import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_lowerCamelCase					:   Tuple							=		'''bert-base-cased'''
_lowerCamelCase					:   Optional[int]							=		'''google/pegasus-xsum'''
_lowerCamelCase					:   Tuple							=		[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_lowerCamelCase					:   Dict							=		['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_lowerCamelCase					:   List[Any]							=		'''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase					:   Optional[int]							=		'''sshleifer/bart-tiny-random'''
_lowerCamelCase					:   str							=		'''sshleifer/tiny-mbart'''
_lowerCamelCase					:   List[str]							=		'''sshleifer/tiny-marian-en-de'''





def 	__lowerCamelCase							(UpperCAmelCase__     :							Path      , UpperCAmelCase__     :							list  ):
					SCREAMING_SNAKE_CASE    			=       "\n".join(UpperCAmelCase__  )
					Path(UpperCAmelCase__  ).open("w"  ).writelines(UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Dict  ):
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(UpperCAmelCase__      , F"{split}.source"  )      , UpperCAmelCase__  )
										_dump_articles(os.path.join(UpperCAmelCase__      , F"{split}.target"  )      , UpperCAmelCase__  )
					return tmp_dir


class 						lowercase    (     a     ):




							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]	,							)
							@slow
							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					List[str]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
												SCREAMING_SNAKE_CASE    			=       max(len(tokenizer.encode(_UpperCamelCase      )      ) for a in ARTICLES      )
												SCREAMING_SNAKE_CASE    			=       max(len(tokenizer.encode(_UpperCamelCase      )      ) for a in SUMMARIES      )
												SCREAMING_SNAKE_CASE    			=       4
												SCREAMING_SNAKE_CASE    			=       8
												assert max_len_target > max_src_len  # Will be truncated
												assert max_len_source > max_src_len  # Will be truncated
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
												SCREAMING_SNAKE_CASE    			=       SeqaSeqDataset(
												    _UpperCamelCase	,							data_dir=_UpperCamelCase	,							type_path="train"	,							max_source_length=_UpperCamelCase	,							max_target_length=_UpperCamelCase	,							src_lang=_UpperCamelCase	,							tgt_lang=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       DataLoader(_UpperCamelCase	,							batch_size=2	,							collate_fn=train_dataset.collate_fn      )
												for batch in dataloader:
																	assert isinstance(_UpperCamelCase	,							_UpperCamelCase      )
																	assert batch["attention_mask"].shape == batch["input_ids"].shape
																	# show that articles were trimmed.
																	assert batch["input_ids"].shape[1] == max_src_len
																	# show that targets are the same len
																	assert batch["labels"].shape[1] == max_tgt_len
																	if tok_name != MBART_TINY:
																						continue
																	# check language codes in correct place
																	SCREAMING_SNAKE_CASE    			=       shift_tokens_right(batch["labels"]	,							tokenizer.pad_token_id      )
																	assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
																	assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
																	assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
																	assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

																	break  # No need to test every batch




							@parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
												SCREAMING_SNAKE_CASE    			=       max(len(tokenizer.encode(_UpperCamelCase      )      ) for a in ARTICLES      )
												SCREAMING_SNAKE_CASE    			=       max(len(tokenizer.encode(_UpperCamelCase      )      ) for a in SUMMARIES      )
												SCREAMING_SNAKE_CASE    			=       4
												SCREAMING_SNAKE_CASE    			=       LegacySeqaSeqDataset(
												    _UpperCamelCase	,							data_dir=_UpperCamelCase	,							type_path="train"	,							max_source_length=20	,							max_target_length=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       DataLoader(_UpperCamelCase	,							batch_size=2	,							collate_fn=train_dataset.collate_fn      )
												for batch in dataloader:
																	assert batch["attention_mask"].shape == batch["input_ids"].shape
																	# show that articles were trimmed.
																	assert batch["input_ids"].shape[1] == max_len_source
																	assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
																	# show that targets were truncated
																	assert batch["labels"].shape[1] == trunc_target  # Truncated
																	assert max_len_target > trunc_target  # Truncated
																	break  # No need to test every batch




							def 	__snake_case(						self   :					List[str]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"      )

