
"""simple docstring"""




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 LevitImageProcessor







class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=7 ,			__UpperCamelCase=3 ,			__UpperCamelCase=18 ,			__UpperCamelCase=30 ,			__UpperCamelCase=4_00 ,			__UpperCamelCase=True ,			__UpperCamelCase=None ,			__UpperCamelCase=True ,			__UpperCamelCase=None ,			__UpperCamelCase=True ,			__UpperCamelCase=[0.5, 0.5, 0.5] ,			__UpperCamelCase=[0.5, 0.5, 0.5] ,			)->					int:
          UpperCAmelCase__							: Optional[int] 							=						size if size is not None else {"shortest_edge": 18}
          UpperCAmelCase__							: Dict 							=						crop_size if crop_size is not None else {"height": 18, "width": 18}
          UpperCAmelCase__							: List[Any] 							=						parent
          UpperCAmelCase__							: Tuple 							=						batch_size
          UpperCAmelCase__							: List[Any] 							=						num_channels
          UpperCAmelCase__							: Optional[Any] 							=						image_size
          UpperCAmelCase__							: List[Any] 							=						min_resolution
          UpperCAmelCase__							: Any 							=						max_resolution
          UpperCAmelCase__							: Dict 							=						do_resize
          UpperCAmelCase__							: List[Any] 							=						size
          UpperCAmelCase__							: Any 							=						do_center_crop
          UpperCAmelCase__							: Optional[int] 							=						crop_size
          UpperCAmelCase__							: Union[str, Any] 							=						do_normalize
          UpperCAmelCase__							: str 							=						image_mean
          UpperCAmelCase__							: Dict 							=						image_std



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "do_center_crop": self.do_center_crop,
              "size": self.size,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							LevitImageProcessor if is_vision_available() else None



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: str 							=						LevitImageProcessingTester(self  )



     @property
     def  lowerCAmelCase__       (      self  )->					Dict:
          return self.image_processor_tester.prepare_image_processor_dict()



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: str 							=						self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"image_mean"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"image_std"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"do_normalize"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"do_resize"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"do_center_crop"  )  )
          self.assertTrue(hasattr(__UpperCamelCase ,			"size"  )  )



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Optional[Any] 							=						self.image_processing_class.from_dict(self.image_processor_dict  )
          self.assertEqual(image_processor.size ,			{"shortest_edge": 18}  )
          self.assertEqual(image_processor.crop_size ,			{"height": 18, "width": 18}  )

          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processing_class.from_dict(self.image_processor_dict ,			size=42 ,			crop_size=84  )
          self.assertEqual(image_processor.size ,			{"shortest_edge": 42}  )
          self.assertEqual(image_processor.crop_size ,			{"height": 84, "width": 84}  )



     def  lowerCAmelCase__       (      self  )->					int:
          pass



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

          # Test not batched input
          UpperCAmelCase__							: Optional[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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              ) ,			)

          # Test batched
          UpperCAmelCase__							: str 							=						image_processing(__UpperCamelCase ,			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  lowerCAmelCase__       (      self  )->					Optional[Any]:
          # Initialize image_processing
          UpperCAmelCase__							: Tuple 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          UpperCAmelCase__							: List[Any] 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__UpperCamelCase ,			numpify=__UpperCamelCase  )
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase ,			np.ndarray  )

          # Test not batched input
          UpperCAmelCase__							: Tuple 							=						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__							: List[Any] 							=						image_processing(__UpperCamelCase ,			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  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          # Initialize image_processing
          UpperCAmelCase__							: List[Any] 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          UpperCAmelCase__							: Optional[int] 							=						prepare_image_inputs(self.image_processor_tester ,			equal_resolution=__UpperCamelCase ,			torchify=__UpperCamelCase  )
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase ,			torch.Tensor  )

          # Test not batched input
          UpperCAmelCase__							: List[str] 							=						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__							: Dict 							=						image_processing(__UpperCamelCase ,			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 argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


if __name__ == "__main__":
    A__      :	List[Any]          =    argparse.ArgumentParser()
    parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
    A__      :	Tuple          =    parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        A__      :	Tuple          =    os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
        A__      :	str          =    f"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )



