'''simple docstring'''






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


SCREAMING_SNAKE_CASE_	   =			['small', 'medium', 'large']

SCREAMING_SNAKE_CASE_	   =			'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_	   =			'lm_head.weight'






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				str     )							->	List[str]:
  __UpperCAmelCase:					Tuple		= torch.load(_lowercase     )
  __UpperCAmelCase:					Tuple		= d.pop(_lowercase     )
  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )
  torch.save(_lowercase   ,    os.path.join(_lowercase   ,    _lowercase     )     )


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




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=0.9   ,    snake_case_=None   ,    snake_case_=True   ,    snake_case_=[0.5, 0.5, 0.5]   ,    snake_case_=[0.5, 0.5, 0.5]   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= size if size is not None else {"""shortest_edge""": 30}
    __UpperCAmelCase:					str		= crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
    __UpperCAmelCase:					Dict		= parent
    __UpperCAmelCase:					Union[str, Any]		= batch_size
    __UpperCAmelCase:					List[Any]		= num_channels
    __UpperCAmelCase:					List[str]		= min_resolution
    __UpperCAmelCase:					List[str]		= max_resolution
    __UpperCAmelCase:					Tuple		= do_resize_and_center_crop
    __UpperCAmelCase:					Union[str, Any]		= size
    __UpperCAmelCase:					str		= crop_pct
    __UpperCAmelCase:					List[Any]		= crop_size
    __UpperCAmelCase:					str		= do_normalize
    __UpperCAmelCase:					Union[str, Any]		= image_mean
    __UpperCAmelCase:					List[str]		= image_std







  def    lowercase_  (							self					):

    '''simple docstring'''







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




@require_torch
@require_vision
class    a (       __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					PoolFormerImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= PoolFormerImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize_and_center_crop"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """crop_pct"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """image_mean"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """image_std"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""shortest_edge""": 30}					)
    self.assertEqual(image_processor.crop_size   ,    {"""height""": 30, """width""": 30}					)

    __UpperCAmelCase:					Dict		= 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    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)
    # create random PIL images
    __UpperCAmelCase:					Optional[Any]		= 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:					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:					Any		= 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    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.image_processing_class(**self.image_processor_dict					)
    # create random numpy tensors
    __UpperCAmelCase:					Union[str, Any]		= 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:					Union[str, 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
    __UpperCAmelCase:					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.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )   ,    )







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)
    # create random PyTorch tensors
    __UpperCAmelCase:					Dict		= 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:					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:					Optional[int]		= 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"""],
        )   ,    )