
'''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    UpperCAmelCase_				(	unittest.TestCase					):





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





 def        snake_case__		(    self):
     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    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						LevitImageProcessor if is_vision_available() else None





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						LevitImageProcessingTester(self)





 @property
 def        snake_case__		(    self):
     return self.image_processor_tester.prepare_image_processor_dict()





 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(lowercase_ ,       "image_mean"))
     self.assertTrue(hasattr(lowercase_ ,       "image_std"))
     self.assertTrue(hasattr(lowercase_ ,       "do_normalize"))
     self.assertTrue(hasattr(lowercase_ ,       "do_resize"))
     self.assertTrue(hasattr(lowercase_ ,       "do_center_crop"))
     self.assertTrue(hasattr(lowercase_ ,       "size"))





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						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})

     snake_case_ :    Optional[int]     			=						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        snake_case__		(    self):
     pass





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    Any     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     snake_case_ :    Tuple     			=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=lowercase_)
     for image in image_inputs:
         self.assertIsInstance(lowercase_ ,       Image.Image)

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

     # Test batched
     snake_case_ :    Dict     			=						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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         ) ,       )





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    Dict     			=						self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     snake_case_ :    Optional[int]     			=						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
     snake_case_ :    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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         ) ,       )

     # Test batched
     snake_case_ :    List[Any]     			=						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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         ) ,       )





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    Dict     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     snake_case_ :    List[str]     			=						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
     snake_case_ :    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
     snake_case_ :    Optional[int]     			=						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.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_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


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




