




import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import YolosImageProcessor
class 			_UpperCamelCase      (				unittest.TestCase		):
      def __init__(			self   :str ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any=7 ,      lowerCamelCase   :Optional[Any]=3 ,      lowerCamelCase   :int=30 ,      lowerCamelCase   :Optional[Any]=400 ,      lowerCamelCase   :Tuple=True ,      lowerCamelCase   :Optional[Any]=None ,      lowerCamelCase   :Dict=True ,      lowerCamelCase   :Tuple=[0.5, 0.5, 0.5] ,      lowerCamelCase   :str=[0.5, 0.5, 0.5] ,      lowerCamelCase   :Tuple=True ,      lowerCamelCase   :int=1 / 255 ,      lowerCamelCase   :Dict=True ,      )     ->			Any:
       UpperCAmelCase__							 =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
       UpperCAmelCase__							 =						parent
       UpperCAmelCase__							 =						batch_size
       UpperCAmelCase__							 =						num_channels
       UpperCAmelCase__							 =						min_resolution
       UpperCAmelCase__							 =						max_resolution
       UpperCAmelCase__							 =						do_resize
       UpperCAmelCase__							 =						size
       UpperCAmelCase__							 =						do_normalize
       UpperCAmelCase__							 =						image_mean
       UpperCAmelCase__							 =						image_std
       UpperCAmelCase__							 =						do_rescale
       UpperCAmelCase__							 =						rescale_factor
       UpperCAmelCase__							 =						do_pad
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			str:
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }







      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :str ,      lowerCamelCase   :str=False      )     ->			Any:


       if not batched:
        UpperCAmelCase__							 =						image_inputs[0]
        if isinstance(__UpperCamelCase ,      Image.Image      ):
         UpperCAmelCase__ , UpperCAmelCase__							 =						image.size
        else:
         UpperCAmelCase__ , UpperCAmelCase__							 =						image.shape[1], image.shape[2]
        if w < h:
         UpperCAmelCase__							 =						int(self.size["shortest_edge"] * h / w      )
         UpperCAmelCase__							 =						self.size["shortest_edge"]
        elif w > h:
         UpperCAmelCase__							 =						self.size["shortest_edge"]
         UpperCAmelCase__							 =						int(self.size["shortest_edge"] * w / h      )
        else:
         UpperCAmelCase__							 =						self.size["shortest_edge"]
         UpperCAmelCase__							 =						self.size["shortest_edge"]

       else:
        UpperCAmelCase__							 =						[]
        for image in image_inputs:
         UpperCAmelCase__ , UpperCAmelCase__							 =						self.get_expected_values([image]      )
         expected_values.append((expected_height, expected_width)      )
        UpperCAmelCase__							 =						max(__UpperCamelCase ,      key=lambda lowerCamelCase      : item[0]      )[0]
        UpperCAmelCase__							 =						max(__UpperCamelCase ,      key=lambda lowerCamelCase      : item[1]      )[1]

       return expected_height, expected_width


@require_torch
@require_vision
class 			_UpperCamelCase      (				__snake_case	,       unittest.TestCase		):
      UpperCAmelCase_        =		YolosImageProcessor if is_vision_available() else None
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						YolosImageProcessingTester(self      )
      @property
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			List[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[str]:
       UpperCAmelCase__							 =						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 ,      "size"      )      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Dict:
       UpperCAmelCase__							 =						self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size ,      {"shortest_edge": 18, "longest_edge": 1333}      )
       self.assertEqual(image_processor.do_pad ,      __UpperCamelCase      )

       UpperCAmelCase__							 =						self.image_processing_class.from_dict(
           self.image_processor_dict ,      size=42 ,      max_size=84 ,      pad_and_return_pixel_mask=__UpperCamelCase      )
       self.assertEqual(image_processor.size ,      {"shortest_edge": 42, "longest_edge": 84}      )
       self.assertEqual(image_processor.do_pad ,      __UpperCamelCase      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Optional[Any]:
       pass
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Any:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCAmelCase__							 =						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__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						self.image_processor_tester.get_expected_values(__UpperCamelCase      )

       self.assertEqual(
           encoded_images.shape ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,      )

       # Test batched
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.image_processor_tester.get_expected_values(__UpperCamelCase ,      batched=__UpperCamelCase      )

       UpperCAmelCase__							 =						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,
               expected_height,
               expected_width,
           ) ,      )
      def 	UpperCAmelCase_						(			self   :Any      )     ->			Dict:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       UpperCAmelCase__							 =						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__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						self.image_processor_tester.get_expected_values(__UpperCamelCase      )

       self.assertEqual(
           encoded_images.shape ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,      )

       # Test batched
       UpperCAmelCase__							 =						image_processing(__UpperCamelCase ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						self.image_processor_tester.get_expected_values(__UpperCamelCase ,      batched=__UpperCamelCase      )

       self.assertEqual(
           encoded_images.shape ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ) ,      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			List[Any]:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       UpperCAmelCase__							 =						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__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						self.image_processor_tester.get_expected_values(__UpperCamelCase      )

       self.assertEqual(
           encoded_images.shape ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,      )

