

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor

class __UpperCamelCase    :






					'''simple docstring'''



					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=1_3							,			lowerCamelCase__=3_2							,			lowerCamelCase__=3							,			lowerCamelCase__=4							,			lowerCamelCase__=[1_0, 2_0, 3_0, 4_0]							,			lowerCamelCase__=[2, 2, 3, 2]							,			lowerCamelCase__=True							,			lowerCamelCase__=True							,			lowerCamelCase__=3_7							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=1_0							,			lowerCamelCase__=0.02							,			lowerCamelCase__=3							,			lowerCamelCase__=None							,			):
						UpperCAmelCase__:  Union[str, Any]		       =   parent
						UpperCAmelCase__:  List[Any]		       =   batch_size
						UpperCAmelCase__:  Optional[int]		       =   image_size
						UpperCAmelCase__:  Optional[Any]		       =   num_channels
						UpperCAmelCase__:  Tuple		       =   num_stages
						UpperCAmelCase__:  List[str]		       =   hidden_sizes
						UpperCAmelCase__:  int		       =   depths
						UpperCAmelCase__:  Dict		       =   is_training
						UpperCAmelCase__:  Union[str, Any]		       =   use_labels
						UpperCAmelCase__:  int		       =   intermediate_size
						UpperCAmelCase__:  int		       =   hidden_act
						UpperCAmelCase__:  List[Any]		       =   type_sequence_label_size
						UpperCAmelCase__:  Optional[Any]		       =   initializer_range
						UpperCAmelCase__:  Dict		       =   scope





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

						UpperCAmelCase__:  Optional[int]		       =   None
						if self.use_labels:
							UpperCAmelCase__:  Union[str, Any]		       =   ids_tensor([self.batch_size]							,			self.type_sequence_label_size	)

						UpperCAmelCase__:  Dict		       =   self.get_config()

						return config, pixel_values, labels





					def       _UpperCAmelCase						(							self	):
						return ConvNextConfig(
						    num_channels=self.num_channels							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			num_stages=self.num_stages							,			hidden_act=self.hidden_act							,			is_decoder=lowerCamelCase__							,			initializer_range=self.initializer_range							,			)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
						UpperCAmelCase__:  Union[str, Any]		       =   TFConvNextModel(config=lowerCamelCase__	)
						UpperCAmelCase__:  Tuple		       =   model(lowerCamelCase__							,			training=lowerCamelCase__	)
						# expected last hidden states: B, C, H // 32, W // 32
						self.parent.assertEqual(
						    result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,			)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
						UpperCAmelCase__:  str		       =   self.type_sequence_label_size
						UpperCAmelCase__:  Any		       =   TFConvNextForImageClassification(lowerCamelCase__	)
						UpperCAmelCase__:  str		       =   model(lowerCamelCase__							,			labels=lowerCamelCase__							,			training=lowerCamelCase__	)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   self.prepare_config_and_inputs()
						UpperCAmelCase__:  List[str]		       =   config_and_inputs
						UpperCAmelCase__:  Dict		       =   {"pixel_values": pixel_values}
						return config, inputs_dict

@require_tf
class __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):






					'''simple docstring'''



					__magic_name__     			=      (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					__magic_name__     			=      (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					__magic_name__     			=      False
					__magic_name__     			=      False
					__magic_name__     			=      False
					__magic_name__     			=      False
					__magic_name__     			=      False





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[str]		       =   TFConvNextModelTester(self	)
						UpperCAmelCase__:  Optional[Any]		       =   ConfigTester(
						    self							,			config_class=lowerCamelCase__							,			has_text_modality=lowerCamelCase__							,			hidden_size=3_7							,			)





					@unittest.skip(reason="ConvNext does not use inputs_embeds"	)
					def       _UpperCAmelCase						(							self	):
						pass





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0							,			reason="TF does not support backprop for grouped convolutions on CPU."							,			)
					@slow
					def       _UpperCAmelCase						(							self	):
						super().test_keras_fit()





