



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       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




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


         """simple docstring"""



         __A         =	size if size is not None else {"""height""": 18, """width""": 18}
         __A         =	parent
         __A         =	batch_size
         __A         =	num_channels
         __A         =	image_size
         __A         =	min_resolution
         __A         =	max_resolution
         __A         =	do_resize
         __A         =	size
         __A         =	do_normalize





     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         return {
             # here we create 2 clusters for the sake of simplicity
             "clusters": np.asarray(
                 [
                     [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                     [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
                 ]				),
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
         }


@require_torch
@require_vision
class       __lowercase      (	lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				ImageGPTImageProcessor if is_vision_available() else None




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	ImageGPTImageProcessingTester(self				)




     @property
     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""clusters"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""do_resize"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""size"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""do_normalize"""				)				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size ,	{"""height""": 18, """width""": 18}				)

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




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)
         __A         =	json.loads(image_processor.to_json_string()				)
         for key, value in self.image_processor_dict.items():
             if key == "clusters":
                 self.assertTrue(np.array_equal(UpperCamelCase_ ,	obj[key]				)				)
             else:
                 self.assertEqual(obj[key] ,	UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
             __A         =	os.path.join(UpperCamelCase_ ,	"""image_processor.json"""				)
             image_processor_first.to_json_file(UpperCamelCase_				)
             __A         =	self.image_processing_class.from_json_file(UpperCamelCase_				).to_dict()

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




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)

         with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(UpperCamelCase_				)
             __A         =	self.image_processing_class.from_pretrained(UpperCamelCase_				).to_dict()

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





     @unittest.skip("""ImageGPT requires clusters at initialization"""				)
     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         pass



def 			_SCREAMING_SNAKE_CASE						( )						->      Dict:





    """simple docstring"""

    __A         =	load_dataset("""hf-internal-testing/fixtures_image_utils"""			,				split="""test"""    )

    __A         =	Image.open(dataset[4]["""file"""]    )
    __A         =	Image.open(dataset[5]["""file"""]    )

    __A         =	[imagea, imagea]

    return images






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







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""				)

         __A         =	prepare_images()

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

         self.assertIsInstance(encoding.input_ids ,	torch.LongTensor				)
         self.assertEqual(encoding.input_ids.shape ,	(1, 1_024)				)

         __A         =	[306, 191, 191]
         self.assertEqual(encoding.input_ids[0, :3].tolist() ,	UpperCamelCase_				)

         # test batched
         __A         =	image_processing(UpperCamelCase_ ,	return_tensors="""pt"""				)

         self.assertIsInstance(encoding.input_ids ,	torch.LongTensor				)
         self.assertEqual(encoding.input_ids.shape ,	(2, 1_024)				)

         __A         =	[303, 13, 13]
         self.assertEqual(encoding.input_ids[1, -3:].tolist() ,	UpperCamelCase_				)












import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str    )						->      str:





    """simple docstring"""

