



'''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_     :       int								=						logging.get_logger(__name__)
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				int=False				):
							SCREAMING_SNAKE_CASE					:      Dict     					=			[]
							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					:      Union[str, 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   (						A  :				str					,		A  :				List[str]					,		A  :				Optional[Any]=False				):
							for i in range(config.num_hidden_layers				):
														if base_model:
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			''''''
														else:
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			'''deit.'''
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE					:      List[str]     					=			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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					:      Optional[Any]     					=			in_proj_weight[
														    : config.hidden_size, :
														]
														SCREAMING_SNAKE_CASE					:      int     					=			in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE					:      str     					=			in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE					:      int     					=			in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE					:      Dict     					=			in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			in_proj_bias[-config.hidden_size :]
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				int					,		A  :				Dict				):
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			dct.pop(A				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			val
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE					:      List[str]     					=			Image.open(requests.get(A					,		stream=A				).raw				)
							return im
@torch.no_grad()
def 					UpperCAmelCase   (						A  :				Optional[int]					,		A  :				Optional[Any]				):
							SCREAMING_SNAKE_CASE					:      int     					=			DeiTConfig()
							# all deit models have fine-tuned heads
							SCREAMING_SNAKE_CASE					:      str     					=			False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1000
							SCREAMING_SNAKE_CASE					:      Dict     					=			'''huggingface/label-files'''
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''imagenet-1k-id2label.json'''
							SCREAMING_SNAKE_CASE					:      Dict     					=			json.load(open(hf_hub_download(A					,		A					,		repo_type='''dataset'''				)					,		'''r'''				)				)
							SCREAMING_SNAKE_CASE					:      Any     					=			{int(A				): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			idalabel
							SCREAMING_SNAKE_CASE					:      Dict     					=			{v: k for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			int(deit_name[-6:-4]				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			int(deit_name[-3:]				)
							# size of the architecture
							if deit_name[9:].startswith('''tiny'''				):
														SCREAMING_SNAKE_CASE					:      int     					=			192
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			768
														SCREAMING_SNAKE_CASE					:      str     					=			12
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			3
							elif deit_name[9:].startswith('''small'''				):
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			384
														SCREAMING_SNAKE_CASE					:      Tuple     					=			1536
														SCREAMING_SNAKE_CASE					:      Any     					=			12
														SCREAMING_SNAKE_CASE					:      List[str]     					=			6
							if deit_name[9:].startswith('''base'''				):
														pass
							elif deit_name[4:].startswith('''large'''				):
														SCREAMING_SNAKE_CASE					:      Tuple     					=			1024
														SCREAMING_SNAKE_CASE					:      List[str]     					=			4096
														SCREAMING_SNAKE_CASE					:      Dict     					=			24
														SCREAMING_SNAKE_CASE					:      Any     					=			16

							# load original model from timm
							SCREAMING_SNAKE_CASE					:      Dict     					=			timm.create_model(A					,		pretrained=A				)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			timm_model.state_dict()
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			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					:      Union[str, Any]     					=			DeiTForImageClassificationWithTeacher(A				).eval()
							model.load_state_dict(A				)

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

							SCREAMING_SNAKE_CASE					:      List[str]     					=			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__":
							lowerCAmelCase_     :       Dict								=						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_     :       List[str]								=						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 BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tempfile.mkdtemp()

													SCREAMING_SNAKE_CASE					:      int     					=			[
													    '''[UNK]''',
													    '''[CLS]''',
													    '''[SEP]''',
													    '''[PAD]''',
													    '''[MASK]''',
													    '''的''',
													    '''价''',
													    '''格''',
													    '''是''',
													    '''15''',
													    '''便''',
													    '''alex''',
													    '''##andra''',
													    '''，''',
													    '''。''',
													    '''-''',
													    '''t''',
													    '''shirt''',
													]
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']				)
													with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''				) as vocab_writer:
																				vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{
													    '''do_resize''': True,
													    '''size''': {'''height''': 2_24, '''width''': 2_24},
													    '''do_center_crop''': True,
													    '''crop_size''': {'''height''': 18, '''width''': 18},
													    '''do_normalize''': True,
													    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
													    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
													    '''do_convert_rgb''': True,
													}
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			os.path.join(self.tmpdirname				,    lowerCAmelCase__				)
													with open(self.image_processor_file				,    '''w'''				,    encoding='''utf-8'''				) as fp:
																				json.dump(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				,    **lowerCAmelCase__   :      int				):

													"""simple docstring"""
													return BertTokenizer.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return BertTokenizerFast.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			[np.random.randint(2_55				,    size=(3, 30, 4_00)				,    dtype=np.uinta				)]

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[Image.fromarray(np.moveaxis(lowerCAmelCase__				,    0				,    -1				)				) for x in image_inputs]

													return image_inputs







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_image_processor()

													SCREAMING_SNAKE_CASE					:      Dict     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)
													processor_slow.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,    use_fast=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)
													processor_fast.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ChineseCLIPProcessor.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				,    lowerCAmelCase__				)
													self.assertIsInstance(processor_fast.tokenizer				,    lowerCAmelCase__				)

													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				,    lowerCAmelCase__				)
													self.assertIsInstance(processor_fast.image_processor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,    image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_tokenizer(cls_token='''(CLS)'''				,    sep_token='''(SEP)'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor(do_normalize=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ChineseCLIPProcessor.from_pretrained(
													    self.tmpdirname				,    cls_token='''(CLS)'''				,    sep_token='''(SEP)'''				,    do_normalize=lowerCAmelCase__				)

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

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







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			image_processor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			processor(images=lowerCAmelCase__				,    return_tensors='''np'''				)

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







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''Alexandra，T-shirt的价格是15便士。'''

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer(lowerCAmelCase__				)

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







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			'''Alexandra，T-shirt的价格是15便士。'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

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

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







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      Any     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_image_processor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Dict     					=			ChineseCLIPProcessor(tokenizer=lowerCAmelCase__				,    image_processor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''Alexandra，T-shirt的价格是15便士。'''
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.prepare_image_inputs()

													SCREAMING_SNAKE_CASE					:      Any     					=			processor(text=lowerCAmelCase__				,    images=lowerCAmelCase__				)

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




