




import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		from .features import FeatureType


_SCREAMING_SNAKE_CASE							:       str						=			False, False, False




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				Optional[int]							=   None
			lowerCAmelCase_					:				bool							=   True
			lowerCAmelCase_					:				bool							=   True
			lowerCAmelCase_					:				Optional[str]							=   None
			# Automatically constructed
			lowerCAmelCase_					:				ClassVar[str]							=   "dict"
			lowerCAmelCase_					:				ClassVar[Any]							=   pa.struct({"bytes": pa.binary(), "path": pa.string()}							)
			lowerCAmelCase_					:				str							=   field(default="Audio"						,						init=_lowerCAmelCase						,						repr=_lowerCAmelCase							)




			def __call__( self							)    ->    List[Any]:

										'''simple docstring'''




										return self.pa_type




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    str:

										'''simple docstring'''




										try:
																	import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
										except ImportError as err:
																	raise ImportError("To support encoding audio data, please install \'soundfile\'."							) from err
										if isinstance(_lowercase    ,							_lowercase							):
																	return {"bytes": None, "path": value}
										elif isinstance(_lowercase    ,							_lowercase							):
																	return {"bytes": value, "path": None}
										elif "array" in value:
																	# convert the audio array to wav bytes
																	snake_case_      =				BytesIO()
																	sf.write(_lowercase    ,							value["array"]    ,							value["sampling_rate"]    ,							format="wav"							)
																	return {"bytes": buffer.getvalue(), "path": None}
										elif value.get("path"							) is not None and os.path.isfile(value["path"]							):
																	# we set "bytes": None to not duplicate the data if they're already available locally
																	if value["path"].endswith("pcm"							):
																								# "PCM" only has raw audio bytes
																								if value.get("sampling_rate"							) is None:
																															# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																															raise KeyError("To use PCM files, please specify a \'sampling_rate\' in Audio object"							)
																								if value.get("bytes"							):
																															# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																															snake_case_      =				np.frombuffer(value["bytes"]    ,							dtype=np.intaa							).astype(np.floataa							) / 32_767
																								else:
																															snake_case_      =				np.memmap(value["path"]    ,							dtype="h"    ,							mode="r"							).astype(np.floataa							) / 32_767

																								snake_case_      =				BytesIO(bytes()							)
																								sf.write(_lowercase    ,							_lowercase    ,							value["sampling_rate"]    ,							format="wav"							)
																								return {"bytes": buffer.getvalue(), "path": None}
																	else:
																								return {"bytes": None, "path": value.get("path"							)}
										elif value.get("bytes"							) is not None or value.get("path"							) is not None:
																	# store the audio bytes, and path is used to infer the audio format using the file extension
																	return {"bytes": value.get("bytes"							), "path": value.get("path"							)}
										else:
																	raise ValueError(
																	    F'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    str:

										'''simple docstring'''




										if not self.decode:
																	raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead."							)

										snake_case_ ,				snake_case_      =				(value["path"], BytesIO(value["bytes"]							)) if value["bytes"] is not None else (value["path"], None)
										if path is None and file is None:
																	raise ValueError(F'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'							)

										try:
																	import librosa
																	import soundfile as sf
										except ImportError as err:
																	raise ImportError("To support decoding audio files, please install \'librosa\' and \'soundfile\'."							) from err

										snake_case_      =				xsplitext(_lowercase							)[1][1:].lower() if path is not None else None
										if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
																	raise RuntimeError(
																	    "Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, "
																	    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "							)
										elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
																	raise RuntimeError(
																	    "Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, "
																	    "You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. "							)

										if file is None:
																	snake_case_      =				token_per_repo_id or {}
																	snake_case_      =				path.split("::"							)[-1]
																	try:
																								snake_case_      =				string_to_dict(_lowercase    ,							config.HUB_DATASETS_URL							)["repo_id"]
																								snake_case_      =				token_per_repo_id[repo_id]
																	except (ValueError, KeyError):
																								snake_case_      =				None

																	with xopen(_lowercase    ,							"rb"    ,							use_auth_token=_lowercase							) as f:
																								snake_case_ ,				snake_case_      =				sf.read(_lowercase							)

										else:
																	snake_case_ ,				snake_case_      =				sf.read(_lowercase							)

										snake_case_      =				array.T
										if self.mono:
																	snake_case_      =				librosa.to_mono(_lowercase							)
										if self.sampling_rate and self.sampling_rate != sampling_rate:
																	snake_case_      =				librosa.resample(_lowercase    ,							orig_sr=_lowercase    ,							target_sr=self.sampling_rate							)
																	snake_case_      =				self.sampling_rate

										return {"path": path, "array": array, "sampling_rate": sampling_rate}




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										from .features import Value

										if self.decode:
																	raise ValueError("Cannot flatten a decoded Audio feature."							)
										return {
										    "bytes": Value("binary"							),
										    "path": Value("string"							),
										}




