
									_UpperCAmelCase	 =     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__     = 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 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Optional[str] = None					)       ->				str:







			'''simple docstring'''
			if variant is not None:
						_UpperCAmelCase	 =     weights_name.split('.'					)
						_UpperCAmelCase	 =     splits[:-1] + [variant] + splits[-1:]
						_UpperCAmelCase	 =     '.'.join(_UpperCAmelCase					)

			return weights_name





def 							A			(  _UpperCAmelCase       :     Union[str, Any]							,     *,
    _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Any=None							,     )       ->				Any:







			'''simple docstring'''
			_UpperCAmelCase	 =     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
									_UpperCAmelCase	 =     os.path.join(_UpperCAmelCase							,     _UpperCAmelCase					)
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)					):
									_UpperCAmelCase	 =     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:
												_UpperCAmelCase	 =     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
									_UpperCAmelCase	 =     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 argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)

UpperCAmelCase__     = logging.getLogger(__name__)

UpperCAmelCase__     = {"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase__     = {"facebook/bart-base": BartTokenizer}





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'					)
			parser.add_argument(
			    '--validation_file'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='A csv or a json file containing the validation data.'					)
			parser.add_argument(
			    '--max_length'							,     type=_UpperCAmelCase							,     default=5							,     help='The maximum total input sequence length after tokenization.'							,     )
			parser.add_argument(
			    '--num_beams'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help=(
			        'Number of beams to use for evaluation. This argument will be '
			        'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
			    )							,     )
			parser.add_argument(
			    '--model_name_or_path'							,     type=_UpperCAmelCase							,     help='Path to pretrained model or model identifier from huggingface.co/models.'							,     required=_UpperCAmelCase							,     )
			parser.add_argument(
			    '--config_name'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Pretrained config name or path if not the same as model_name'							,     )
			parser.add_argument(
			    '--device'							,     type=_UpperCAmelCase							,     default='cpu'							,     help='Device where the model will be run'							,     )
			parser.add_argument('--output_file_path'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Where to store the final ONNX file.'					)

			_UpperCAmelCase	 =     parser.parse_args()

			return args





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[Any]="cpu"					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     model_dict[model_name].from_pretrained(_UpperCAmelCase					).to(_UpperCAmelCase					)
			_UpperCAmelCase	 =     tokenizer_dict[model_name].from_pretrained(_UpperCAmelCase					)

			if model_name in ["facebook/bart-base"]:
						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     0

			return huggingface_model, tokenizer





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Tuple					)       ->				List[Any]:







			'''simple docstring'''
			model.eval()

			_UpperCAmelCase	 =     None
			_UpperCAmelCase	 =     torch.jit.script(BARTBeamSearchGenerator(_UpperCAmelCase					)					)

			with torch.no_grad():
						_UpperCAmelCase	 =     'My friends are cool but they eat too many carbs.'
						_UpperCAmelCase	 =     tokenizer([ARTICLE_TO_SUMMARIZE]							,     max_length=1_024							,     return_tensors='pt'					).to(model.device					)

						_UpperCAmelCase	 =     model.generate(
						    inputs['input_ids']							,     attention_mask=inputs['attention_mask']							,     num_beams=_UpperCAmelCase							,     max_length=_UpperCAmelCase							,     early_stopping=_UpperCAmelCase							,     decoder_start_token_id=model.config.decoder_start_token_id							,     )

						torch.onnx.export(
						    _UpperCAmelCase							,     (
						        inputs['input_ids'],
						        inputs['attention_mask'],
						        num_beams,
						        max_length,
						        model.config.decoder_start_token_id,
						    )							,     _UpperCAmelCase							,     opset_version=14							,     input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']							,     output_names=['output_ids']							,     dynamic_axes={
						        'input_ids': {0: 'batch', 1: 'seq'},
						        'output_ids': {0: 'batch', 1: 'seq_out'},
						    }							,     example_outputs=_UpperCAmelCase							,     )

						logger.info('Model exported to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     remove_dup_initializers(os.path.abspath(_UpperCAmelCase					)					)

						logger.info('Deduplicated and optimized model written to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     onnxruntime.InferenceSession(_UpperCAmelCase					)
						_UpperCAmelCase	 =     ort_sess.run(
						    _UpperCAmelCase							,     {
						        'input_ids': inputs['input_ids'].cpu().numpy(),
						        'attention_mask': inputs['attention_mask'].cpu().numpy(),
						        'num_beams': np.array(_UpperCAmelCase					),
						        'max_length': np.array(_UpperCAmelCase					),
						        'decoder_start_token_id': np.array(model.config.decoder_start_token_id					),
						    }							,     )

						np.testing.assert_allclose(summary_ids.cpu().numpy()							,     ort_out[0]							,     rtol=1E-3							,     atol=1E-3					)

						logger.info('Model outputs from torch and ONNX Runtime are similar.'					)
						logger.info('Success.'					)





def 							A			(  )       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     parse_args()
			_UpperCAmelCase	 =     5
			_UpperCAmelCase	 =     4

			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     level=logging.INFO							,     )

			logger.setLevel(logging.INFO					)
			transformers.utils.logging.set_verbosity_error()

			_UpperCAmelCase	 =     torch.device(args.device					)

			_UpperCAmelCase ,  _UpperCAmelCase	 =     load_model_tokenizer(args.model_name_or_path							,     _UpperCAmelCase					)

			if model.config.decoder_start_token_id is None:
						raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'					)

			model.to(_UpperCAmelCase					)

			if args.max_length:
						_UpperCAmelCase	 =     args.max_length

			if args.num_beams:
						_UpperCAmelCase	 =     args.num_beams

			if args.output_file_path:
						_UpperCAmelCase	 =     args.output_file_path
			else:
						_UpperCAmelCase	 =     'BART.onnx'

			logger.info('Exporting model to ONNX'					)
			export_and_validate_model(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)


if __name__ == "__main__":
							main()