    if "cls_token" in name:
        __A         =	name.replace("""cls_token"""			,				"""vit.embeddings.cls_token"""    )
    if "mask_token" in name:
        __A         =	name.replace("""mask_token"""			,				"""decoder.mask_token"""    )
    if "decoder_pos_embed" in name:
        __A         =	name.replace("""decoder_pos_embed"""			,				"""decoder.decoder_pos_embed"""    )
    if "pos_embed" in name and "decoder" not in name:
        __A         =	name.replace("""pos_embed"""			,				"""vit.embeddings.position_embeddings"""    )
    if "patch_embed.proj" in name:
        __A         =	name.replace("""patch_embed.proj"""			,				"""vit.embeddings.patch_embeddings.projection"""    )
    if "patch_embed.norm" in name:
        __A         =	name.replace("""patch_embed.norm"""			,				"""vit.embeddings.norm"""    )
    if "decoder_blocks" in name:
        __A         =	name.replace("""decoder_blocks"""			,				"""decoder.decoder_layers"""    )
    if "blocks" in name:
        __A         =	name.replace("""blocks"""			,				"""vit.encoder.layer"""    )
    if "attn.proj" in name:
        __A         =	name.replace("""attn.proj"""			,				"""attention.output.dense"""    )
    if "attn" in name:
        __A         =	name.replace("""attn"""			,				"""attention.self"""    )
    if "norm1" in name:
        __A         =	name.replace("""norm1"""			,				"""layernorm_before"""    )
    if "norm2" in name:
        __A         =	name.replace("""norm2"""			,				"""layernorm_after"""    )
    if "mlp.fc1" in name:
        __A         =	name.replace("""mlp.fc1"""			,				"""intermediate.dense"""    )
    if "mlp.fc2" in name:
        __A         =	name.replace("""mlp.fc2"""			,				"""output.dense"""    )
    if "decoder_embed" in name:
        __A         =	name.replace("""decoder_embed"""			,				"""decoder.decoder_embed"""    )
    if "decoder_norm" in name:
        __A         =	name.replace("""decoder_norm"""			,				"""decoder.decoder_norm"""    )
    if "decoder_pred" in name:
        __A         =	name.replace("""decoder_pred"""			,				"""decoder.decoder_pred"""    )
    if "norm.weight" in name and "decoder" not in name:
        __A         =	name.replace("""norm.weight"""			,				"""vit.layernorm.weight"""    )
    if "norm.bias" in name and "decoder" not in name:
        __A         =	name.replace("""norm.bias"""			,				"""vit.layernorm.bias"""    )

    return name



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			Dict    )						->      str:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        __A         =	orig_state_dict.pop(__lowercase    )

        if "qkv" in key:
            __A         =	key.split("""."""    )
            __A         =	int(key_split[1]    )
            if "decoder_blocks" in key:
                __A         =	config.decoder_hidden_size
                __A         =	"""decoder.decoder_layers."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]
            else:
                __A         =	config.hidden_size
                __A         =	"""vit.encoder.layer."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]

        else:
            __A         =	val

    return orig_state_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			str    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	ViTMAEConfig()
    if "large" in checkpoint_url:
        __A         =	1_0_2_4
        __A         =	4_0_9_6
        __A         =	2_4
        __A         =	1_6
    elif "huge" in checkpoint_url:
        __A         =	1_4
        __A         =	1_2_8_0
        __A         =	5_1_2_0
        __A         =	3_2
        __A         =	1_6

    __A         =	ViTMAEForPreTraining(__lowercase    )

    __A         =	torch.hub.load_state_dict_from_url(__lowercase			,				map_location="""cpu"""    )["""model"""]

    __A         =	ViTMAEImageProcessor(size=config.image_size    )

    __A         =	convert_state_dict(__lowercase			,				__lowercase    )

    model.load_state_dict(__lowercase    )
    model.eval()

    __A         =	"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

    __A         =	Image.open(requests.get(__lowercase			,				stream=__lowercase    ).raw    )
    __A         =	ViTMAEImageProcessor(size=config.image_size    )
    __A         =	image_processor(images=__lowercase			,				return_tensors="""pt"""    )

    # forward pass
    torch.manual_seed(2    )
    __A         =	model(**__lowercase    )
    __A         =	outputs.logits

    if "large" in checkpoint_url:
        __A         =	torch.tensor(
            [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]    )
    elif "huge" in checkpoint_url:
        __A         =	torch.tensor(
            [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]    )
    else:
        __A         =	torch.tensor(
            [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]    )

    # verify logits
    assert torch.allclose(logits[0, :3, :3]			,				__lowercase			,				atol=1E-4    )

    print(f"Saving model to {pytorch_dump_folder_path}"    )
    model.save_pretrained(__lowercase    )

    print(f"Saving image processor to {pytorch_dump_folder_path}"    )
    image_processor.save_pretrained(__lowercase    )


if __name__ == "__main__":
  __a      :       List[str]				  =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
      type=str,
      help="URL of the checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  __a      :       List[str]				  =   parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




