






'''simple docstring'''




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()
lowerCAmelCase__          =						logging.get_logger(__name__)







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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     int:
		UpperCamelCase__				:					Union[str, Any]					=					dct.pop(lowerCamelCase_)
		UpperCamelCase__				:					Optional[Any]					=					val







def       __UpperCAmelCase						(  )							->     str:
		UpperCamelCase__				:					Union[str, Any]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase__				:					List[str]					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw)
		return im







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					Optional[int]					=					DeiTConfig()
		# all deit models have fine-tuned heads
		UpperCamelCase__				:					Tuple					=					False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		UpperCamelCase__				:					Tuple					=					1_000
		UpperCamelCase__				:					List[Any]					=					'huggingface/label-files'
		UpperCamelCase__				:					Any					=					'imagenet-1k-id2label.json'
		UpperCamelCase__				:					int					=					json.load(open(hf_hub_download(lowerCamelCase_     ,      lowerCamelCase_     ,      repo_type='dataset')     ,      'r'))
		UpperCamelCase__				:					str					=					{int(lowerCamelCase_): v for k, v in idalabel.items()}
		UpperCamelCase__				:					Tuple					=					idalabel
		UpperCamelCase__				:					int					=					{v: k for k, v in idalabel.items()}
		UpperCamelCase__				:					int					=					int(deit_name[-6:-4])
		UpperCamelCase__				:					int					=					int(deit_name[-3:])
		# size of the architecture
		if deit_name[9:].startswith('tiny'):
				UpperCamelCase__				:					Union[str, Any]					=					192
				UpperCamelCase__				:					int					=					768
				UpperCamelCase__				:					List[str]					=					12
				UpperCamelCase__				:					Union[str, Any]					=					3
		elif deit_name[9:].startswith('small'):
				UpperCamelCase__				:					str					=					384
				UpperCamelCase__				:					Union[str, Any]					=					1_536
				UpperCamelCase__				:					List[str]					=					12
				UpperCamelCase__				:					List[Any]					=					6
		if deit_name[9:].startswith('base'):
				pass
		elif deit_name[4:].startswith('large'):
				UpperCamelCase__				:					List[Any]					=					1_024
				UpperCamelCase__				:					Union[str, Any]					=					4_096
				UpperCamelCase__				:					List[Any]					=					24
				UpperCamelCase__				:					str					=					16

		# load original model from timm
		UpperCamelCase__				:					Optional[Any]					=					timm.create_model(lowerCamelCase_     ,      pretrained=lowerCamelCase_)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		UpperCamelCase__				:					List[Any]					=					timm_model.state_dict()
		UpperCamelCase__				:					List[Any]					=					create_rename_keys(lowerCamelCase_     ,      lowerCamelCase_)
		for src, dest in rename_keys:
				rename_key(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		read_in_q_k_v(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

		# load HuggingFace model
		UpperCamelCase__				:					List[str]					=					DeiTForImageClassificationWithTeacher(lowerCamelCase_).eval()
		model.load_state_dict(lowerCamelCase_)

		# Check outputs on an image, prepared by DeiTImageProcessor
		UpperCamelCase__				:					Dict					=					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
		UpperCamelCase__				:					int					=					DeiTImageProcessor(size=lowerCamelCase_     ,      crop_size=config.image_size)
		UpperCamelCase__				:					List[Any]					=					image_processor(images=prepare_img()     ,      return_tensors='pt')
		UpperCamelCase__				:					Optional[Any]					=					encoding['pixel_values']
		UpperCamelCase__				:					str					=					model(lowerCamelCase_)

		UpperCamelCase__				:					List[str]					=					timm_model(lowerCamelCase_)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(lowerCamelCase_     ,      outputs.logits     ,      atol=1e-3)

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


if __name__ == "__main__":
		lowerCAmelCase__          =						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.'
		)

		lowerCAmelCase__          =						parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)












'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					int					=					tempfile.mkdtemp()

			# fmt: off
			UpperCamelCase__				:					Union[str, Any]					=					['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
			# fmt: on
			UpperCamelCase__				:					Dict					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))
			UpperCamelCase__				:					Optional[Any]					=					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			UpperCamelCase__				:					Union[str, Any]					=					{'unk_token': '<unk>'}

			UpperCamelCase__				:					Union[str, Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__				:					Optional[Any]					=					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['merges_file'])
			with open(self.vocab_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write(json.dumps(UpperCAmelCase_) + '\n')
			with open(self.merges_file  ,					'w'  ,					encoding='utf-8') as fp:
					fp.write('\n'.join(UpperCAmelCase_))

			UpperCamelCase__				:					Dict					=					{
			    'do_resize': True,
			    'size': 20,
			    'do_center_crop': True,
			    'crop_size': 18,
			    'do_normalize': True,
			    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
			    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
			}
			UpperCamelCase__				:					Any					=					os.path.join(self.tmpdirname  ,					UpperCAmelCase_)
			with open(self.image_processor_file  ,					'w'  ,					encoding='utf-8') as fp:
					json.dump(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPTokenizer.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					**UpperCAmelCase_   :						str):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					**UpperCAmelCase_   :						Union[str, Any]):
			return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			shutil.rmtree(self.tmpdirname)

