
"""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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_       :    List[Any]						      =			logging.get_logger(__name__)
def 				A_  (__a					, __a=False					):

					'''simple docstring'''





					A_								=    []
					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'vit.encoder.layer.{i}.layernorm_before.weight')					)
										rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')					)
										rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')					)
										rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')					)
										rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')					)
										rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')					)
										rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')					)
										rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')					)
										rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')					)
										rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')					)

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ("cls_token", "vit.embeddings.cls_token"),
					        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
					        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
					        ("pos_embed", "vit.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 "vit" from all keys that start with "vit"
										A_								=    [(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 				A_  (__a					, __a					, __a=False					):

					'''simple docstring'''





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

					'''simple docstring'''





					A_								=    ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(__a					, __a					)
def 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





					A_								=    dct.pop(__a					)
					A_								=    val
def 				A_  ():

					'''simple docstring'''





					A_								=    "http://images.cocodataset.org/val2017/000000039769.jpg"
					A_								=    Image.open(requests.get(__a					, stream=__a					).raw					)
					return im
@torch.no_grad()
def 				A_  (__a					, __a					):

					'''simple docstring'''





					A_								=    ViTConfig()
					A_								=    False
					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					if vit_name[-5:] == "in21k":
										A_								=    True
										A_								=    int(vit_name[-12:-10]					)
										A_								=    int(vit_name[-9:-6]					)
					else:
										A_								=    1000
										A_								=    "huggingface/label-files"
										A_								=    "imagenet-1k-id2label.json"
										A_								=    json.load(open(hf_hub_download(__a					, __a					, repo_type="dataset"					)					, "r"					)					)
										A_								=    {int(__a					): v for k, v in idalabel.items()}
										A_								=    idalabel
										A_								=    {v: k for k, v in idalabel.items()}
										A_								=    int(vit_name[-6:-4]					)
										A_								=    int(vit_name[-3:]					)
					# size of the architecture
					if "deit" in vit_name:
										if vit_name[9:].startswith("tiny"					):
															A_								=    192
															A_								=    768
															A_								=    12
															A_								=    3
										elif vit_name[9:].startswith("small"					):
															A_								=    384
															A_								=    1536
															A_								=    12
															A_								=    6
										else:
															pass
					else:
										if vit_name[4:].startswith("small"					):
															A_								=    768
															A_								=    2304
															A_								=    8
															A_								=    8
										elif vit_name[4:].startswith("base"					):
															pass
										elif vit_name[4:].startswith("large"					):
															A_								=    1024
															A_								=    4096
															A_								=    24
															A_								=    16
										elif vit_name[4:].startswith("huge"					):
															A_								=    1280
															A_								=    5120
															A_								=    32
															A_								=    16

    # load original model from timm
					A_								=    timm.create_model(__a					, pretrained=__a					)
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					A_								=    timm_model.state_dict()
					if base_model:
										remove_classification_head_(__a					)
					A_								=    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
					if vit_name[-5:] == "in21k":
										A_								=    ViTModel(__a					).eval()
					else:
										A_								=    ViTForImageClassification(__a					).eval()
					model.load_state_dict(__a					)

					# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
					if "deit" in vit_name:
										A_								=    DeiTImageProcessor(size=config.image_size					)
					else:
										A_								=    ViTImageProcessor(size=config.image_size					)
					A_								=    image_processor(images=prepare_img()					, return_tensors="pt"					)
					A_								=    encoding["pixel_values"]
					A_								=    model(__a					)

					if base_model:
										A_								=    timm_model.forward_features(__a					)
										assert timm_pooled_output.shape == outputs.pooler_output.shape
										assert torch.allclose(__a					, outputs.pooler_output					, atol=1e-3					)
					else:
										A_								=    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 {vit_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__":
		UpperCamelCase_       :    int						      =			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--vit_name''',
		    default='''vit_base_patch16_224''',
		    type=str,
		    help='''Name of the ViT 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.'''
		)

