




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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							Optional[int]:


			'''simple docstring'''







			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'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"
						SCREAMING_SNAKE_CASE_								=						[(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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=False )			->							str:


			'''simple docstring'''







			for i in range(config.num_hidden_layers ):
						if base_model:
									SCREAMING_SNAKE_CASE_								=						''
						else:
									SCREAMING_SNAKE_CASE_								=						'vit.'
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						SCREAMING_SNAKE_CASE_								=						state_dict.pop(F'module.blocks.{i}.attn.qkv.weight' )
						SCREAMING_SNAKE_CASE_								=						state_dict.pop(F'module.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 lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[
			    '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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						dct.pop(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						val

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						ViTMSNConfig()
			SCREAMING_SNAKE_CASE_								=						10_00

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

			if "s16" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						3_84
						SCREAMING_SNAKE_CASE_								=						15_36
						SCREAMING_SNAKE_CASE_								=						6
			elif "l16" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						10_24
						SCREAMING_SNAKE_CASE_								=						40_96
						SCREAMING_SNAKE_CASE_								=						24
						SCREAMING_SNAKE_CASE_								=						16
						SCREAMING_SNAKE_CASE_								=						0.1
			elif "b4" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						4
			elif "l7" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						7
						SCREAMING_SNAKE_CASE_								=						10_24
						SCREAMING_SNAKE_CASE_								=						40_96
						SCREAMING_SNAKE_CASE_								=						24
						SCREAMING_SNAKE_CASE_								=						16
						SCREAMING_SNAKE_CASE_								=						0.1

			SCREAMING_SNAKE_CASE_								=						ViTMSNModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE       ,    map_location='cpu' )['target_encoder']

			SCREAMING_SNAKE_CASE_								=						ViTImageProcessor(size=config.image_size )

			remove_projection_head(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						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()

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

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

			# forward pass
			torch.manual_seed(2 )
			SCREAMING_SNAKE_CASE_								=						model(**SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						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:
						SCREAMING_SNAKE_CASE_								=						torch.tensor([[-1.0915, -1.4876, -1.1809]] )
			elif "b16" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						torch.tensor([[14.2889, -18.9045, 11.7281]] )
			elif "l16" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						torch.tensor([[41.5028, -22.8681, 45.6475]] )
			elif "b4" in checkpoint_url:
						SCREAMING_SNAKE_CASE_								=						torch.tensor([[-4.3868, 5.2932, -0.4137]] )
			else:
						SCREAMING_SNAKE_CASE_								=						torch.tensor([[-0.1792, -0.6465, 2.4263]] )

			# 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__":
				SCREAMING_SNAKE_CASE__     :     int		    =       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."
				)

				SCREAMING_SNAKE_CASE__     :     Any		    =       parser.parse_args()
				convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
