
import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=False							):
	lowercase       =    []
	for i in range(config.num_hidden_layers							):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')							)
		rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')							)
		rename_keys.append(
		    (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')							)
		rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')							)
		rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')							)
		rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')							)
		rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')							)
		rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')							)
		rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')							)
		rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')							)

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

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

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

	return rename_keys






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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	# projection head is used in the self-supervised pre-training in MSN,
	# for downstream task it's not needed.
	lowercase       =    [
	    'module.fc.fc1.weight',
	    'module.fc.fc1.bias',
	    'module.fc.bn1.weight',
	    'module.fc.bn1.bias',
	    'module.fc.bn1.running_mean',
	    'module.fc.bn1.running_var',
	    'module.fc.bn1.num_batches_tracked',
	    'module.fc.fc2.weight',
	    'module.fc.fc2.bias',
	    'module.fc.bn2.weight',
	    'module.fc.bn2.bias',
	    'module.fc.bn2.running_mean',
	    'module.fc.bn2.running_var',
	    'module.fc.bn2.num_batches_tracked',
	    'module.fc.fc3.weight',
	    'module.fc.fc3.bias',
	]
	for k in ignore_keys:
		state_dict.pop(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    dct.pop(__SCREAMING_SNAKE_CASE							)
	lowercase       =    val






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    ViTMSNConfig()
	lowercase       =    1000

	lowercase       =    'datasets/huggingface/label-files'
	lowercase       =    'imagenet-1k-id2label.json'
	lowercase       =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							'r'							)							)
	lowercase       =    {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
	lowercase       =    idalabel
	lowercase       =    {v: k for k, v in idalabel.items()}

	if "s16" in checkpoint_url:
		lowercase       =    384
		lowercase       =    1536
		lowercase       =    6
	elif "l16" in checkpoint_url:
		lowercase       =    1024
		lowercase       =    4096
		lowercase       =    24
		lowercase       =    16
		lowercase       =    0.1
	elif "b4" in checkpoint_url:
		lowercase       =    4
	elif "l7" in checkpoint_url:
		lowercase       =    7
		lowercase       =    1024
		lowercase       =    4096
		lowercase       =    24
		lowercase       =    16
		lowercase       =    0.1

	lowercase       =    ViTMSNModel(__SCREAMING_SNAKE_CASE							)

	lowercase       =    torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE ,							map_location='cpu'							)['target_encoder']

	lowercase       =    ViTImageProcessor(size=config.image_size							)

	remove_projection_head(__SCREAMING_SNAKE_CASE							)
	lowercase       =    create_rename_keys(__SCREAMING_SNAKE_CASE ,							base_model=__SCREAMING_SNAKE_CASE							)

	for src, dest in rename_keys:
		rename_key(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	read_in_q_k_v(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							base_model=__SCREAMING_SNAKE_CASE							)

	model.load_state_dict(__SCREAMING_SNAKE_CASE							)
	model.eval()

	lowercase       =    'http://images.cocodataset.org/val2017/000000039769.jpg'

	lowercase       =    Image.open(requests.get(__SCREAMING_SNAKE_CASE ,							stream=__SCREAMING_SNAKE_CASE							).raw							)
	lowercase       =    ViTImageProcessor(
	    size=config.image_size ,							image_mean=__SCREAMING_SNAKE_CASE ,							image_std=__SCREAMING_SNAKE_CASE							)
	lowercase       =    image_processor(images=__SCREAMING_SNAKE_CASE ,							return_tensors='pt'							)

	# forward pass
	torch.manual_seed(2							)
	lowercase       =    model(**__SCREAMING_SNAKE_CASE							)
	lowercase       =    outputs.last_hidden_state

	# The following Colab Notebook was used to generate these outputs:
	# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
	if "s16" in checkpoint_url:
		lowercase       =    torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]							)
	elif "b16" in checkpoint_url:
		lowercase       =    torch.tensor([[14.28_89, -18.90_45, 11.72_81]]							)
	elif "l16" in checkpoint_url:
		lowercase       =    torch.tensor([[41.50_28, -22.86_81, 45.64_75]]							)
	elif "b4" in checkpoint_url:
		lowercase       =    torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]							)
	else:
		lowercase       =    torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]							)

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

	print(F'''Saving model to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(__SCREAMING_SNAKE_CASE							)

	print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
	image_processor.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
	    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.'''
	)

	UpperCAmelCase	      = parser.parse_args()
	convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



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        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    tempfile.mkdtemp()

		lowercase       =    [
		    '[UNK]',
		    '[CLS]',
		    '[SEP]',
		    '[PAD]',
		    '[MASK]',
		    '的',
		    '价',
		    '格',
		    '是',
		    '15',
		    '便',
		    'alex',
		    '##andra',
		    '，',
		    '。',
		    '-',
		    't',
		    'shirt',
		]
		lowercase       =    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]       )       )

		lowercase       =    {
		    'do_resize': True,
		    'size': {'height': 224, 'width': 224},
		    'do_center_crop': True,
		    'crop_size': {'height': 18, 'width': 18},
		    'do_normalize': True,
		    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
		    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
		    'do_convert_rgb': True,
		}
		lowercase       =    os.path.join(self.tmpdirname		,    snake_case       )
		with open(self.image_processor_file		,    'w'		,    encoding='utf-8'       ) as fp:
			json.dump(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizer.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizerFast.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		shutil.rmtree(self.tmpdirname       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    [np.random.randint(255		,    size=(3, 30, 400)		,    dtype=np.uinta       )]

		lowercase       =    [Image.fromarray(np.moveaxis(snake_case		,    0		,    -1       )       ) for x in image_inputs]

		return image_inputs





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_rust_tokenizer()
		lowercase       =    self.get_image_processor()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		processor_slow.save_pretrained(self.tmpdirname       )
		lowercase       =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,    use_fast=snake_case       )

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		processor_fast.save_pretrained(self.tmpdirname       )
		lowercase       =    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		,    snake_case       )
		self.assertIsInstance(processor_fast.tokenizer		,    snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,    image_processor=self.get_image_processor()       )
		processor.save_pretrained(self.tmpdirname       )

		lowercase       =    self.get_tokenizer(cls_token='(CLS)'		,    sep_token='(SEP)'       )
		lowercase       =    self.get_image_processor(do_normalize=snake_case       )

		lowercase       =    ChineseCLIPProcessor.from_pretrained(
		    self.tmpdirname		,    cls_token='(CLS)'		,    sep_token='(SEP)'		,    do_normalize=snake_case       )

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    self.prepare_image_inputs()

		lowercase       =    image_processor(snake_case		,    return_tensors='np'       )
		lowercase       =    processor(images=snake_case		,    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'

		lowercase       =    processor(text=snake_case       )

		lowercase       =    tokenizer(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'
		lowercase       =    self.prepare_image_inputs()

		lowercase       =    processor(text=snake_case		,    images=snake_case       )

		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(snake_case       ):
			processor()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		lowercase       =    processor.batch_decode(snake_case       )
		lowercase       =    tokenizer.batch_decode(snake_case       )

		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    'Alexandra，T-shirt的价格是15便士。'
		lowercase       =    self.prepare_image_inputs()

		lowercase       =    processor(text=snake_case		,    images=snake_case       )

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

