



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()
snake_case    :						Any  =						logging.get_logger(__name__)
def 	snake_case__       (					__lowercase							,     __lowercase=False       )		->		List[Any]:







						"""simple docstring"""

						A__	:							str						    = []
						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__	:							List[str]						    = [(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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase=False       )		->		Union[str, Any]:







						"""simple docstring"""

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







						"""simple docstring"""

						A__	:							str						    = ["head.weight", "head.bias"]
						for k in ignore_keys:
												state_dict.pop(__lowercase							,     __lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Optional[Any]:







						"""simple docstring"""

						A__	:							int						    = dct.pop(__lowercase       )
						A__	:							Union[str, Any]						    = val
def 	snake_case__       (					)		->		Optional[int]:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
						A__	:							Dict						    = Image.open(requests.get(__lowercase							,     stream=__lowercase       ).raw       )
						return im
@torch.no_grad()
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		Union[str, Any]:







						"""simple docstring"""

						A__	:							Dict						    = ViTConfig()
						A__	:							Union[str, Any]						    = False
						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						if vit_name[-5:] == "in21k":
												A__	:							Optional[Any]						    = True
												A__	:							int						    = int(vit_name[-1_2:-1_0]       )
												A__	:							int						    = int(vit_name[-9:-6]       )
						else:
												A__	:							Dict						    = 1_0_0_0
												A__	:							List[Any]						    = "huggingface/label-files"
												A__	:							Any						    = "imagenet-1k-id2label.json"
												A__	:							int						    = json.load(open(hf_hub_download(__lowercase							,     __lowercase							,     repo_type="dataset"       )							,     "r"       )       )
												A__	:							Optional[int]						    = {int(__lowercase       ): v for k, v in idalabel.items()}
												A__	:							List[Any]						    = idalabel
												A__	:							Dict						    = {v: k for k, v in idalabel.items()}
												A__	:							Union[str, Any]						    = int(vit_name[-6:-4]       )
												A__	:							Optional[int]						    = int(vit_name[-3:]       )
						# size of the architecture
						if "deit" in vit_name:
												if vit_name[9:].startswith("tiny"       ):
																		A__	:							Dict						    = 1_9_2
																		A__	:							Optional[Any]						    = 7_6_8
																		A__	:							Any						    = 1_2
																		A__	:							Dict						    = 3
												elif vit_name[9:].startswith("small"       ):
																		A__	:							List[Any]						    = 3_8_4
																		A__	:							Dict						    = 1_5_3_6
																		A__	:							int						    = 1_2
																		A__	:							Optional[Any]						    = 6
												else:
																		pass
						else:
												if vit_name[4:].startswith("small"       ):
																		A__	:							Union[str, Any]						    = 7_6_8
																		A__	:							Union[str, Any]						    = 2_3_0_4
																		A__	:							Optional[int]						    = 8
																		A__	:							Union[str, Any]						    = 8
												elif vit_name[4:].startswith("base"       ):
																		pass
												elif vit_name[4:].startswith("large"       ):
																		A__	:							int						    = 1_0_2_4
																		A__	:							Optional[Any]						    = 4_0_9_6
																		A__	:							Tuple						    = 2_4
																		A__	:							List[Any]						    = 1_6
												elif vit_name[4:].startswith("huge"       ):
																		A__	:							Union[str, Any]						    = 1_2_8_0
																		A__	:							str						    = 5_1_2_0
																		A__	:							str						    = 3_2
																		A__	:							List[str]						    = 1_6

    # load original model from timm
						A__	:							Any						    = timm.create_model(__lowercase							,     pretrained=__lowercase       )
						timm_model.eval()

						# load state_dict of original model, remove and rename some keys
						A__	:							Dict						    = timm_model.state_dict()
						if base_model:
												remove_classification_head_(__lowercase       )
						A__	:							Dict						    = create_rename_keys(__lowercase							,     __lowercase       )
						for src, dest in rename_keys:
												rename_key(__lowercase							,     __lowercase							,     __lowercase       )
						read_in_q_k_v(__lowercase							,     __lowercase							,     __lowercase       )

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												A__	:							Tuple						    = ViTModel(__lowercase       ).eval()
						else:
												A__	:							Dict						    = ViTForImageClassification(__lowercase       ).eval()
						model.load_state_dict(__lowercase       )

						# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
						if "deit" in vit_name:
												A__	:							Dict						    = DeiTImageProcessor(size=config.image_size       )
						else:
												A__	:							Union[str, Any]						    = ViTImageProcessor(size=config.image_size       )
						A__	:							str						    = image_processor(images=prepare_img()							,     return_tensors="pt"       )
						A__	:							int						    = encoding["pixel_values"]
						A__	:							Optional[int]						    = model(__lowercase       )

						if base_model:
												A__	:							List[Any]						    = timm_model.forward_features(__lowercase       )
												assert timm_pooled_output.shape == outputs.pooler_output.shape
												assert torch.allclose(__lowercase							,     outputs.pooler_output							,     atol=1E-3       )
						else:
												A__	:							str						    = timm_model(__lowercase       )
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(__lowercase							,     outputs.logits							,     atol=1E-3       )

