



import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


A__       =			"""1.5.0"""

A__       =			["""vision-encoder-decoder"""]


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					if is_optimum_available():
										from optimum.version import __version__ as optimum_version

										_lowerCAmelCase         =       version.parse(snake_case     )
										if parsed_optimum_version < version.parse(snake_case     ):
															raise RuntimeError(
															    F'transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You '
															    """can upgrade optimum by running: pip install -U optimum[exporters]"""     )
					else:
										raise RuntimeError(
										    """transformers.onnx requires optimum to run, you can install the library by running: pip install """
										    """optimum[exporters]"""     )
					_lowerCAmelCase         =       [
					    sys.executable,
					    """-m""",
					    """optimum.exporters.onnx""",
					    F'--model {args.model}',
					    F'--task {args.feature}',
					    F'--framework {args.framework}' if args.framework is not None else """""",
					    F'{args.output}',
					]
					_lowerCAmelCase         =       subprocess.Popen(""" """.join(snake_case     )      ,		stdout=subprocess.PIPE      ,		shell=snake_case     )
					proc.wait()

					logger.info(
					    """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
					    """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
					    """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""     )


def 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""     )
					if not args.output.parent.exists():
										args.output.parent.mkdir(parents=snake_case     )

					# Allocate the model
					_lowerCAmelCase         =       FeaturesManager.get_model_from_feature(
					    args.feature      ,		args.model      ,		framework=args.framework      ,		cache_dir=args.cache_dir     )

					_lowerCAmelCase      ,      _lowerCAmelCase         =       FeaturesManager.check_supported_model_or_raise(snake_case      ,		feature=args.feature     )
					_lowerCAmelCase         =       model_onnx_config(model.config     )

					if model_kind in ENCODER_DECODER_MODELS:
										_lowerCAmelCase         =       model.get_encoder()
										_lowerCAmelCase         =       model.get_decoder()

										_lowerCAmelCase         =       onnx_config.get_encoder_config(encoder_model.config     )
										_lowerCAmelCase         =       onnx_config.get_decoder_config(
										    encoder_model.config      ,		decoder_model.config      ,		feature=args.feature     )

										if args.opset is None:
															_lowerCAmelCase         =       max(encoder_onnx_config.default_onnx_opset      ,		decoder_onnx_config.default_onnx_opset     )

										if args.opset < min(encoder_onnx_config.default_onnx_opset      ,		decoder_onnx_config.default_onnx_opset     ):
															raise ValueError(
															    F'Opset {args.opset} is not sufficient to export {model_kind}. At least '
															    F' {min(encoder_onnx_config.default_onnx_opset      ,		decoder_onnx_config.default_onnx_opset     )} is required.'     )

										_lowerCAmelCase         =       AutoFeatureExtractor.from_pretrained(args.model     )

										_lowerCAmelCase      ,      _lowerCAmelCase         =       export(
										    snake_case      ,		snake_case      ,		snake_case      ,		args.opset      ,		args.output.parent.joinpath("""encoder_model.onnx"""     )      ,		)

										validate_model_outputs(
										    snake_case      ,		snake_case      ,		snake_case      ,		args.output.parent.joinpath("""encoder_model.onnx"""     )      ,		snake_case      ,		args.atol if args.atol else encoder_onnx_config.atol_for_validation      ,		)

										_lowerCAmelCase         =       AutoTokenizer.from_pretrained(args.model     )

