"""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


_a      :		str           =   get_logger(__name__)


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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[Dict, str, None] = None	)       ->      str:
  _lowerCAmelCase						:       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(_lowerCamelCase			,_lowerCamelCase	):
    ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()	)
  elif isinstance(_lowerCamelCase			,_lowerCamelCase	):
    ua += "; " + user_agent
  return ua





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       Optional[str] = None			,_lowerCamelCase						:       Optional[str] = None	)       ->      Any:
  if token is None:
    _lowerCAmelCase						:       str  			=     HfFolder.get_token()
  if organization is None:
    _lowerCAmelCase						:       Optional[Any]  			=     whoami(_lowerCamelCase	)["""name"""]
    return f"{username}/{model_id}"
  else:
    return f"{organization}/{model_id}"





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       List[Any]	)       ->      int:
  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						:       Optional[int]  			=     args.hub_token if hasattr(_lowerCamelCase			,"""hub_token"""	) else None
  _lowerCAmelCase						:       int  			=     get_full_repo_name(_lowerCamelCase			,token=_lowerCamelCase	)

  _lowerCAmelCase						:       Optional[int]  			=     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						:       Optional[int]  			=     os.path.join(args.output_dir			,"""README.md"""	)
  model_card.save(_lowerCamelCase	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[str]			,_lowerCamelCase						:       Optional[str] = None	)       ->      Optional[int]:
  if resolved_file is None or commit_hash is not None:
    return commit_hash
  _lowerCAmelCase						:       List[str]  			=     str(Path(_lowerCamelCase	).as_posix()	)
  _lowerCAmelCase						:       List[Any]  			=     re.search(r"""snapshots/([^/]+)/"""			,_lowerCamelCase	)
  if search is None:
    return None
  _lowerCAmelCase						:       List[Any]  			=     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      :		int           =   os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
_a      :		Union[str, Any]           =   os.path.join(hf_cache_home, 'diffusers')





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[str] = None			,_lowerCamelCase						:       Optional[str] = None	)       ->      None:
  if new_cache_dir is None:
    _lowerCAmelCase						:       List[str]  			=     DIFFUSERS_CACHE
  if old_cache_dir is None:
    _lowerCAmelCase						:       str  			=     old_diffusers_cache

  _lowerCAmelCase						:       List[str]  			=     Path(_lowerCamelCase	).expanduser()
  _lowerCAmelCase						:       List[str]  			=     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						:       Optional[int]  			=     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      :		Any           =   os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
     _a      :		Dict           =   0
else:
     with open(cache_version_file) as f:
          try:
               _a      :		Optional[int]           =   int(f.read())
          except ValueError:
               _a      :		int           =   0

if cache_version < 1:
     _a      :		Any           =   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      :		Optional[int]           =   '\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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       Optional[str] = None	)       ->      str:
  if variant is not None:
    _lowerCAmelCase						:       int  			=     weights_name.split("""."""	)
    _lowerCAmelCase						:       str  			=     splits[:-1] + [variant] + splits[-1:]
    _lowerCAmelCase						:       Union[str, Any]  			=     """.""".join(_lowerCamelCase	)

  return weights_name






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,*,
    _lowerCamelCase						:       Tuple			,_lowerCamelCase						:       str			,_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Dict			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Any			,_lowerCamelCase						:       Any			,_lowerCamelCase						:       Any			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Dict=None			,)       ->      Any:
  _lowerCAmelCase						:       Tuple  			=     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						:       List[str]  			=     os.path.join(_lowerCamelCase			,_lowerCamelCase	)
      return model_file
    elif subfolder is not None and os.path.isfile(
        os.path.join(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)	):
      _lowerCAmelCase						:       Union[str, Any]  			=     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						:       Tuple  			=     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						:       Tuple  			=     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}"	)





"""simple docstring"""




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,
)

_a      :		Tuple           =   logging.getLogger(__name__)

_a      :		Any           =   {'facebook/bart-base': BartForConditionalGeneration}
_a      :		List[str]           =   {'facebook/bart-base': BartTokenizer}





def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  _lowerCAmelCase						:       int  			=     argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""	)
  parser.add_argument(
      """--validation_file"""			,type=_lowerCamelCase			,default=_lowerCamelCase			,help="""A csv or a json file containing the validation data."""	)
  parser.add_argument(
      """--max_length"""			,type=_lowerCamelCase			,default=5			,help="""The maximum total input sequence length after tokenization."""			,)
  parser.add_argument(
      """--num_beams"""			,type=_lowerCamelCase			,default=_lowerCamelCase			,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=_lowerCamelCase			,help="""Path to pretrained model or model identifier from huggingface.co/models."""			,required=_lowerCamelCase			,)
  parser.add_argument(
      """--config_name"""			,type=_lowerCamelCase			,default=_lowerCamelCase			,help="""Pretrained config name or path if not the same as model_name"""			,)
  parser.add_argument(
      """--device"""			,type=_lowerCamelCase			,default="""cpu"""			,help="""Device where the model will be run"""			,)
  parser.add_argument("""--output_file_path"""			,type=_lowerCamelCase			,default=_lowerCamelCase			,help="""Where to store the final ONNX file."""	)

  _lowerCAmelCase						:       Optional[Any]  			=     parser.parse_args()

  return args





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Union[str, Any]="cpu"	)       ->      str:
  _lowerCAmelCase						:       List[str]  			=     model_dict[model_name].from_pretrained(_lowerCamelCase	).to(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[Any]  			=     tokenizer_dict[model_name].from_pretrained(_lowerCamelCase	)

  if model_name in ["facebook/bart-base"]:
    _lowerCAmelCase						:       List[Any]  			=     0
    _lowerCAmelCase						:       str  			=     None
    _lowerCAmelCase						:       List[str]  			=     0

  return huggingface_model, tokenizer





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       List[str]	)       ->      Tuple:
  model.eval()

  _lowerCAmelCase						:       str  			=     None
  _lowerCAmelCase						:       int  			=     torch.jit.script(BARTBeamSearchGenerator(_lowerCamelCase	)	)

  with torch.no_grad():
    _lowerCAmelCase						:       List[Any]  			=     """My friends are cool but they eat too many carbs."""
    _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer([ARTICLE_TO_SUMMARIZE]			,max_length=1024			,return_tensors="""pt"""	).to(model.device	)

    _lowerCAmelCase						:       Any  			=     model.generate(
        inputs["""input_ids"""]			,attention_mask=inputs["""attention_mask"""]			,num_beams=_lowerCamelCase			,max_length=_lowerCamelCase			,early_stopping=_lowerCamelCase			,decoder_start_token_id=model.config.decoder_start_token_id			,)

    torch.onnx.export(
        _lowerCamelCase			,(
            inputs["""input_ids"""],
            inputs["""attention_mask"""],
            num_beams,
            max_length,
            model.config.decoder_start_token_id,
        )			,_lowerCamelCase			,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=_lowerCamelCase			,)

    logger.info("""Model exported to {}""".format(_lowerCamelCase	)	)

    _lowerCAmelCase						:       List[str]  			=     remove_dup_initializers(os.path.abspath(_lowerCamelCase	)	)

    logger.info("""Deduplicated and optimized model written to {}""".format(_lowerCamelCase	)	)

    _lowerCAmelCase						:       str  			=     onnxruntime.InferenceSession(_lowerCamelCase	)
    _lowerCAmelCase						:       Union[str, Any]  			=     ort_sess.run(
        _lowerCamelCase			,{
            """input_ids""": inputs["""input_ids"""].cpu().numpy(),
            """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
            """num_beams""": np.array(_lowerCamelCase	),
            """max_length""": np.array(_lowerCamelCase	),
            """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 							SCREAMING_SNAKE_CASE     (			)       ->      Any:
  _lowerCAmelCase						:       Any  			=     parse_args()
  _lowerCAmelCase						:       List[Any]  			=     5
  _lowerCAmelCase						:       str  			=     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()

  _lowerCAmelCase						:       Optional[Any]  			=     torch.device(args.device	)

  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     load_model_tokenizer(args.model_name_or_path			,_lowerCamelCase	)

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

  model.to(_lowerCamelCase	)

  if args.max_length:
    _lowerCAmelCase						:       Dict  			=     args.max_length

  if args.num_beams:
    _lowerCAmelCase						:       Dict  			=     args.num_beams

  if args.output_file_path:
    _lowerCAmelCase						:       Any  			=     args.output_file_path
  else:
    _lowerCAmelCase						:       Union[str, Any]  			=     """BART.onnx"""

  logger.info("""Exporting model to ONNX"""	)
  export_and_validate_model(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)


if __name__ == "__main__":
     main()