						Path(__lowercase       ).mkdir(exist_ok=__lowercase       )
						print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'       )
						model.save_pretrained(__lowercase       )
						print(F'Saving image processor to {pytorch_dump_folder_path}'       )
						image_processor.save_pretrained(__lowercase       )


if __name__ == "__main__":
		snake_case    :						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.'
		)

		snake_case    :						Union[str, Any]  =						parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

snake_case    :						Optional[int]  =						get_logger()

snake_case    :						Optional[dict]  =						None




class 							lowerCAmelCase__  (	TensorFormatter[Mapping, 'jax.Array', Mapping]						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Optional[int]=None			,		_A      :     str=None			,		**_A      :     Tuple):
											super().__init__(features=_A)
											import jax
											from jaxlib.xla_client import Device

											if isinstance(_A			,		_A):
																	raise ValueError(
																	    F'Expected {device} to be a `str` not {type(_A)}, as `jaxlib.xla_extension.Device` '
																	    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
																	    "the device with `str()` to get its string identifier that will be internally mapped "
																	    "to the actual `jaxlib.xla_extension.Device`.")
											A__	:							Union[str, Any]						    = device if isinstance(_A			,		_A) else str(jax.devices()[0])
											# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
											# with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Any						    = self._map_devices_to_str()
											if self.device not in list(DEVICE_MAPPING.keys()):
																	logger.warning(
																	    F'Device with string identifier {self.device} not listed among the available '
																	    F'devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default '
																	    F'device: {str(jax.devices()[0])}.')
																	A__	:							str						    = str(jax.devices()[0])
											A__	:							List[Any]						    = jnp_array_kwargs







					@staticmethod
					def  _lowercase			(       ):
											import jax

											return {str(_A): device for device in jax.devices()}







					def  _lowercase			(       self      :     Tuple			,		_A      :     int):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		_A) and column:
																	if all(
																	    isinstance(_A			,		jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
																							return jnp.stack(_A			,		axis=0)
											return column







					def  _lowercase			(       self      :     List[str]			,		_A      :     Union[str, Any]):
											import jax
											import jax.numpy as jnp

											if isinstance(_A			,		(str, bytes, type(_A))):
																	return value
											elif isinstance(_A			,		(np.character, np.ndarray)) and np.issubdtype(value.dtype			,		np.character):
																	return value.tolist()

											A__	:							Dict						    = {}

											if isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.integer):
																	# the default int precision depends on the jax config
																	# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
																	if jax.config.jax_enable_xaa:
																							A__	:							Union[str, Any]						    = {"dtype": jnp.intaa}
																	else:
																							A__	:							int						    = {"dtype": jnp.intaa}
											elif isinstance(_A			,		(np.number, np.ndarray)) and np.issubdtype(value.dtype			,		np.floating):
																	A__	:							str						    = {"dtype": jnp.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(_A			,		PIL.Image.Image):
																							A__	:							Any						    = np.asarray(_A)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																	A__	:							Optional[Any]						    = self._map_devices_to_str()

											with jax.default_device(DEVICE_MAPPING[self.device]):
																	# calling jnp.array on a np.ndarray does copy the data
																	# see https://github.com/google/jax/issues/4486
																	return jnp.array(_A			,		**{**default_dtype, **self.jnp_array_kwargs})







					def  _lowercase			(       self      :     Dict			,		_A      :     Any):
											import jax

											# support for torch, tf, jax etc.
											if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(_A			,		torch.Tensor):
																							return self._tensorize(data_struct.detach().cpu().numpy()[()])
											if hasattr(_A			,		"__array__") and not isinstance(_A			,		jax.Array):
																	A__	:							Any						    = data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_A			,		np.ndarray):
																	if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																							return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											elif isinstance(_A			,		(list, tuple)):
																	return self._consolidate([self.recursive_tensorize(_A) for substruct in data_struct])
											return self._tensorize(_A)







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     dict):
											return map_nested(self._recursive_tensorize			,		_A			,		map_list=_A)







					def  _lowercase			(       self      :     List[str]			,		_A      :     pa.Table):
											A__	:							Dict						    = self.numpy_arrow_extractor().extract_row(_A)
											A__	:							int						    = self.python_features_decoder.decode_row(_A)
											return self.recursive_tensorize(_A)







					def  _lowercase			(       self      :     int			,		_A      :     pa.Table):
											A__	:							int						    = self.numpy_arrow_extractor().extract_column(_A)
											A__	:							Optional[int]						    = self.python_features_decoder.decode_column(_A			,		pa_table.column_names[0])
											A__	:							Dict						    = self.recursive_tensorize(_A)
											A__	:							List[Any]						    = self._consolidate(_A)
											return column







					def  _lowercase			(       self      :     Tuple			,		_A      :     pa.Table):
											A__	:							Any						    = self.numpy_arrow_extractor().extract_batch(_A)
											A__	:							str						    = self.python_features_decoder.decode_batch(_A)
											A__	:							Optional[Any]						    = self.recursive_tensorize(_A)
											for column_name in batch:
																	A__	:							Optional[int]						    = self._consolidate(batch[column_name])
											return batch