		UpperCamelCase_       :    Tuple						      =			parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






"""simple docstring"""





import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
		from transformers.trainer import DEFAULT_CALLBACKS

		from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			def __init__(					self    :       Optional[int]						)   ->			str:







								"""simple docstring"""
								A_								=    []




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       List[str]							, _snake_case    :       Optional[int]							, _snake_case    :       Optional[int]							, **_snake_case    :       str						)   ->			int:







								"""simple docstring"""
								self.events.append("on_init_end"						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Union[str, Any]							, _snake_case    :       List[Any]							, _snake_case    :       Tuple							, **_snake_case    :       Tuple						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_train_begin"						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Any							, _snake_case    :       Optional[Any]							, _snake_case    :       Tuple							, **_snake_case    :       Optional[int]						)   ->			str:







								"""simple docstring"""
								self.events.append("on_train_end"						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       str							, **_snake_case    :       Optional[int]						)   ->			Dict:







								"""simple docstring"""
								self.events.append("on_epoch_begin"						)




			def   lowerCamelCase__  (					self    :       Optional[int]							, _snake_case    :       Optional[int]							, _snake_case    :       Any							, _snake_case    :       Optional[Any]							, **_snake_case    :       Optional[Any]						)   ->			str:







								"""simple docstring"""
								self.events.append("on_epoch_end"						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[str]							, _snake_case    :       str							, _snake_case    :       Optional[Any]							, **_snake_case    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_step_begin"						)




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       Any							, **_snake_case    :       Union[str, Any]						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_step_end"						)




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       Dict							, _snake_case    :       Dict							, _snake_case    :       Union[str, Any]							, **_snake_case    :       List[str]						)   ->			Optional[int]:







								"""simple docstring"""
								self.events.append("on_evaluate"						)




			def   lowerCamelCase__  (					self    :       Optional[int]							, _snake_case    :       Optional[Any]							, _snake_case    :       int							, _snake_case    :       int							, **_snake_case    :       List[Any]						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_predict"						)




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Union[str, Any]							, _snake_case    :       List[str]							, _snake_case    :       Optional[Any]							, **_snake_case    :       List[Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								self.events.append("on_save"						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Dict							, _snake_case    :       List[Any]							, _snake_case    :       Optional[int]							, **_snake_case    :       Dict						)   ->			str:







								"""simple docstring"""
								self.events.append("on_log"						)






			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       Optional[Any]							, _snake_case    :       str							, _snake_case    :       int							, **_snake_case    :       List[Any]						)   ->			int:







								"""simple docstring"""
								self.events.append("on_prediction_step"						)





@require_torch
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								A_								=    tempfile.mkdtemp()




			def   lowerCamelCase__  (					self    :       str						)   ->			Tuple:







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




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Any=0							, _snake_case    :       Optional[int]=0							, _snake_case    :       int=64							, _snake_case    :       Optional[int]=64							, _snake_case    :       List[Any]=None							, _snake_case    :       List[Any]=False							, **_snake_case    :       Optional[int]						)   ->			List[Any]:







								"""simple docstring"""
								# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
								# its set to False since the tests later on depend on its value.
								A_								=    RegressionDataset(length=_snake_case						)
								A_								=    RegressionDataset(length=_snake_case						)
								A_								=    RegressionModelConfig(a=_snake_case							, b=_snake_case						)
								A_								=    RegressionPreTrainedModel(_snake_case						)

								A_								=    TrainingArguments(self.output_dir							, disable_tqdm=_snake_case							, report_to=[]							, **_snake_case						)
								return Trainer(
								    _snake_case							, _snake_case							, train_dataset=_snake_case							, eval_dataset=_snake_case							, callbacks=_snake_case							, )




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       List[Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								self.assertEqual(len(_snake_case						)							, len(_snake_case						)						)

