


'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=7   ,						snake_case__=3   ,						snake_case__=18   ,						snake_case__=30   ,						snake_case__=400   ,						snake_case__=True   ,						snake_case__=None   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=[0.5, 0.5, 0.5]   ,						snake_case__=[0.5, 0.5, 0.5]   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							parent
            _lowerCAmelCase     :				str       =							batch_size
            _lowerCAmelCase     :				List[str]       =							num_channels
            _lowerCAmelCase     :				Any       =							image_size
            _lowerCAmelCase     :				List[Any]       =							min_resolution
            _lowerCAmelCase     :				int       =							max_resolution
            _lowerCAmelCase     :				Optional[int]       =							do_resize
            _lowerCAmelCase     :				List[Any]       =							size if size is not None else {'height': 18, 'width': 20}
            _lowerCAmelCase     :				str       =							do_thumbnail
            _lowerCAmelCase     :				Optional[Any]       =							do_align_axis
            _lowerCAmelCase     :				Optional[Any]       =							do_pad
            _lowerCAmelCase     :				Any       =							do_normalize
            _lowerCAmelCase     :				Optional[int]       =							image_mean
            _lowerCAmelCase     :				Optional[int]       =							image_std







     def a						(			self ):

            '''simple docstring'''

            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_thumbnail": self.do_thumbnail,
                "do_align_long_axis": self.do_align_axis,
                "do_pad": self.do_pad,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }
@require_torch
@require_vision
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     DonutImageProcessor if is_vision_available() else None







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							DonutImageProcessingTester(self )







     @property
     def a						(			self ):

            '''simple docstring'''

            return self.image_processor_tester.prepare_image_processor_dict()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							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_thumbnail' ) )
            self.assertTrue(hasattr(snake_case__   ,						'do_align_long_axis' ) )
            self.assertTrue(hasattr(snake_case__   ,						'do_pad' ) )
            self.assertTrue(hasattr(snake_case__   ,						'do_normalize' ) )
            self.assertTrue(hasattr(snake_case__   ,						'image_mean' ) )
            self.assertTrue(hasattr(snake_case__   ,						'image_std' ) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size   ,						{'height': 18, 'width': 20} )

            _lowerCAmelCase     :				Dict       =							self.image_processing_class.from_dict(self.image_processor_dict   ,						size=42 )
            self.assertEqual(image_processor.size   ,						{'height': 42, 'width': 42} )

            # Previous config had dimensions in (width, height) order
            _lowerCAmelCase     :				Union[str, Any]       =							self.image_processing_class.from_dict(self.image_processor_dict   ,						size=(42, 84) )
            self.assertEqual(image_processor.size   ,						{'height': 84, 'width': 42} )







     def a						(			self ):

            '''simple docstring'''

            pass







     @is_flaky()
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.image_processing_class(**self.image_processor_dict )
            # create random PIL images
            _lowerCAmelCase     :				Tuple       =							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
            _lowerCAmelCase     :				Optional[Any]       =							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
            _lowerCAmelCase     :				Dict       =							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.size['height'],
                    self.image_processor_tester.size['width'],
                )   ,						)







     @is_flaky()
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            _lowerCAmelCase     :				int       =							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
            _lowerCAmelCase     :				List[Any]       =							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
            _lowerCAmelCase     :				List[str]       =							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.size['height'],
                    self.image_processor_tester.size['width'],
                )   ,						)







     @is_flaky()
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            _lowerCAmelCase     :				Union[str, Any]       =							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
            _lowerCAmelCase     :				int       =							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
            _lowerCAmelCase     :				Tuple       =							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.size['height'],
                    self.image_processor_tester.size['width'],
                )   ,						)





'''simple docstring'''







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Optional[Any]       =							TaConfig.from_json_file(_A       )
       print(f'Building PyTorch model from configuration: {config}'       )
       _lowerCAmelCase     :				Union[str, Any]       =							TaForConditionalGeneration(_A       )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_A ,    _A ,    _A       )

       # Save pytorch-model
       print(f'Save PyTorch model to {pytorch_dump_path}'       )
       model.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  str											=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       lowerCAmelCase     :  int											=    parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