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					List[str]					=					[np.random.randint(255  ,					size=(3, 30, 400)  ,					dtype=np.uinta)]

			UpperCamelCase__				:					List[str]					=					[Image.fromarray(np.moveaxis(UpperCAmelCase_  ,					0  ,					-1)) for x in image_inputs]

			return image_inputs

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_tokenizer()
			UpperCamelCase__				:					Optional[Any]					=					self.get_rust_tokenizer()
			UpperCamelCase__				:					Any					=					self.get_image_processor()

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_slow.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Any					=					CLIPProcessor.from_pretrained(self.tmpdirname  ,					use_fast=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)
			processor_fast.save_pretrained(self.tmpdirname)
			UpperCamelCase__				:					Optional[int]					=					CLIPProcessor.from_pretrained(self.tmpdirname)

			self.assertEqual(processor_slow.tokenizer.get_vocab()  ,					tokenizer_slow.get_vocab())
			self.assertEqual(processor_fast.tokenizer.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertEqual(tokenizer_slow.get_vocab()  ,					tokenizer_fast.get_vocab())
			self.assertIsInstance(processor_slow.tokenizer  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.tokenizer  ,					UpperCAmelCase_)

			self.assertEqual(processor_slow.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertEqual(processor_fast.image_processor.to_json_string()  ,					image_processor.to_json_string())
			self.assertIsInstance(processor_slow.image_processor  ,					UpperCAmelCase_)
			self.assertIsInstance(processor_fast.image_processor  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					Union[str, Any]					=					CLIPProcessor(tokenizer=self.get_tokenizer()  ,					image_processor=self.get_image_processor())
			processor.save_pretrained(self.tmpdirname)

			UpperCamelCase__				:					List[str]					=					self.get_tokenizer(bos_token='(BOS)'  ,					eos_token='(EOS)')
			UpperCamelCase__				:					Tuple					=					self.get_image_processor(do_normalize=UpperCAmelCase_  ,					padding_value=1.0)

			UpperCamelCase__				:					Dict					=					CLIPProcessor.from_pretrained(
			    self.tmpdirname  ,					bos_token='(BOS)'  ,					eos_token='(EOS)'  ,					do_normalize=UpperCAmelCase_  ,					padding_value=1.0)

			self.assertEqual(processor.tokenizer.get_vocab()  ,					tokenizer_add_kwargs.get_vocab())
			self.assertIsInstance(processor.tokenizer  ,					UpperCAmelCase_)

			self.assertEqual(processor.image_processor.to_json_string()  ,					image_processor_add_kwargs.to_json_string())
			self.assertIsInstance(processor.image_processor  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					int					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					self.prepare_image_inputs()

			UpperCamelCase__				:					int					=					image_processor(UpperCAmelCase_  ,					return_tensors='np')
			UpperCamelCase__				:					Optional[int]					=					processor(images=UpperCAmelCase_  ,					return_tensors='np')

			for key in input_image_proc.keys():
					self.assertAlmostEqual(input_image_proc[key].sum()  ,					input_processor[key].sum()  ,					delta=1e-2)

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					self.get_image_processor()
			UpperCamelCase__				:					Dict					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					'lower newer'

			UpperCamelCase__				:					Union[str, Any]					=					processor(text=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					tokenizer(UpperCAmelCase_)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]  ,					encoded_processor[key])

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Optional[int]					=					self.get_image_processor()
			UpperCamelCase__				:					List[str]					=					self.get_tokenizer()

			UpperCamelCase__				:					List[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					'lower newer'
			UpperCamelCase__				:					List[Any]					=					self.prepare_image_inputs()

			UpperCamelCase__				:					str					=					processor(text=UpperCAmelCase_  ,					images=UpperCAmelCase_)

			self.assertListEqual(list(inputs.keys())  ,					['input_ids', 'attention_mask', 'pixel_values'])

			# test if it raises when no input is passed
			with pytest.raises(UpperCAmelCase_):
					processor()

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Any					=					self.get_image_processor()
			UpperCamelCase__				:					Dict					=					self.get_tokenizer()

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					Optional[Any]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			UpperCamelCase__				:					List[Any]					=					processor.batch_decode(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					tokenizer.batch_decode(UpperCAmelCase_)

			self.assertListEqual(UpperCAmelCase_  ,					UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Union[str, Any]					=					self.get_image_processor()
			UpperCamelCase__				:					List[str]					=					self.get_tokenizer()

			UpperCamelCase__				:					Optional[Any]					=					CLIPProcessor(tokenizer=UpperCAmelCase_  ,					image_processor=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					'lower newer'
			UpperCamelCase__				:					Optional[int]					=					self.prepare_image_inputs()

			UpperCamelCase__				:					List[str]					=					processor(text=UpperCAmelCase_  ,					images=UpperCAmelCase_)

			self.assertListEqual(list(inputs.keys())  ,					processor.model_input_names)