								# Order doesn't matter
								A_								=    sorted(_snake_case							, key=lambda _snake_case						: cb.__name__ if isinstance(_snake_case							, _snake_case						) else cb.__class__.__name__						)
								A_								=    sorted(_snake_case							, key=lambda _snake_case						: cb.__name__ if isinstance(_snake_case							, _snake_case						) else cb.__class__.__name__						)

								for cba, cba in zip(_snake_case							, _snake_case						):
													if isinstance(_snake_case							, _snake_case						) and isinstance(_snake_case							, _snake_case						):
																		self.assertEqual(_snake_case							, _snake_case						)
													elif isinstance(_snake_case							, _snake_case						) and not isinstance(_snake_case							, _snake_case						):
																		self.assertEqual(_snake_case							, cba.__class__						)
													elif not isinstance(_snake_case							, _snake_case						) and isinstance(_snake_case							, _snake_case						):
																		self.assertEqual(cba.__class__							, _snake_case						)
													else:
																		self.assertEqual(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Union[str, Any]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    ["on_init_end", "on_train_begin"]
								A_								=    0
								A_								=    len(trainer.get_eval_dataloader()						)
								A_								=    ["on_prediction_step"] * len(trainer.get_eval_dataloader()						) + ["on_log", "on_evaluate"]
								for _ in range(trainer.state.num_train_epochs						):
													expected_events.append("on_epoch_begin"						)
													for _ in range(_snake_case						):
																		step += 1
																		expected_events += ["on_step_begin", "on_step_end"]
																		if step % trainer.args.logging_steps == 0:
																							expected_events.append("on_log"						)
																		if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
																							expected_events += evaluation_events.copy()
																		if step % trainer.args.save_steps == 0:
																							expected_events.append("on_save"						)
													expected_events.append("on_epoch_end"						)
													if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
																		expected_events += evaluation_events.copy()
								expected_events += ["on_log", "on_train_end"]
								return expected_events




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			int:







								"""simple docstring"""
								A_								=    self.get_trainer()
								A_								=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

								# Callbacks passed at init are added to the default callbacks
								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]						)
								expected_callbacks.append(_snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

								# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
								A_								=    self.get_trainer(disable_tqdm=_snake_case						)
								A_								=    DEFAULT_CALLBACKS.copy() + [PrinterCallback]
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)




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







								"""simple docstring"""
								A_								=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
								A_								=    self.get_trainer()

								# We can add, pop, or remove by class name
								trainer.remove_callback(_snake_case						)
								expected_callbacks.remove(_snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

								A_								=    self.get_trainer()
								A_								=    trainer.pop_callback(_snake_case						)
								self.assertEqual(cb.__class__							, _snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

								trainer.add_callback(_snake_case						)
								expected_callbacks.insert(0							, _snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

								# We can also add, pop, or remove by instance
								A_								=    self.get_trainer()
								A_								=    trainer.callback_handler.callbacks[0]
								trainer.remove_callback(_snake_case						)
								expected_callbacks.remove(_snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

								A_								=    self.get_trainer()
								A_								=    trainer.callback_handler.callbacks[0]
								A_								=    trainer.pop_callback(_snake_case						)
								self.assertEqual(_snake_case							, _snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

								trainer.add_callback(_snake_case						)
								expected_callbacks.insert(0							, _snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)






			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								import warnings

								# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
								warnings.simplefilter(action="ignore"							, category=_snake_case						)

								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]						)
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

								# Independent log/save/eval
								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]							, logging_steps=5						)
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]							, save_steps=5						)
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]							, eval_steps=5							, evaluation_strategy="steps"						)
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]							, evaluation_strategy="epoch"						)
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

								# A bit of everything
								A_								=    self.get_trainer(
								    callbacks=[MyTestTrainerCallback]							, logging_steps=3							, save_steps=10							, eval_steps=5							, evaluation_strategy="steps"							, )
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

								# warning should be emitted for duplicated callbacks
								with patch("transformers.trainer_callback.logger.warning"						) as warn_mock:
													A_								=    self.get_trainer(
													    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]							, )
													assert str(_snake_case						) in warn_mock.call_args[0][0]




