


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_						:			Optional[Any]													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__	,a__=False):
				_SCREAMING_SNAKE_CASE			  =[]
				for i in range(config.num_hidden_layers):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight"))
								rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias"))
								rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight"))
								rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias"))
								rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight"))
								rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias"))
								rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight"))
								rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias"))
								rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight"))
								rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias"))

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ('''cls_token''', '''deit.embeddings.cls_token'''),
				        ('''dist_token''', '''deit.embeddings.distillation_token'''),
				        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
				        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
				        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
				    ])

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ('''norm.weight''', '''layernorm.weight'''),
								        ('''norm.bias''', '''layernorm.bias'''),
								        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
								        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
								    ])

								# if just the base model, we should remove "deit" from all keys that start with "deit"
								_SCREAMING_SNAKE_CASE			  =[(pair[0], pair[1][4:]) if pair[1].startswith('''deit''') else pair for pair in rename_keys]
				else:
								# layernorm + classification heads
								rename_keys.extend(
								    [
								        ('''norm.weight''', '''deit.layernorm.weight'''),
								        ('''norm.bias''', '''deit.layernorm.bias'''),
								        ('''head.weight''', '''cls_classifier.weight'''),
								        ('''head.bias''', '''cls_classifier.bias'''),
								        ('''head_dist.weight''', '''distillation_classifier.weight'''),
								        ('''head_dist.bias''', '''distillation_classifier.bias'''),
								    ])

				return rename_keys







def 						lowerCamelCase(   a__	,a__	,a__=False):
				for i in range(config.num_hidden_layers):
								if base_model:
												_SCREAMING_SNAKE_CASE			  =''''''
								else:
												_SCREAMING_SNAKE_CASE			  ='''deit.'''
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE			  =state_dict.pop(f"blocks.{i}.attn.qkv.weight")
								_SCREAMING_SNAKE_CASE			  =state_dict.pop(f"blocks.{i}.attn.qkv.bias")
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    : config.hidden_size, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[: config.hidden_size]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[-config.hidden_size :]







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =dct.pop(a__)
				_SCREAMING_SNAKE_CASE			  =val







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(a__	,stream=a__).raw)
				return im







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =DeiTConfig()
				# all deit models have fine-tuned heads
				_SCREAMING_SNAKE_CASE			  =False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				_SCREAMING_SNAKE_CASE			  =1000
				_SCREAMING_SNAKE_CASE			  ='''huggingface/label-files'''
				_SCREAMING_SNAKE_CASE			  ='''imagenet-1k-id2label.json'''
				_SCREAMING_SNAKE_CASE			  =json.load(open(hf_hub_download(a__	,a__	,repo_type='''dataset''')	,'''r'''))
				_SCREAMING_SNAKE_CASE			  ={int(a__): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE			  =idalabel
				_SCREAMING_SNAKE_CASE			  ={v: k for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE			  =int(deit_name[-6:-4])
				_SCREAMING_SNAKE_CASE			  =int(deit_name[-3:])
				# size of the architecture
				if deit_name[9:].startswith('''tiny'''):
								_SCREAMING_SNAKE_CASE			  =192
								_SCREAMING_SNAKE_CASE			  =768
								_SCREAMING_SNAKE_CASE			  =12
								_SCREAMING_SNAKE_CASE			  =3
				elif deit_name[9:].startswith('''small'''):
								_SCREAMING_SNAKE_CASE			  =384
								_SCREAMING_SNAKE_CASE			  =1536
								_SCREAMING_SNAKE_CASE			  =12
								_SCREAMING_SNAKE_CASE			  =6
				if deit_name[9:].startswith('''base'''):
								pass
				elif deit_name[4:].startswith('''large'''):
								_SCREAMING_SNAKE_CASE			  =1024
								_SCREAMING_SNAKE_CASE			  =4096
								_SCREAMING_SNAKE_CASE			  =24
								_SCREAMING_SNAKE_CASE			  =16