										_lowerCAmelCase      ,      _lowerCAmelCase         =       export(
										    snake_case      ,		snake_case      ,		snake_case      ,		args.opset      ,		args.output.parent.joinpath("""decoder_model.onnx"""     )      ,		)

										validate_model_outputs(
										    snake_case      ,		snake_case      ,		snake_case      ,		args.output.parent.joinpath("""decoder_model.onnx"""     )      ,		snake_case      ,		args.atol if args.atol else decoder_onnx_config.atol_for_validation      ,		)
										logger.info(
										    F'All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"     ).as_posix()},'
										    F' {args.output.parent.joinpath("decoder_model.onnx"     ).as_posix()}'     )

					else:
										# Instantiate the appropriate preprocessor
										if args.preprocessor == "auto":
															_lowerCAmelCase         =       get_preprocessor(args.model     )
										elif args.preprocessor == "tokenizer":
															_lowerCAmelCase         =       AutoTokenizer.from_pretrained(args.model     )
										elif args.preprocessor == "image_processor":
															_lowerCAmelCase         =       AutoImageProcessor.from_pretrained(args.model     )
										elif args.preprocessor == "feature_extractor":
															_lowerCAmelCase         =       AutoFeatureExtractor.from_pretrained(args.model     )
										elif args.preprocessor == "processor":
															_lowerCAmelCase         =       AutoProcessor.from_pretrained(args.model     )
										else:
															raise ValueError(F'Unknown preprocessor type \'{args.preprocessor}\''     )

										# Ensure the requested opset is sufficient
										if args.opset is None:
															_lowerCAmelCase         =       onnx_config.default_onnx_opset

										if args.opset < onnx_config.default_onnx_opset:
															raise ValueError(
															    F'Opset {args.opset} is not sufficient to export {model_kind}. '
															    F'At least  {onnx_config.default_onnx_opset} is required.'     )

										_lowerCAmelCase      ,      _lowerCAmelCase         =       export(
										    snake_case      ,		snake_case      ,		snake_case      ,		args.opset      ,		args.output      ,		)

										if args.atol is None:
															_lowerCAmelCase         =       onnx_config.atol_for_validation

										validate_model_outputs(snake_case      ,		snake_case      ,		snake_case      ,		args.output      ,		snake_case      ,		args.atol     )
										logger.info(F'All good, model saved at: {args.output.as_posix()}'     )
										warnings.warn(
										    """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
										    """ using optimum.exporters.onnx in future. You can find more information here:"""
										    """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""      ,		snake_case      ,		)


def 						_UpperCAmelCase							( ):





					"""simple docstring"""

					_lowerCAmelCase         =       ArgumentParser("""Hugging Face Transformers ONNX exporter"""     )
					parser.add_argument(
					    """-m"""      ,		"""--model"""      ,		type=snake_case      ,		required=snake_case      ,		help="""Model ID on huggingface.co or path on disk to load model from."""     )
					parser.add_argument(
					    """--feature"""      ,		default="""default"""      ,		help="""The type of features to export the model with."""      ,		)
					parser.add_argument("""--opset"""      ,		type=snake_case      ,		default=snake_case      ,		help="""ONNX opset version to export the model with."""     )
					parser.add_argument(
					    """--atol"""      ,		type=snake_case      ,		default=snake_case      ,		help="""Absolute difference tolerance when validating the model."""     )
					parser.add_argument(
					    """--framework"""      ,		type=snake_case      ,		choices=["""pt""", """tf"""]      ,		default=snake_case      ,		help=(
					        """The framework to use for the ONNX export."""
					        """ If not provided, will attempt to use the local checkpoint's original framework"""
					        """ or what is available in the environment."""
					    )      ,		)
					parser.add_argument("""output"""      ,		type=snake_case      ,		help="""Path indicating where to store generated ONNX model."""     )
					parser.add_argument("""--cache_dir"""      ,		type=snake_case      ,		default=snake_case      ,		help="""Path indicating where to store cache."""     )
					parser.add_argument(
					    """--preprocessor"""      ,		type=snake_case      ,		choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]      ,		default="""auto"""      ,		help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""      ,		)
					parser.add_argument(
					    """--export_with_transformers"""      ,		action="""store_true"""      ,		help=(
					        """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
					        """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
					        """recommended."""
					    )      ,		)