					@unittest.skip(reason="ConvNext does not support input and output embeddings"	)
					def       _UpperCAmelCase						(							self	):
						pass





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[Any]		       =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase__:  List[Any]		       =   model_class(lowerCamelCase__	)
							UpperCAmelCase__:  Tuple		       =   inspect.signature(model.call	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							UpperCAmelCase__:  List[Any]		       =   [*signature.parameters.keys()]

							UpperCAmelCase__:  Optional[int]		       =   ["pixel_values"]
							self.assertListEqual(arg_names[:1]							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  str		       =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__	)





					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("GPU"	)	) == 0							,			reason="TF does not support backprop for grouped convolutions on CPU."							,			)
					def       _UpperCAmelCase						(							self	):
						super().test_dataset_conversion()





					def       _UpperCAmelCase						(							self	):
						def check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
							UpperCAmelCase__:  Any		       =   model_class(lowerCamelCase__	)

							UpperCAmelCase__:  int		       =   model(**self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)	)
							UpperCAmelCase__:  Optional[int]		       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							UpperCAmelCase__:  Dict		       =   self.model_tester.num_stages
							self.assertEqual(len(lowerCamelCase__	)							,			expected_num_stages + 1	)

							# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]	)							,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,			)

						UpperCAmelCase__:  Dict		       =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase__:  str		       =   True
							check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase__:  Optional[int]		       =   True

							check_hidden_states_output(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Any		       =   self.model_tester.prepare_config_and_inputs_for_common()

						def check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__={}	):
							UpperCAmelCase__:  Optional[Any]		       =   model(lowerCamelCase__							,			return_dict=lowerCamelCase__							,			**lowerCamelCase__	)
							UpperCAmelCase__:  Union[str, Any]		       =   model(lowerCamelCase__							,			return_dict=lowerCamelCase__							,			**lowerCamelCase__	).to_tuple()

							def recursive_check(lowerCamelCase__							,			lowerCamelCase__	):
								if isinstance(lowerCamelCase__							,			(List, Tuple)	):
									for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase__							,			lowerCamelCase__	):
										recursive_check(lowerCamelCase__							,			lowerCamelCase__	)
								elif tuple_object is None:
									return
								else:
									self.assertTrue(
									    all(tf.equal(lowerCamelCase__							,			lowerCamelCase__	)	)							,			msg=(
									        "Tuple and dict output are not equal. Difference:"
									        F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object	)	)}"
									    )							,			)

								recursive_check(lowerCamelCase__							,			lowerCamelCase__	)

						for model_class in self.all_model_classes:
							UpperCAmelCase__:  Tuple		       =   model_class(lowerCamelCase__	)

							UpperCAmelCase__:  Tuple		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
							UpperCAmelCase__:  int		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)

							UpperCAmelCase__:  Tuple		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
							UpperCAmelCase__:  Any		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)

							UpperCAmelCase__:  Any		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
							UpperCAmelCase__:  List[str]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__	)
							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			{"output_hidden_states": True}	)

							UpperCAmelCase__:  Union[str, Any]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
							UpperCAmelCase__:  int		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
							check_equivalence(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			{"output_hidden_states": True}	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Any		       =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)





					@slow
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[Any]		       =   TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"	)
						self.assertIsNotNone(lowerCamelCase__	)



def  _A  (			):
	UpperCAmelCase__:  Optional[Any]		       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
	return image

@require_tf
@require_vision
class __UpperCamelCase    (							unittest.TestCase		):






					'''simple docstring'''



					@cached_property
					def       _UpperCAmelCase						(							self	):
						return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"	) if is_vision_available() else None





					@slow
					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Tuple		       =   TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"	)

						UpperCAmelCase__:  List[str]		       =   self.default_image_processor
						UpperCAmelCase__:  Union[str, Any]		       =   prepare_img()
						UpperCAmelCase__:  Any		       =   image_processor(images=lowerCamelCase__							,			return_tensors="tf"	)

