






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


snake_case_   :	List[str]							      ='''1.5.0'''

snake_case_   :	List[str]							      =['''vision-encoder-decoder''']



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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

				__A    =							version.parse(lowerCAmelCase__	)
				if parsed_optimum_version < version.parse(lowerCAmelCase__	):
						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]"	)
		__A    =							[
		    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}""",
		]
		__A    =							subprocess.Popen(" ".join(lowerCAmelCase__	)					,     stdout=subprocess.PIPE					,     shell=lowerCAmelCase__	)
		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						( lowerCAmelCase__	):


		'''simple docstring'''

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

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

		__A ,   __A    =							FeaturesManager.check_supported_model_or_raise(lowerCAmelCase__					,     feature=args.feature	)
		__A    =							model_onnx_config(model.config	)

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

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

				if args.opset is None:
						__A    =							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."""	)

				__A    =							AutoFeatureExtractor.from_pretrained(args.model	)

				__A ,   __A    =							export(
				    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     args.opset					,     args.output.parent.joinpath("encoder_model.onnx"	)					,     )

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

				__A    =							AutoTokenizer.from_pretrained(args.model	)

				__A ,   __A    =							export(
				    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     args.opset					,     args.output.parent.joinpath("decoder_model.onnx"	)					,     )

				validate_model_outputs(
				    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     args.output.parent.joinpath("decoder_model.onnx"	)					,     lowerCAmelCase__					,     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":
						__A    =							get_preprocessor(args.model	)
				elif args.preprocessor == "tokenizer":
						__A    =							AutoTokenizer.from_pretrained(args.model	)
				elif args.preprocessor == "image_processor":
						__A    =							AutoImageProcessor.from_pretrained(args.model	)
				elif args.preprocessor == "feature_extractor":
						__A    =							AutoFeatureExtractor.from_pretrained(args.model	)
				elif args.preprocessor == "processor":
						__A    =							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:
						__A    =							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."""	)

				__A ,   __A    =							export(
				    lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     args.opset					,     args.output					,     )

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

				validate_model_outputs(lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     args.output					,     lowerCAmelCase__					,     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."					,     lowerCAmelCase__					,     )



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							ArgumentParser("Hugging Face Transformers ONNX exporter"	)
		parser.add_argument(
		    "-m"					,     "--model"					,     type=lowerCAmelCase__					,     required=lowerCAmelCase__					,     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=lowerCAmelCase__					,     default=lowerCAmelCase__					,     help="ONNX opset version to export the model with."	)
		parser.add_argument(
		    "--atol"					,     type=lowerCAmelCase__					,     default=lowerCAmelCase__					,     help="Absolute difference tolerance when validating the model."	)
		parser.add_argument(
		    "--framework"					,     type=lowerCAmelCase__					,     choices=["pt", "tf"]					,     default=lowerCAmelCase__					,     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=lowerCAmelCase__					,     help="Path indicating where to store generated ONNX model."	)
		parser.add_argument("--cache_dir"					,     type=lowerCAmelCase__					,     default=lowerCAmelCase__					,     help="Path indicating where to store cache."	)
		parser.add_argument(
		    "--preprocessor"					,     type=lowerCAmelCase__					,     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."
		    )					,     )

		__A    =							parser.parse_args()
		if args.export_with_transformers or not is_optimum_available():
				export_with_transformers(lowerCAmelCase__	)
		else:
				export_with_optimum(lowerCAmelCase__	)


if __name__ == "__main__":
					snake_case_   :	List[Any]							      =logging.get_logger('''transformers.onnx''')  # pylint: disable=invalid-name
					logger.setLevel(logging.INFO)
					main()









import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SegformerImageProcessor





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=False							, )				->				Tuple:
				__A    =							size if size is not None else {"height": 30, "width": 30}
				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_reduce_labels






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_reduce_labels": self.do_reduce_labels,
				}



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)

		return image, map



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							load_dataset("hf-internal-testing/fixtures_ade20k"					,     split="test"	)

		__A    =							Image.open(dataset[0]["file"]	)
		__A    =							Image.open(dataset[1]["file"]	)
		__A    =							Image.open(dataset[2]["file"]	)
		__A    =							Image.open(dataset[3]["file"]	)

		return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class        a__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Dict 					= SegformerImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							SegformerImageProcessingTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				str:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_reduce_labels"						)						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size							, {"height": 30, "width": 30}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)

				__A    =							self.image_processing_class.from_dict(self.image_processor_dict							, size=42							, reduce_labels=lowercase__						)
				self.assertEqual(image_processor.size							, {"height": 42, "width": 42}						)
				self.assertEqual(image_processor.do_reduce_labels							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				pass






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, Image.Image						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, numpify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, np.ndarray						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )

				# Test batched
				__A    =							image_processing(lowercase__							, return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )






		def 					_lowerCamelCase					(      self						)				->				Dict:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_image_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				__A    =							[]
				for image in image_inputs:
						self.assertIsInstance(lowercase__							, torch.Tensor						)
						maps.append(torch.zeros(image.shape[-2:]						).long()						)

				# Test not batched input
				__A    =							image_processing(image_inputs[0]							, maps[0]							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test not batched input (PIL images)
				__A ,   __A    =							prepare_semantic_single_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        1,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

				# Test batched input (PIL images)
				__A ,   __A    =							prepare_semantic_batch_inputs()

				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertEqual(
				    encoding["pixel_values"].shape							, (
				        2,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(
				    encoding["labels"].shape							, (
				        2,
				        self.image_processor_tester.size["height"],
				        self.image_processor_tester.size["width"],
				    )							, )
				self.assertEqual(encoding["labels"].dtype							, torch.long						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				# Initialize image_processing
				__A    =							self.image_processing_class(**self.image_processor_dict						)

				# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
				__A ,   __A    =							prepare_semantic_single_inputs()
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 150						)

				__A    =							True
				__A    =							image_processing(lowercase__							, lowercase__							, return_tensors="pt"						)
				self.assertTrue(encoding["labels"].min().item() >= 0						)
				self.assertTrue(encoding["labels"].max().item() <= 255						)

