


"""simple docstring"""


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]  ):
 lowercase__       :       List[Any]				=						[]
 embed.append(
     (
         f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
         f'''stage{idx}.patch_embed.proj.weight''',
     )  )
 embed.append(
     (
         f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
         f'''stage{idx}.patch_embed.proj.bias''',
     )  )
 embed.append(
     (
         f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
         f'''stage{idx}.patch_embed.norm.weight''',
     )  )
 embed.append(
     (
         f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
         f'''stage{idx}.patch_embed.norm.bias''',
     )  )
 return embed


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							Dict  ):
 lowercase__       :       List[str]				=						[]
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
         f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
         f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
         f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
         f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
         f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
     )  )
 attention_weights.append(
     (
         f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
         f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
     )  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''')  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''')  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''')  )
 attention_weights.append(
     (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''')  )
 return attention_weights


def    _lowerCamelCase   (     lowerCamelCase__	:							Dict  ):
 lowercase__       :       List[str]				=						[]
 token.append((f'''cvt.encoder.stages.{idx}.cls_token''', """stage2.cls_token""")  )
 return token


def    _lowerCamelCase   (     ):
 lowercase__       :       int				=						[]
 head.append(("""layernorm.weight""", """norm.weight""")  )
 head.append(("""layernorm.bias""", """norm.bias""")  )
 head.append(("""classifier.weight""", """head.weight""")  )
 head.append(("""classifier.bias""", """head.bias""")  )
 return head


def    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       int				=						"""imagenet-1k-id2label.json"""
 lowercase__       :       str				=						10_00

 lowercase__       :       List[Any]				=						"""huggingface/label-files"""
 lowercase__       :       List[Any]				=						num_labels
 lowercase__       :       Dict				=						json.load(open(cached_download(hf_hub_url(lowerCamelCase__       ,		lowerCamelCase__       ,		repo_type="""dataset"""  )  )       ,		"""r"""  )  )
 lowercase__       :       Optional[int]				=						{int(lowerCamelCase__  ): v for k, v in idalabel.items()}

 lowercase__       :       Tuple				=						idalabel
 lowercase__       :       Optional[Any]				=						{v: k for k, v in idalabel.items()}

 lowercase__       :       int				=						CvtConfig(num_labels=lowerCamelCase__       ,		idalabel=lowerCamelCase__       ,		labelaid=lowerCamelCase__  )

 # For depth size 13 (13 = 1+2+10)
 if cvt_model.rsplit("""/"""       ,		1  )[-1][4:6] == "13":
  lowercase__       :       Any				=						[1, 2, 10]

 # For depth size 21 (21 = 1+4+16)
 elif cvt_model.rsplit("""/"""       ,		1  )[-1][4:6] == "21":
  lowercase__       :       str				=						[1, 4, 16]

 # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
 else:
  lowercase__       :       Any				=						[2, 2, 20]
  lowercase__       :       Any				=						[3, 12, 16]
  lowercase__       :       Union[str, Any]				=						[1_92, 7_68, 10_24]

 lowercase__       :       Dict				=						CvtForImageClassification(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""  )
 lowercase__       :       Optional[Any]				=						image_size
 lowercase__       :       str				=						torch.load(lowerCamelCase__       ,		map_location=torch.device("""cpu"""  )  )

 lowercase__       :       List[Any]				=						OrderedDict()
 lowercase__       :       List[Any]				=						[]

 for idx in range(len(config.depth  )  ):
  if config.cls_token[idx]:
   lowercase__       :       Tuple				=						list_of_state_dict + cls_token(lowerCamelCase__  )
  lowercase__       :       int				=						list_of_state_dict + embeddings(lowerCamelCase__  )
  for cnt in range(config.depth[idx]  ):
   lowercase__       :       Dict				=						list_of_state_dict + attention(lowerCamelCase__       ,		lowerCamelCase__  )

 lowercase__       :       Optional[int]				=						list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(lowerCamelCase__  )
 for i in range(len(lowerCamelCase__  )  ):
  lowercase__       :       List[str]				=						original_weights[list_of_state_dict[i][1]]

 model.load_state_dict(lowerCamelCase__  )
 model.save_pretrained(lowerCamelCase__  )
 image_processor.save_pretrained(lowerCamelCase__  )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
   __snake_case    					=					argparse.ArgumentParser()
   parser.add_argument(
       '--cvt_model',
       default='cvt-w24',
       type=str,
       help='Name of the cvt model you\'d like to convert.',
   )
   parser.add_argument(
       '--image_size',
       default=384,
       type=int,
       help='Input Image Size',
   )
   parser.add_argument(
       '--cvt_file_name',
       default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
       type=str,
       help='Input Image Size',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )

   __snake_case    					=					parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



"""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 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

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


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

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







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

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

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)