						# forward pass
						UpperCAmelCase__:  List[str]		       =   model(**lowerCamelCase__	)

						# verify the logits
						UpperCAmelCase__:  Tuple		       =   tf.TensorShape((1, 1_0_0_0)	)
						self.assertEqual(outputs.logits.shape							,			lowerCamelCase__	)

						UpperCAmelCase__:  List[Any]		       =   tf.constant([-0.0_260, -0.4_739, 0.1_911]	)

						tf.debugging.assert_near(outputs.logits[0, :3]							,			lowerCamelCase__							,			atol=1e-4	)


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


_lowerCAmelCase     :			int    ="""1.5.0"""

_lowerCAmelCase     :			Optional[Any]    =["""vision-encoder-decoder"""]



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

		UpperCAmelCase__:  Optional[int]		       =   version.parse(SCREAMING_SNAKE_CASE			)
		if parsed_optimum_version < version.parse(SCREAMING_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]"			)
	UpperCAmelCase__:  Any		       =   [
	    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}",
	]
	UpperCAmelCase__:  str		       =   subprocess.Popen(" ".join(SCREAMING_SNAKE_CASE			)  ,stdout=subprocess.PIPE  ,shell=SCREAMING_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  _A  (			SCREAMING_SNAKE_CASE			):
	UpperCAmelCase__:  Optional[int]		       =   args.output if args.output.is_file() else args.output.joinpath("model.onnx"			)
	if not args.output.parent.exists():
		args.output.parent.mkdir(parents=SCREAMING_SNAKE_CASE			)

	# Allocate the model
	UpperCAmelCase__:  int		       =   FeaturesManager.get_model_from_feature(
	    args.feature  ,args.model  ,framework=args.framework  ,cache_dir=args.cache_dir			)

	UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE  ,feature=args.feature			)
	UpperCAmelCase__:  str		       =   model_onnx_config(model.config			)

	if model_kind in ENCODER_DECODER_MODELS:
		UpperCAmelCase__:  List[str]		       =   model.get_encoder()
		UpperCAmelCase__:  Optional[int]		       =   model.get_decoder()

		UpperCAmelCase__:  List[str]		       =   onnx_config.get_encoder_config(encoder_model.config			)
		UpperCAmelCase__:  Any		       =   onnx_config.get_decoder_config(
		    encoder_model.config  ,decoder_model.config  ,feature=args.feature			)

		if args.opset is None:
			UpperCAmelCase__:  Tuple		       =   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."			)

		UpperCAmelCase__:  str		       =   AutoFeatureExtractor.from_pretrained(args.model			)

		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,)

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

		UpperCAmelCase__:  Dict		       =   AutoTokenizer.from_pretrained(args.model			)

		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   export(
		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,)

		validate_model_outputs(
		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,SCREAMING_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":
			UpperCAmelCase__:  str		       =   get_preprocessor(args.model			)
		elif args.preprocessor == "tokenizer":
			UpperCAmelCase__:  Any		       =   AutoTokenizer.from_pretrained(args.model			)
		elif args.preprocessor == "image_processor":
			UpperCAmelCase__:  Optional[int]		       =   AutoImageProcessor.from_pretrained(args.model			)
		elif args.preprocessor == "feature_extractor":
			UpperCAmelCase__:  int		       =   AutoFeatureExtractor.from_pretrained(args.model			)
		elif args.preprocessor == "processor":
			UpperCAmelCase__:  Optional[int]		       =   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:
			UpperCAmelCase__:  List[str]		       =   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."			)

		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output  ,)

		if args.atol is None:
			UpperCAmelCase__:  List[Any]		       =   onnx_config.atol_for_validation

		validate_model_outputs(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output  ,SCREAMING_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."  ,SCREAMING_SNAKE_CASE  ,)



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

	UpperCAmelCase__:  Optional[int]		       =   parser.parse_args()
	if args.export_with_transformers or not is_optimum_available():
		export_with_transformers(SCREAMING_SNAKE_CASE			)
	else:
		export_with_optimum(SCREAMING_SNAKE_CASE			)


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