




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      (						):
					__lowerCAmelCase				   =       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."     )

					__lowerCAmelCase				   =       parser.parse_args()

					return args







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]="cpu"     ):
					__lowerCAmelCase				   =       model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_     ).to(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_     )

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

					return huggingface_model, tokenizer







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					model.eval()

					__lowerCAmelCase				   =       None
					__lowerCAmelCase				   =       torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE_     )     )

					with torch.no_grad():
										__lowerCAmelCase				   =       "My friends are cool but they eat too many carbs."
										__lowerCAmelCase				   =       tokenizer([ARTICLE_TO_SUMMARIZE]      ,      max_length=10_24      ,      return_tensors="pt"     ).to(model.device     )

										__lowerCAmelCase				   =       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_     )     )

										__lowerCAmelCase				   =       remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE_     )     )

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

										__lowerCAmelCase				   =       onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       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 	_a      (						):
					__lowerCAmelCase				   =       parse_args()
					__lowerCAmelCase				   =       5
					__lowerCAmelCase				   =       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				   =       torch.device(args.device     )

					__lowerCAmelCase   ,							__lowerCAmelCase				   =       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:
										__lowerCAmelCase				   =       args.max_length

					if args.num_beams:
										__lowerCAmelCase				   =       args.num_beams

					if args.output_file_path:
										__lowerCAmelCase				   =       args.output_file_path
					else:
										__lowerCAmelCase				   =       "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()










import argparse
import os
import re

import packaging.version


UpperCamelCase__    			=			"""examples/"""
UpperCamelCase__    			=			{
    """examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
    """doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCamelCase__    			=			{
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
UpperCamelCase__    			=			"""README.md"""







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					with open(SCREAMING_SNAKE_CASE_      ,      "r"      ,      encoding="utf-8"      ,      newline="\n"     ) as f:
										__lowerCAmelCase				   =       f.read()
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       REPLACE_PATTERNS[pattern]
					__lowerCAmelCase				   =       replace.replace("VERSION"      ,      SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       re_pattern.sub(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					with open(SCREAMING_SNAKE_CASE_      ,      "w"      ,      encoding="utf-8"      ,      newline="\n"     ) as f:
										f.write(SCREAMING_SNAKE_CASE_     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					for folder, directories, fnames in os.walk(SCREAMING_SNAKE_CASE_     ):
										# Removing some of the folders with non-actively maintained examples from the walk
										if "research_projects" in directories:
															directories.remove("research_projects"     )
										if "legacy" in directories:
															directories.remove("legacy"     )
										for fname in fnames:
															if fname.endswith(".py"     ):
																				update_version_in_file(os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )      ,      SCREAMING_SNAKE_CASE_      ,      pattern="examples"     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]=False     ):
					for pattern, fname in REPLACE_FILES.items():
										update_version_in_file(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					if not patch:
										update_version_in_examples(SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					__lowerCAmelCase				   =       "🤗 Transformers currently provides the following architectures"
					__lowerCAmelCase				   =       "1. Want to contribute a new model?"
					with open(SCREAMING_SNAKE_CASE_      ,      "r"      ,      encoding="utf-8"      ,      newline="\n"     ) as f:
										__lowerCAmelCase				   =       f.readlines()

					# Find the start of the list.
					__lowerCAmelCase				   =       0
					while not lines[start_index].startswith(_start_prompt     ):
										start_index += 1
					start_index += 1

					__lowerCAmelCase				   =       start_index
					# Update the lines in the model list.
					while not lines[index].startswith(_end_prompt     ):
										if lines[index].startswith("1."     ):
															__lowerCAmelCase				   =       lines[index].replace(
															    "https://huggingface.co/docs/transformers/main/model_doc"      ,      "https://huggingface.co/docs/transformers/model_doc"      ,      )
										index += 1

					with open(SCREAMING_SNAKE_CASE_      ,      "w"      ,      encoding="utf-8"      ,      newline="\n"     ) as f:
										f.writelines(SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					with open(REPLACE_FILES["init"]      ,      "r"     ) as f:
										__lowerCAmelCase				   =       f.read()
					__lowerCAmelCase				   =       REPLACE_PATTERNS["init"][0].search(SCREAMING_SNAKE_CASE_     ).groups()[0]
					return packaging.version.parse(SCREAMING_SNAKE_CASE_     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]=False     ):
					__lowerCAmelCase				   =       get_version()
					if patch and default_version.is_devrelease:
										raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"     )
					if default_version.is_devrelease:
										__lowerCAmelCase				   =       default_version.base_version
					elif patch:
										__lowerCAmelCase				   =       F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
					else:
										__lowerCAmelCase				   =       F"""{default_version.major}.{default_version.minor + 1}.0"""

					# Now let's ask nicely if that's the right one.
					__lowerCAmelCase				   =       input(F"""Which version are you releasing? [{default_version}]"""     )
					if len(SCREAMING_SNAKE_CASE_     ) == 0:
										__lowerCAmelCase				   =       default_version

					print(F"""Updating version to {version}."""     )
					global_version_update(SCREAMING_SNAKE_CASE_      ,      patch=SCREAMING_SNAKE_CASE_     )
					if not patch:
										print("Cleaning main README, don't forget to run `make fix-copies`."     )
										clean_main_ref_in_model_list()







def 	_a      (						):
					__lowerCAmelCase				   =       get_version()
					__lowerCAmelCase				   =       F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
					__lowerCAmelCase				   =       current_version.base_version

					# Check with the user we got that right.
					__lowerCAmelCase				   =       input(F"""Which version are we developing now? [{dev_version}]"""     )
					if len(SCREAMING_SNAKE_CASE_     ) == 0:
										__lowerCAmelCase				   =       dev_version

					print(F"""Updating version to {version}."""     )
					global_version_update(SCREAMING_SNAKE_CASE_     )
					print("Cleaning main README, don't forget to run `make fix-copies`."     )
					clean_main_ref_in_model_list()


if __name__ == "__main__":
					UpperCamelCase__    			=			argparse.ArgumentParser()
					parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
					parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
					UpperCamelCase__    			=			parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print("""Nothing to do after a patch :-)""")
					else:
										post_release_work()