					_lowerCAmelCase         =       parser.parse_args()
					if args.export_with_transformers or not is_optimum_available():
										export_with_transformers(snake_case     )
					else:
										export_with_optimum(snake_case     )


if __name__ == "__main__":
	A__       =			logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
	logger.setLevel(logging.INFO)
	main()





import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


SCREAMING_SNAKE_CASE__				=							datasets.utils.logging.get_logger(__name__)
@dataclass
class       A__  (		datasets.BuilderConfig							):
							lowerCAmelCase__						:		Optional[datasets.Features]            =				None
							lowerCAmelCase__						:		str            =				"utf-8"
							lowerCAmelCase__						:		Optional[str]            =				None
							lowerCAmelCase__						:		int            =				10 << 20  # 10MB
							lowerCAmelCase__						:		bool            =				False
							lowerCAmelCase__						:		str            =				"line"






class       A__  (		datasets.ArrowBasedBuilder							):
							lowerCAmelCase__						:		Optional[int]            =				TextConfig





							def      a__					(					self    :  List[str]							)		->						Any:



											"""simple docstring"""


											return datasets.DatasetInfo(features=self.config.features							)





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  str							)		->						List[str]:



											"""simple docstring"""


											if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
											__lowercase								=   dl_manager.download_and_extract(self.config.data_files							)
											if isinstance(_UpperCAmelCase	,					(str, list, tuple)							):
															__lowercase								=   data_files
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,					gen_kwargs={'files': files}							)]
											__lowercase								=   []
											for split_name, files in data_files.items():
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															splits.append(datasets.SplitGenerator(name=_UpperCAmelCase	,					gen_kwargs={'files': files}							)							)
											return splits





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pa.Table							)		->						pa.Table:



											"""simple docstring"""


											if self.config.features is not None:
															__lowercase								=   self.config.features.arrow_schema
															if all(not require_storage_cast(_UpperCAmelCase							) for feature in self.config.features.values()							):
																			# cheaper cast
																			__lowercase								=   pa_table.cast(_UpperCAmelCase							)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			__lowercase								=   table_cast(_UpperCAmelCase	,					_UpperCAmelCase							)
															return pa_table
											else:
															return pa_table.cast(pa.schema({'text': pa.string()}							)							)





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]							)		->						List[Any]:



											"""simple docstring"""


											__lowercase								=   list(self.config.features							) if self.config.features is not None else ['text']
											for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase							)							):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(_UpperCAmelCase	,					encoding=self.config.encoding	,					errors=self.config.errors							) as f:
																			if self.config.sample_by == "line":
																							__lowercase								=   0
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not batch:
																															break
																											batch += f.readline()  # finish current line
																											# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																											__lowercase								=   StringIO(_UpperCAmelCase							).readlines()
																											if not self.config.keep_linebreaks:
																															__lowercase								=   [line.rstrip('\n'							) for line in batch]
																											__lowercase								=   pa.Table.from_arrays([pa.array(_UpperCAmelCase							)]	,					names=_UpperCAmelCase							)
																											# Uncomment for debugging (will print the Arrow table size and elements)
																											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																											batch_idx += 1
																			elif self.config.sample_by == "paragraph":
																							__lowercase								=   0
																							__lowercase								=   ''
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not new_batch:
																															break
																											batch += new_batch
																											batch += f.readline()  # finish current line
																											__lowercase								=   batch.split('\n\n'							)
																											__lowercase								=   pa.Table.from_arrays(
																											    [pa.array([example for example in batch[:-1] if example]							)]	,					names=_UpperCAmelCase							)
																											# Uncomment for debugging (will print the Arrow table size and elements)
																											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																											batch_idx += 1
																											__lowercase								=   batch[-1]
																							if batch:
																											__lowercase								=   pa.Table.from_arrays([pa.array([batch]							)]	,					names=_UpperCAmelCase							)
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																			elif self.config.sample_by == "document":
																							__lowercase								=   f.read()
																							__lowercase								=   pa.Table.from_arrays([pa.array([text]							)]	,					names=_UpperCAmelCase							)
																							yield file_idx, self._cast_table(_UpperCAmelCase							)





