




'''simple docstring'''
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ImageGPTImageProcessor






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 ,       ):
     snake_case_ :    Union[str, Any]     			=						size if size is not None else {"height": 18, "width": 18}
     snake_case_ :    str     			=						parent
     snake_case_ :    List[str]     			=						batch_size
     snake_case_ :    Tuple     			=						num_channels
     snake_case_ :    Optional[int]     			=						image_size
     snake_case_ :    List[str]     			=						min_resolution
     snake_case_ :    List[str]     			=						max_resolution
     snake_case_ :    Optional[Any]     			=						do_resize
     snake_case_ :    Optional[Any]     			=						size
     snake_case_ :    List[str]     			=						do_normalize





 def        snake_case__		(    self):
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						ImageGPTImageProcessor if is_vision_available() else None





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





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





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(lowercase_ ,       "clusters"))
     self.assertTrue(hasattr(lowercase_ ,       "do_resize"))
     self.assertTrue(hasattr(lowercase_ ,       "size"))
     self.assertTrue(hasattr(lowercase_ ,       "do_normalize"))





 def        snake_case__		(    self):
     snake_case_ :    int     			=						self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size ,       {"height": 18, "width": 18})

     snake_case_ :    int     			=						self.image_processing_class.from_dict(self.image_processor_dict ,       size=42)
     self.assertEqual(image_processor.size ,       {"height": 42, "width": 42})





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.image_processing_class(**self.image_processor_dict)
     snake_case_ :    int     			=						json.loads(image_processor.to_json_string())
     for key, value in self.image_processor_dict.items():
         if key == "clusters":
             self.assertTrue(np.array_equal(lowercase_ ,       obj[key]))
         else:
             self.assertEqual(obj[key] ,       lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						self.image_processing_class(**self.image_processor_dict)

     with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case_ :    List[str]     			=						os.path.join(lowercase_ ,       "image_processor.json")
         image_processor_first.to_json_file(lowercase_)
         snake_case_ :    Tuple     			=						self.image_processing_class.from_json_file(lowercase_).to_dict()

     snake_case_ :    Union[str, Any]     			=						image_processor_first.to_dict()
     for key, value in image_processor_first.items():
         if key == "clusters":
             self.assertTrue(np.array_equal(lowercase_ ,       image_processor_second[key]))
         else:
             self.assertEqual(image_processor_first[key] ,       lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						self.image_processing_class(**self.image_processor_dict)

     with tempfile.TemporaryDirectory() as tmpdirname:
         image_processor_first.save_pretrained(lowercase_)
         snake_case_ :    Optional[Any]     			=						self.image_processing_class.from_pretrained(lowercase_).to_dict()

     snake_case_ :    List[Any]     			=						image_processor_first.to_dict()
     for key, value in image_processor_first.items():
         if key == "clusters":
             self.assertTrue(np.array_equal(lowercase_ ,       image_processor_second[key]))
         else:
             self.assertEqual(image_processor_first[key] ,       lowercase_)





 @unittest.skip("ImageGPT requires clusters at initialization")
 def        snake_case__		(    self):
     pass


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






    snake_case_ :    Any     			=						load_dataset("hf-internal-testing/fixtures_image_utils",		split="test"       )

    snake_case_ :    str     			=						Image.open(dataset[4]["file"]       )
    snake_case_ :    Any     			=						Image.open(dataset[5]["file"]       )

    snake_case_ :    List[str]     			=						[imagea, imagea]

    return images






@require_vision
@require_torch
class    UpperCAmelCase_				(	unittest.TestCase					):





 @slow
 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small")

     snake_case_ :    Optional[Any]     			=						prepare_images()

     # test non-batched
     snake_case_ :    Any     			=						image_processing(images[0] ,       return_tensors="pt")

     self.assertIsInstance(encoding.input_ids ,       torch.LongTensor)
     self.assertEqual(encoding.input_ids.shape ,       (1, 10_24))

     snake_case_ :    str     			=						[3_06, 1_91, 1_91]
     self.assertEqual(encoding.input_ids[0, :3].tolist() ,       lowercase_)

     # test batched
     snake_case_ :    List[str]     			=						image_processing(lowercase_ ,       return_tensors="pt")

     self.assertIsInstance(encoding.input_ids ,       torch.LongTensor)
     self.assertEqual(encoding.input_ids.shape ,       (2, 10_24))

     snake_case_ :    str     			=						[3_03, 13, 13]
     self.assertEqual(encoding.input_ids[1, -3:].tolist() ,       lowercase_)






'''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,
    )