				# load original model from timm
				_SCREAMING_SNAKE_CASE			  =timm.create_model(a__	,pretrained=a__)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				_SCREAMING_SNAKE_CASE			  =timm_model.state_dict()
				_SCREAMING_SNAKE_CASE			  =create_rename_keys(a__	,a__)
				for src, dest in rename_keys:
								rename_key(a__	,a__	,a__)
				read_in_q_k_v(a__	,a__	,a__)

				# load HuggingFace model
				_SCREAMING_SNAKE_CASE			  =DeiTForImageClassificationWithTeacher(a__).eval()
				model.load_state_dict(a__)

				# Check outputs on an image, prepared by DeiTImageProcessor
				_SCREAMING_SNAKE_CASE			  =int(
				    (256 / 224) * config.image_size)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
				_SCREAMING_SNAKE_CASE			  =DeiTImageProcessor(size=a__	,crop_size=config.image_size)
				_SCREAMING_SNAKE_CASE			  =image_processor(images=prepare_img()	,return_tensors='''pt''')
				_SCREAMING_SNAKE_CASE			  =encoding['''pixel_values''']
				_SCREAMING_SNAKE_CASE			  =model(a__)

				_SCREAMING_SNAKE_CASE			  =timm_model(a__)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(a__	,outputs.logits	,atol=1e-3)

				Path(a__).mkdir(exist_ok=a__)
				print(f"Saving model {deit_name} to {pytorch_dump_folder_path}")
				model.save_pretrained(a__)
				print(f"Saving image processor to {pytorch_dump_folder_path}")
				image_processor.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_						:			int													=							argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--deit_name''',
							    default='''vit_deit_base_distilled_patch16_224''',
							    type=str,
							    help='''Name of the DeiT timm model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							snake_case_						:			int													=							parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



import unittest

import numpy as np
import requests

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

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							snake_case_						:			Any													=							False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor

class 						A__							(		unittest.TestCase  ):




						def __init__(       self					:      Tuple				,    _a					:      str				,    _a					:      List[str]=7				,    _a					:      Union[str, Any]=3				,    _a					:      List[str]=18				,    _a					:      List[Any]=30				,    _a					:      Optional[Any]=400				,    _a					:      Dict=None				,    _a					:      Union[str, Any]=True				,    _a					:      List[str]=True				,    _a					:      Optional[Any]=None				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''height''': 20, '''width''': 20}
										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =num_channels
										_SCREAMING_SNAKE_CASE			  =image_size
										_SCREAMING_SNAKE_CASE			  =min_resolution
										_SCREAMING_SNAKE_CASE			  =max_resolution
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =do_convert_rgb
										_SCREAMING_SNAKE_CASE			  =[512, 1024, 2048, 4096]
										_SCREAMING_SNAKE_CASE			  =patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




						def 							__UpperCamelCase				(       self					:      str  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
										_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(_a				,    stream=_a  ).raw  ).convert('''RGB'''  )
										return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self  )




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      int  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor_tester.prepare_dummy_image()

										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										_SCREAMING_SNAKE_CASE			  =2048

										_SCREAMING_SNAKE_CASE			  =image_processor(_a				,    return_tensors='''pt'''				,    max_patches=_a  )
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,    torch.tensor(0.06_06  )				,    atol=1E-3				,    rtol=1E-3  )  )




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    Image.Image  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    Image.Image  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										_SCREAMING_SNAKE_CASE			  =True

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														with self.assertRaises(_a  ):
																		_SCREAMING_SNAKE_CASE			  =image_processor(
																		    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches

														_SCREAMING_SNAKE_CASE			  ='''Hello'''

														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random numpy tensors
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a				,    numpify=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    np.ndarray  )

										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PyTorch tensors
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a				,    torchify=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    torch.Tensor  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self				,    num_channels=4  )
										_SCREAMING_SNAKE_CASE			  =3




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										_SCREAMING_SNAKE_CASE			  =prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_a  )
										for image in image_inputs:
														self.assertIsInstance(_a				,    Image.Image  )

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * (self.image_processor_tester.num_channels - 1)
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )