


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  __lowerCAmelCase					()->						Optional[Any]:




      """simple docstring"""

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

      snake_case_ =   parser.parse_args()

      return args






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE="cpu"      )->						List[str]:




      """simple docstring"""

      snake_case_ =   model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )
      snake_case_ =   tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE      )

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

      return huggingface_model, tokenizer






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      model.eval()

      snake_case_ =   None
      snake_case_ =   torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE      )      )

      with torch.no_grad():
            snake_case_ =   '''My friends are cool but they eat too many carbs.'''
            snake_case_ =   tokenizer([ARTICLE_TO_SUMMARIZE]      ,      max_length=1024      ,      return_tensors='''pt'''      ).to(model.device      )

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

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

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

            snake_case_ =   remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE      )      )

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

            snake_case_ =   onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE      )
            snake_case_ =   ort_sess.run(
                SCREAMING_SNAKE_CASE      ,      {
                    '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                    '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                    '''num_beams''': np.array(SCREAMING_SNAKE_CASE      ),
                    '''max_length''': np.array(SCREAMING_SNAKE_CASE      ),
                    '''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  __lowerCAmelCase					()->						str:




      """simple docstring"""

      snake_case_ =   parse_args()
      snake_case_ =   5
      snake_case_ =   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()

      snake_case_ =   torch.device(args.device      )

      snake_case_ ,					snake_case_ =   load_model_tokenizer(args.model_name_or_path      ,      SCREAMING_SNAKE_CASE      )

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

      model.to(SCREAMING_SNAKE_CASE      )

      if args.max_length:
            snake_case_ =   args.max_length

      if args.num_beams:
            snake_case_ =   args.num_beams

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

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


if __name__ == "__main__":
   main()



# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


UpperCAmelCase    =					"""pytorch_model.bin"""
UpperCAmelCase    =					"""pytorch_model.bin.index.json"""
UpperCAmelCase    =					"""adapter_config.json"""
UpperCAmelCase    =					"""adapter_model.bin"""
UpperCAmelCase    =					"""adapter_model.safetensors"""
UpperCAmelCase    =					"""tf_model.h5"""
UpperCAmelCase    =					"""tf_model.h5.index.json"""
UpperCAmelCase    =					"""model.ckpt"""
UpperCAmelCase    =					"""flax_model.msgpack"""
UpperCAmelCase    =					"""flax_model.msgpack.index.json"""
UpperCAmelCase    =					"""model.safetensors"""
UpperCAmelCase    =					"""model.safetensors.index.json"""
UpperCAmelCase    =					"""config.json"""
UpperCAmelCase    =					"""preprocessor_config.json"""
UpperCAmelCase    =					FEATURE_EXTRACTOR_NAME
UpperCAmelCase    =					"""generation_config.json"""
UpperCAmelCase    =					"""modelcard.json"""

UpperCAmelCase    =					"""▁"""
UpperCAmelCase    =					SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

UpperCAmelCase    =					[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCAmelCase    =					[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCAmelCase    =					[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[str]:




      """simple docstring"""

      if version.parse(SCREAMING_SNAKE_CASE      ) < version.parse(SCREAMING_SNAKE_CASE      ):
            if "dev" in min_version:
                  snake_case_ =   (
                      '''This example requires a source install from HuggingFace Transformers (see '''
                      '''`https://huggingface.co/docs/transformers/installation#install-from-source`),'''
                  )
            else:
                  snake_case_ =   f'''This example requires a minimum version of {min_version},'''
            error_message += f''' but the version found is {__version__}.\n'''
            raise ImportError(
                error_message
                + '''Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '''
                '''versions of HuggingFace Transformers.'''      )