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    str:

										'''simple docstring'''




										if pa.types.is_string(storage.type							):
																	snake_case_      =				pa.array([None] * len(_lowercase							)    ,							type=pa.binary()							)
																	snake_case_      =				pa.StructArray.from_arrays([bytes_array, storage]    ,							["bytes", "path"]    ,							mask=storage.is_null()							)
										elif pa.types.is_binary(storage.type							):
																	snake_case_      =				pa.array([None] * len(_lowercase							)    ,							type=pa.string()							)
																	snake_case_      =				pa.StructArray.from_arrays([storage, path_array]    ,							["bytes", "path"]    ,							mask=storage.is_null()							)
										elif pa.types.is_struct(storage.type							) and storage.type.get_all_field_indices("array"							):
																	snake_case_      =				pa.array([Audio().encode_example(_lowercase							) if x is not None else None for x in storage.to_pylist()]							)
										elif pa.types.is_struct(storage.type							):
																	if storage.type.get_field_index("bytes"							) >= 0:
																								snake_case_      =				storage.field("bytes"							)
																	else:
																								snake_case_      =				pa.array([None] * len(_lowercase							)    ,							type=pa.binary()							)
																	if storage.type.get_field_index("path"							) >= 0:
																								snake_case_      =				storage.field("path"							)
																	else:
																								snake_case_      =				pa.array([None] * len(_lowercase							)    ,							type=pa.string()							)
																	snake_case_      =				pa.StructArray.from_arrays([bytes_array, path_array]    ,							["bytes", "path"]    ,							mask=storage.is_null()							)
										return array_cast(_lowercase    ,							self.pa_type							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

										'''simple docstring'''




										@no_op_if_value_is_null
										def path_to_bytes(a__							):
																	with xopen(_lowercase    ,							"rb"							) as f:
																								snake_case_      =				f.read()
																	return bytes_

										snake_case_      =				pa.array(
										    [
										        (path_to_bytes(x["path"]							) if x["bytes"] is None else x["bytes"]) if x is not None else None
										        for x in storage.to_pylist()
										    ]    ,							type=pa.binary()    ,							)
										snake_case_      =				pa.array(
										    [os.path.basename(_lowercase							) if path is not None else None for path in storage.field("path"							).to_pylist()]    ,							type=pa.string()    ,							)
										snake_case_      =				pa.StructArray.from_arrays([bytes_array, path_array]    ,							["bytes", "path"]    ,							mask=bytes_array.is_null()							)
										return array_cast(_lowercase    ,							self.pa_type							)










'''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,
)

_SCREAMING_SNAKE_CASE							:       Dict						=			logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE							:       List[str]						=			{"facebook/bart-base": BartForConditionalGeneration}
_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			{"facebook/bart-base": BartTokenizer}


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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

							snake_case_      =				parser.parse_args()

							return args


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]							,    snake_case							:	List[str]="cpu"					):

							'''simple docstring'''






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

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

							return huggingface_model, tokenizer


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	Tuple							,    snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							model.eval()

							snake_case_      =				None
							snake_case_      =				torch.jit.script(BARTBeamSearchGenerator(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=1_0_2_4							,    return_tensors="pt"					).to(model.device					)

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

														torch.onnx.export(
														    snake_case							,    (
														        inputs["input_ids"],
														        inputs["attention_mask"],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )							,    snake_case							,    opset_version=1_4							,    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=snake_case							,    )

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

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

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

														snake_case_      =				onnxruntime.InferenceSession(snake_case					)
														snake_case_      =				ort_sess.run(
														    snake_case							,    {
														        "input_ids": inputs["input_ids"].cpu().numpy(),
														        "attention_mask": inputs["attention_mask"].cpu().numpy(),
														        "num_beams": np.array(snake_case					),
														        "max_length": np.array(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 					UpperCamelCase_(     ):

							'''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							,    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(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(snake_case							,    snake_case							,    snake_case							,    snake_case							,    snake_case					)


if __name__ == "__main__":
		main()