												SCREAMING_SNAKE_CASE    			=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
												SCREAMING_SNAKE_CASE    			=       tmp_dir.joinpath("train.source"      ).open().readlines()
												SCREAMING_SNAKE_CASE    			=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
												pack_data_dir(_UpperCamelCase	,							_UpperCamelCase	,							128	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       {x.name for x in tmp_dir.iterdir()}
												SCREAMING_SNAKE_CASE    			=       {x.name for x in save_dir.iterdir()}
												SCREAMING_SNAKE_CASE    			=       save_dir.joinpath("train.source"      ).open().readlines()
												# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
												# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
												assert len(_UpperCamelCase      ) < len(_UpperCamelCase      )
												assert len(_UpperCamelCase      ) == 1
												assert len(packed_examples[0]      ) == sum(len(_UpperCamelCase      ) for x in orig_examples      )
												assert orig_paths == new_paths




							@pytest.mark.skipif(not FAIRSEQ_AVAILABLE	,							reason="This test requires fairseq"      )
							def 	__snake_case(						self   :					str      )					->				str:



												'''simple docstring'''

												if not FAIRSEQ_AVAILABLE:
																	return
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self._get_dataset(max_len=64      )
												SCREAMING_SNAKE_CASE    			=       64
												SCREAMING_SNAKE_CASE    			=       ds.make_dynamic_sampler(_UpperCamelCase	,							required_batch_size_multiple=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       [len(_UpperCamelCase      ) for x in batch_sampler]
												assert len(set(_UpperCamelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
												assert sum(_UpperCamelCase      ) == len(_UpperCamelCase      )  # no dropped or added examples
												SCREAMING_SNAKE_CASE    			=       DataLoader(_UpperCamelCase	,							batch_sampler=_UpperCamelCase	,							collate_fn=ds.collate_fn	,							num_workers=2      )
												SCREAMING_SNAKE_CASE    			=       []
												SCREAMING_SNAKE_CASE    			=       []
												for batch in data_loader:
																	SCREAMING_SNAKE_CASE    			=       batch["input_ids"].shape
																	SCREAMING_SNAKE_CASE    			=       src_shape[0]
																	assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																	SCREAMING_SNAKE_CASE    			=       np.product(batch["input_ids"].shape      )
																	num_src_per_batch.append(_UpperCamelCase      )
																	if num_src_tokens > (max_tokens * 1.1):
																						failures.append(_UpperCamelCase      )
												assert num_src_per_batch[0] == max(_UpperCamelCase      )
												if failures:
																	raise AssertionError(F"too many tokens in {len(_UpperCamelCase      )} batches"      )




							def 	__snake_case(						self   :					Any      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self._get_dataset(max_len=512      )
												SCREAMING_SNAKE_CASE    			=       2
												SCREAMING_SNAKE_CASE    			=       ds.make_sortish_sampler(_UpperCamelCase	,							shuffle=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       DataLoader(_UpperCamelCase	,							batch_size=_UpperCamelCase	,							collate_fn=ds.collate_fn	,							num_workers=2      )
												SCREAMING_SNAKE_CASE    			=       DataLoader(_UpperCamelCase	,							batch_size=_UpperCamelCase	,							collate_fn=ds.collate_fn	,							num_workers=2	,							sampler=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       tokenizer.pad_token_id

												def count_pad_tokens(_UpperCamelCase   :					Dict	,							_UpperCamelCase   :					Dict="input_ids"      ):
																	return [batch[k].eq(_UpperCamelCase      ).sum().item() for batch in data_loader]

												assert sum(count_pad_tokens(_UpperCamelCase	,							k="labels"      )      ) < sum(count_pad_tokens(_UpperCamelCase	,							k="labels"      )      )
												assert sum(count_pad_tokens(_UpperCamelCase      )      ) < sum(count_pad_tokens(_UpperCamelCase      )      )
												assert len(_UpperCamelCase      ) == len(_UpperCamelCase      )




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					str=1_000	,							_UpperCamelCase   :					List[Any]=128      )					->				Tuple:



