




"""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_video_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import VivitImageProcessor



class   a__					(						unittest.TestCase ):


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Any=7   ,	UpperCamelCase_   :     Union[str, Any]=3   ,	UpperCamelCase_   :     Union[str, Any]=10   ,	UpperCamelCase_   :     Dict=18   ,	UpperCamelCase_   :     str=30   ,	UpperCamelCase_   :     int=400   ,	UpperCamelCase_   :     List[str]=True   ,	UpperCamelCase_   :     Dict=None   ,	UpperCamelCase_   :     Optional[int]=True   ,	UpperCamelCase_   :     Dict=[0.5, 0.5, 0.5]   ,	UpperCamelCase_   :     Dict=[0.5, 0.5, 0.5]   ,	UpperCamelCase_   :     List[str]=None   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       size if size is not None else {"""shortest_edge""": 18}
						__UpperCAmelCase						:			List[str]												=       crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

						__UpperCAmelCase						:			str												=       parent
						__UpperCAmelCase						:			Optional[int]												=       batch_size
						__UpperCAmelCase						:			List[str]												=       num_channels
						__UpperCAmelCase						:			Any												=       num_frames
						__UpperCAmelCase						:			Optional[Any]												=       image_size
						__UpperCAmelCase						:			Union[str, Any]												=       min_resolution
						__UpperCAmelCase						:			Optional[Any]												=       max_resolution
						__UpperCAmelCase						:			int												=       do_resize
						__UpperCAmelCase						:			List[str]												=       size
						__UpperCAmelCase						:			Optional[Any]												=       do_normalize
						__UpperCAmelCase						:			str												=       image_mean
						__UpperCAmelCase						:			List[Any]												=       image_std
						__UpperCAmelCase						:			Optional[int]												=       crop_size


			def    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}






@require_torch
@require_vision
class   a__					(						_UpperCamelCase				,     unittest.TestCase ):
			lowercase_		=						VivitImageProcessor if is_vision_available() else None


			def    a_  (   self   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			List[Any]												=       VivitImageProcessingTester(self)


			@property
			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						return self.image_processor_tester.prepare_image_processor_dict()


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       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    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       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						:			str												=       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    a_  (   self   :     List[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.image_processing_class(**self.image_processor_dict)
						# create random PIL videos
						__UpperCAmelCase						:			Any												=       prepare_video_inputs(self.image_processor_tester   ,	equal_resolution=UpperCamelCase_)
						for video in video_inputs:
									self.assertIsInstance(UpperCamelCase_   ,	UpperCamelCase_)
									self.assertIsInstance(video[0]   ,	Image.Image)

						# Test not batched input
						__UpperCAmelCase						:			str												=       image_processing(video_inputs[0]   ,	return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_videos.shape   ,	(
						        1,
						        self.image_processor_tester.num_frames,
						        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_videos.shape   ,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,	)


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__UpperCAmelCase						:			Optional[int]												=       prepare_video_inputs(self.image_processor_tester   ,	equal_resolution=UpperCamelCase_   ,	numpify=UpperCamelCase_)
						for video in video_inputs:
									self.assertIsInstance(UpperCamelCase_   ,	UpperCamelCase_)
									self.assertIsInstance(video[0]   ,	np.ndarray)

						# Test not batched input
						__UpperCAmelCase						:			Union[str, Any]												=       image_processing(video_inputs[0]   ,	return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_videos.shape   ,	(
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,	)

						# Test batched
						__UpperCAmelCase						:			List[str]												=       image_processing(UpperCamelCase_   ,	return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_videos.shape   ,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,	)


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__UpperCAmelCase						:			Dict												=       prepare_video_inputs(self.image_processor_tester   ,	equal_resolution=UpperCamelCase_   ,	torchify=UpperCamelCase_)
						for video in video_inputs:
									self.assertIsInstance(UpperCamelCase_   ,	UpperCamelCase_)
									self.assertIsInstance(video[0]   ,	torch.Tensor)

						# Test not batched input
						__UpperCAmelCase						:			Optional[Any]												=       image_processing(video_inputs[0]   ,	return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_videos.shape   ,	(
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,	)

						# Test batched
						__UpperCAmelCase						:			Any												=       image_processing(UpperCamelCase_   ,	return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_videos.shape   ,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,	)








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 				A__ (snake_case   :		str		,	snake_case   :		str				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Union[str, Any]             =      torch.load(snake_case				)
					__UpperCamelCase					:	int             =      d.pop(snake_case				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					torch.save(snake_case		,	os.path.join(snake_case		,	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,
		)






