




import unittest

import numpy as np

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 OwlViTImageProcessor

class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def __init__(  self       :			str			,							snake_case       :			Any			,							snake_case       :			Union[str, Any]=7			,							snake_case       :			Optional[Any]=3			,							snake_case       :			str=18			,							snake_case       :			Union[str, Any]=30			,							snake_case       :			List[Any]=400			,							snake_case       :			List[Any]=True			,							snake_case       :			int=None			,							snake_case       :			Union[str, Any]=True			,							snake_case       :			str=None			,							snake_case       :			Any=True			,							snake_case       :			Optional[Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,							snake_case       :			Optional[int]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,							snake_case       :			List[Any]=True			,							):
          __UpperCamelCase			=    parent
          __UpperCamelCase			=    batch_size
          __UpperCamelCase			=    num_channels
          __UpperCamelCase			=    image_size
          __UpperCamelCase			=    min_resolution
          __UpperCamelCase			=    max_resolution
          __UpperCamelCase			=    do_resize
          __UpperCamelCase			=    size if size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_center_crop
          __UpperCamelCase			=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __UpperCamelCase			=    do_normalize
          __UpperCamelCase			=    image_mean
          __UpperCamelCase			=    image_std
          __UpperCamelCase			=    do_convert_rgb







      def    snake_case						(  self       :			Union[str, Any]					):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_convert_rgb": self.do_convert_rgb,
          }







@require_torch
@require_vision
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			List[str]     =       OwlViTImageProcessor if is_vision_available() else None







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    OwlViTImageProcessingTester(self					)







      @property
      def    snake_case						(  self       :			Optional[int]					):
          return self.image_processor_tester.prepare_image_processor_dict()







      def    snake_case						(  self       :			Any					):
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          self.assertTrue(hasattr(snake_case			,							'''do_resize'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''size'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_center_crop'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''center_crop'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_normalize'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''image_mean'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''image_std'''					)					)
          self.assertTrue(hasattr(snake_case			,							'''do_convert_rgb'''					)					)







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict					)
          self.assertEqual(image_processor.size			,							{'''height''': 18, '''width''': 18}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 18, '''width''': 18}					)

          __UpperCamelCase			=    self.image_processing_class.from_dict(self.image_processor_dict			,							size=42			,							crop_size=84					)
          self.assertEqual(image_processor.size			,							{'''height''': 42, '''width''': 42}					)
          self.assertEqual(image_processor.crop_size			,							{'''height''': 84, '''width''': 84}					)







      def    snake_case						(  self       :			Tuple					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case					)

          for image in image_inputs:
              self.assertIsInstance(snake_case			,							Image.Image					)

          # Test not batched input
          __UpperCamelCase			=    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.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							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.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)







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

          # Test not batched input
          __UpperCamelCase			=    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.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							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.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)







      def    snake_case						(  self       :			int					):
          # Initialize image_processing
          __UpperCamelCase			=    self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          __UpperCamelCase			=    prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case			,							torchify=snake_case					)
          for image in image_inputs:
              self.assertIsInstance(snake_case			,							torch.Tensor					)

          # Test not batched input
          __UpperCamelCase			=    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.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)

          # Test batched
          __UpperCamelCase			=    image_processing(snake_case			,							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.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )			,							)







'''simple docstring'''

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 :				List[Any]														= """1.5.0"""

_lowerCamelCase :				List[Any]														= ["""vision-encoder-decoder"""]



def  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







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

          _UpperCamelCase  :Dict       =version.parse(__a      )
          if parsed_optimum_version < version.parse(__a      ):
               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  :Tuple       =[
         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  :Union[str, Any]       =subprocess.Popen(""" """.join(__a      )			,      stdout=subprocess.PIPE			,      shell=__a      )
     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  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _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=__a      )

     # Allocate the model
     _UpperCamelCase  :Dict       =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(__a			,      feature=args.feature      )
     _UpperCamelCase  :int       =model_onnx_config(model.config      )

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

          _UpperCamelCase  :Dict       =onnx_config.get_encoder_config(encoder_model.config      )
          _UpperCamelCase  :str       =onnx_config.get_decoder_config(
              encoder_model.config			,      decoder_model.config			,      feature=args.feature      )

          if args.opset is None:
               _UpperCamelCase  :int       =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  :Optional[Any]       =AutoFeatureExtractor.from_pretrained(args.model      )

          _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output.parent.joinpath("""encoder_model.onnx"""      )			,      )

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

          _UpperCamelCase  :str       =AutoTokenizer.from_pretrained(args.model      )

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

          validate_model_outputs(
              __a			,      __a			,      __a			,      args.output.parent.joinpath("""decoder_model.onnx"""      )			,      __a			,      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  :Union[str, Any]       =get_preprocessor(args.model      )
          elif args.preprocessor == "tokenizer":
               _UpperCamelCase  :Dict       =AutoTokenizer.from_pretrained(args.model      )
          elif args.preprocessor == "image_processor":
               _UpperCamelCase  :Union[str, Any]       =AutoImageProcessor.from_pretrained(args.model      )
          elif args.preprocessor == "feature_extractor":
               _UpperCamelCase  :int       =AutoFeatureExtractor.from_pretrained(args.model      )
          elif args.preprocessor == "processor":
               _UpperCamelCase  :Dict       =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  :Optional[int]       =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  :Optional[Any]       =export(
              __a			,      __a			,      __a			,      args.opset			,      args.output			,      )

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

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



def  _lowerCAmelCase				(				)  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, Any]       =ArgumentParser("""Hugging Face Transformers ONNX exporter"""      )
     parser.add_argument(
         """-m"""			,      """--model"""			,      type=__a			,      required=__a			,      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=__a			,      default=__a			,      help="""ONNX opset version to export the model with."""      )
     parser.add_argument(
         """--atol"""			,      type=__a			,      default=__a			,      help="""Absolute difference tolerance when validating the model."""      )
     parser.add_argument(
         """--framework"""			,      type=__a			,      choices=["""pt""", """tf"""]			,      default=__a			,      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=__a			,      help="""Path indicating where to store generated ONNX model."""      )
     parser.add_argument("""--cache_dir"""			,      type=__a			,      default=__a			,      help="""Path indicating where to store cache."""      )
     parser.add_argument(
         """--preprocessor"""			,      type=__a			,      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  :str       =parser.parse_args()
     if args.export_with_transformers or not is_optimum_available():
          export_with_transformers(__a      )
     else:
          export_with_optimum(__a      )


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