       # Test batched
       UpperCAmelCase__							 =						image_processing(__UpperCamelCase ,      return_tensors="pt"      ).pixel_values

       UpperCAmelCase__ , UpperCAmelCase__							 =						self.image_processor_tester.get_expected_values(__UpperCamelCase ,      batched=__UpperCamelCase      )

       self.assertEqual(
           encoded_images.shape ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           ) ,      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Any:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       UpperCAmelCase__							 =						self.image_processing_class(do_resize=__UpperCamelCase ,      do_normalize=__UpperCamelCase ,      do_rescale=__UpperCamelCase      )
       # create random PyTorch tensors
       UpperCAmelCase__							 =						prepare_image_inputs(self.image_processor_tester ,      equal_resolution=__UpperCamelCase ,      torchify=__UpperCamelCase      )
       for image in image_inputs:
        self.assertIsInstance(__UpperCamelCase ,      torch.Tensor      )

       # Test whether the method "pad" and calling the image processor return the same tensors
       UpperCAmelCase__							 =						image_processing_a.pad(__UpperCamelCase ,      return_tensors="pt"      )
       UpperCAmelCase__							 =						image_processing_a(__UpperCamelCase ,      return_tensors="pt"      )

       self.assertTrue(
           torch.allclose(encoded_images_with_method["pixel_values"] ,      encoded_images["pixel_values"] ,      atol=1e-4      )      )
      @slow
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			List[str]:
       UpperCAmelCase__							 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,      "r"      ) as f:
        UpperCAmelCase__							 =						json.loads(f.read()      )

       UpperCAmelCase__							 =						{"image_id": 3_9769, "annotations": target}

       # encode them
       UpperCAmelCase__							 =						YolosImageProcessor.from_pretrained("hustvl/yolos-small"      )
       UpperCAmelCase__							 =						image_processing(images=__UpperCamelCase ,      annotations=__UpperCamelCase ,      return_tensors="pt"      )

       # verify pixel values
       UpperCAmelCase__							 =						torch.Size([1, 3, 800, 1066]      )
       self.assertEqual(encoding["pixel_values"].shape ,      __UpperCamelCase      )

       UpperCAmelCase__							 =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,      __UpperCamelCase ,      atol=1e-4      )      )

       # verify area
       UpperCAmelCase__							 =						torch.tensor([58_87.96_00, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,      __UpperCamelCase      )      )
       # verify boxes
       UpperCAmelCase__							 =						torch.Size([6, 4]      )
       self.assertEqual(encoding["labels"][0]["boxes"].shape ,      __UpperCamelCase      )
       UpperCAmelCase__							 =						torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,      __UpperCamelCase ,      atol=1e-3      )      )
       # verify image_id
       UpperCAmelCase__							 =						torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,      __UpperCamelCase      )      )
       # verify is_crowd
       UpperCAmelCase__							 =						torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,      __UpperCamelCase      )      )
       # verify class_labels
       UpperCAmelCase__							 =						torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,      __UpperCamelCase      )      )
       # verify orig_size
       UpperCAmelCase__							 =						torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,      __UpperCamelCase      )      )
       # verify size
       UpperCAmelCase__							 =						torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,      __UpperCamelCase      )      )







      @slow
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,      "r"      ) as f:
        UpperCAmelCase__							 =						json.loads(f.read()      )

       UpperCAmelCase__							 =						{"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

       UpperCAmelCase__							 =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

       # encode them
       UpperCAmelCase__							 =						YolosImageProcessor(format="coco_panoptic"      )
       UpperCAmelCase__							 =						image_processing(images=__UpperCamelCase ,      annotations=__UpperCamelCase ,      masks_path=__UpperCamelCase ,      return_tensors="pt"      )

       # verify pixel values
       UpperCAmelCase__							 =						torch.Size([1, 3, 800, 1066]      )
       self.assertEqual(encoding["pixel_values"].shape ,      __UpperCamelCase      )

       UpperCAmelCase__							 =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,      __UpperCamelCase ,      atol=1e-4      )      )

       # verify area
       UpperCAmelCase__							 =						torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 58_79.65_62, 76_34.11_47]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,      __UpperCamelCase      )      )
       # verify boxes
       UpperCAmelCase__							 =						torch.Size([6, 4]      )
       self.assertEqual(encoding["labels"][0]["boxes"].shape ,      __UpperCamelCase      )
       UpperCAmelCase__							 =						torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,      __UpperCamelCase ,      atol=1e-3      )      )
       # verify image_id
       UpperCAmelCase__							 =						torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,      __UpperCamelCase      )      )
       # verify is_crowd
       UpperCAmelCase__							 =						torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,      __UpperCamelCase      )      )
       # verify class_labels
       UpperCAmelCase__							 =						torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,      __UpperCamelCase      )      )
       # verify masks
       UpperCAmelCase__							 =						82_2873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,      __UpperCamelCase      )
       # verify orig_size
       UpperCAmelCase__							 =						torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,      __UpperCamelCase      )      )
       # verify size
       UpperCAmelCase__							 =						torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,      __UpperCamelCase      )      )











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


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