												'''simple docstring'''

												if os.getenv("USE_REAL_DATA"	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       "examples/seq2seq/wmt_en_ro"
																	SCREAMING_SNAKE_CASE    			=       max_len * 2 * 64
																	if not Path(_UpperCamelCase      ).joinpath("train.len"      ).exists():
																						save_len_file(_UpperCamelCase	,							_UpperCamelCase      )
												else:
																	SCREAMING_SNAKE_CASE    			=       "examples/seq2seq/test_data/wmt_en_ro"
																	SCREAMING_SNAKE_CASE    			=       max_len * 4
																	save_len_file(_UpperCamelCase	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       SeqaSeqDataset(
												    _UpperCamelCase	,							data_dir=_UpperCamelCase	,							type_path="train"	,							max_source_length=_UpperCamelCase	,							max_target_length=_UpperCamelCase	,							n_obs=_UpperCamelCase	,							)
												return ds, max_tokens, tokenizer




							def 	__snake_case(						self   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self._get_dataset()
												SCREAMING_SNAKE_CASE    			=       set(DistributedSortishSampler(_UpperCamelCase	,							256	,							num_replicas=2	,							rank=0	,							add_extra_examples=_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       set(DistributedSortishSampler(_UpperCamelCase	,							256	,							num_replicas=2	,							rank=1	,							add_extra_examples=_UpperCamelCase      )      )
												assert idsa.intersection(_UpperCamelCase      ) == set()





							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]	,							)
							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					List[str]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained(_UpperCamelCase	,							use_fast=_UpperCamelCase      )
												if tok_name == MBART_TINY:
																	SCREAMING_SNAKE_CASE    			=       SeqaSeqDataset(
																	    _UpperCamelCase	,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )	,							type_path="train"	,							max_source_length=4	,							max_target_length=8	,							src_lang="EN"	,							tgt_lang="FR"	,							)
																	SCREAMING_SNAKE_CASE    			=       train_dataset.dataset_kwargs
																	assert "src_lang" in kwargs and "tgt_lang" in kwargs
												else:
																	SCREAMING_SNAKE_CASE    			=       SeqaSeqDataset(
																	    _UpperCamelCase	,							data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )	,							type_path="train"	,							max_source_length=4	,							max_target_length=8	,							)
																	SCREAMING_SNAKE_CASE    			=       train_dataset.dataset_kwargs
																	assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																	assert len(_UpperCamelCase      ) == 1 if tok_name == BART_TINY else len(_UpperCamelCase      ) == 0





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 	__lowerCamelCase							(UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Dict=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"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 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							int      , UpperCAmelCase__     :							str=False  ):
					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 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple  ):
					SCREAMING_SNAKE_CASE    			=       ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any  ):
					# projection head is used in the self-supervised pre-training in MSN,
					# for downstream task it's not needed.
					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(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Dict  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str  ):
					SCREAMING_SNAKE_CASE    			=       ViTMSNConfig()
					SCREAMING_SNAKE_CASE    			=       1_0_0_0

					SCREAMING_SNAKE_CASE    			=       "datasets/huggingface/label-files"
					SCREAMING_SNAKE_CASE    			=       "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(UpperCAmelCase__      , UpperCAmelCase__  )      , "r"  )  )
					SCREAMING_SNAKE_CASE    			=       {int(UpperCAmelCase__  ): 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_8_4
										SCREAMING_SNAKE_CASE    			=       1_5_3_6
										SCREAMING_SNAKE_CASE    			=       6
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										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    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1

					SCREAMING_SNAKE_CASE    			=       ViTMSNModel(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      , map_location="cpu"  )["target_encoder"]

					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(size=config.image_size  )

					remove_projection_head(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       create_rename_keys(UpperCAmelCase__      , base_model=UpperCAmelCase__  )

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

					model.load_state_dict(UpperCAmelCase__  )
					model.eval()

					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"

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

					# forward pass
					torch.manual_seed(2  )
					SCREAMING_SNAKE_CASE    			=       model(**UpperCAmelCase__  )
					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]      , UpperCAmelCase__      , atol=1e-4  )

					print(F"Saving model to {pytorch_dump_folder_path}"  )
					model.save_pretrained(UpperCAmelCase__  )

					print(F"Saving image processor to {pytorch_dump_folder_path}"  )
					image_processor.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
							_lowerCamelCase					:   Optional[Any]							=		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.'''
							)

							_lowerCamelCase					:   Optional[Any]							=		parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




