






"""simple docstring"""
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__="attention"					):
	A_		:						Dict	    =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]					)
	A_		:						Dict	    =   k_tmp.reshape(k_tmp.shape[0]   ,   k_tmp.shape[1] * k_tmp.shape[2]					)
	A_		:						str	    =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]					)
	A_		:						List[Any]	    =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]   ,   o_tmp.shape[2]					)
	A_		:						Optional[Any]	    =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]					)
	A_		:						Any	    =   q_tmp.reshape(q_tmp.shape[0]   ,   q_tmp.shape[1] * q_tmp.shape[2]					)
	A_		:						List[Any]	    =   np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]					)
	A_		:						Any	    =   v_tmp.reshape(v_tmp.shape[0]   ,   v_tmp.shape[1] * v_tmp.shape[2]					)
	return k, o, q, v






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=False					):
	if split_mlp_wi:
		A_		:						str	    =   params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
		A_		:						Tuple	    =   params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
		A_		:						Union[str, Any]	    =   (wi_a, wi_a)
	else:
		A_		:						Dict	    =   params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

	A_		:						Union[str, Any]	    =   params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
	return wi, wo






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]






def 			__UpperCamelCase	(		snake_case__   ,   *, snake_case__   ,   snake_case__   ,   snake_case__ = False					):
	A_		:						Any	    =   traverse_util.flatten_dict(variables["""target"""]					)
	A_		:						Optional[int]	    =   {"""/""".join(snake_case__					): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	A_		:						str	    =   """encoder/encoder/mlp/wi_0/kernel""" in old
	print("""Split MLP:"""   ,   snake_case__					)

	A_		:						int	    =   collections.OrderedDict()

	# Shared embeddings.
	A_		:						Optional[Any]	    =   old["""token_embedder/embedding"""]

	# Encoder.
	for i in range(snake_case__					):
		# Block i, layer 0 (Self Attention).
		A_		:						Optional[Any]	    =   tax_layer_norm_lookup(snake_case__   ,   snake_case__   ,   """encoder"""   ,   """pre_attention_layer_norm"""					)
		A_    ,     A_    ,     A_    ,     A_		:						Optional[int]	    =   tax_attention_lookup(snake_case__   ,   snake_case__   ,   """encoder"""   ,   """attention"""					)
		A_		:						Any	    =   layer_norm
		A_		:						Optional[Any]	    =   k.T
		A_		:						Any	    =   o.T
		A_		:						str	    =   q.T
		A_		:						List[Any]	    =   v.T

		# Block i, layer 1 (MLP).
		A_		:						Dict	    =   tax_layer_norm_lookup(snake_case__   ,   snake_case__   ,   """encoder"""   ,   """pre_mlp_layer_norm"""					)
		A_    ,     A_		:						int	    =   tax_mlp_lookup(snake_case__   ,   snake_case__   ,   """encoder"""   ,   snake_case__					)
		A_		:						Optional[Any]	    =   layer_norm
		if split_mlp_wi:
			A_		:						List[str]	    =   wi[0].T
			A_		:						str	    =   wi[1].T
		else:
			A_		:						Dict	    =   wi.T
		A_		:						Dict	    =   wo.T
		if scalable_attention:
			# convert the rel_embedding of each layer
			A_		:						Optional[int]	    =   tax_relpos_bias_lookup(
			    snake_case__   ,   snake_case__   ,   """encoder"""					).T

	A_		:						Optional[int]	    =   old["""encoder/encoder_norm/scale"""]

	if not scalable_attention:
		A_		:						List[str]	    =   tax_relpos_bias_lookup(
		    snake_case__   ,   0   ,   """encoder"""					).T
		A_		:						str	    =   tax_relpos_bias_lookup(
		    snake_case__   ,   0   ,   """decoder"""					).T

	if not is_encoder_only:
		# Decoder.
		for i in range(snake_case__					):
			# Block i, layer 0 (Self Attention).
			A_		:						List[str]	    =   tax_layer_norm_lookup(snake_case__   ,   snake_case__   ,   """decoder"""   ,   """pre_self_attention_layer_norm"""					)
			A_    ,     A_    ,     A_    ,     A_		:						Dict	    =   tax_attention_lookup(snake_case__   ,   snake_case__   ,   """decoder"""   ,   """self_attention"""					)
			A_		:						List[str]	    =   layer_norm
			A_		:						Dict	    =   k.T
			A_		:						int	    =   o.T
			A_		:						Any	    =   q.T
			A_		:						List[Any]	    =   v.T

			# Block i, layer 1 (Cross Attention).
			A_		:						List[str]	    =   tax_layer_norm_lookup(snake_case__   ,   snake_case__   ,   """decoder"""   ,   """pre_cross_attention_layer_norm"""					)
			A_    ,     A_    ,     A_    ,     A_		:						int	    =   tax_attention_lookup(snake_case__   ,   snake_case__   ,   """decoder"""   ,   """encoder_decoder_attention"""					)
			A_		:						Tuple	    =   layer_norm
			A_		:						int	    =   k.T
			A_		:						Optional[int]	    =   o.T
			A_		:						List[Any]	    =   q.T
			A_		:						str	    =   v.T

			# Block i, layer 2 (MLP).
			A_		:						List[Any]	    =   tax_layer_norm_lookup(snake_case__   ,   snake_case__   ,   """decoder"""   ,   """pre_mlp_layer_norm"""					)
			A_    ,     A_		:						Dict	    =   tax_mlp_lookup(snake_case__   ,   snake_case__   ,   """decoder"""   ,   snake_case__					)
			A_		:						int	    =   layer_norm
			if split_mlp_wi:
				A_		:						List[str]	    =   wi[0].T
				A_		:						Tuple	    =   wi[1].T
			else:
				A_		:						int	    =   wi.T
			A_		:						Optional[int]	    =   wo.T

			if scalable_attention:
				# convert the rel_embedding of each layer
				A_		:						Union[str, Any]	    =   tax_relpos_bias_lookup(snake_case__   ,   snake_case__   ,   """decoder"""					).T

		A_		:						List[Any]	    =   old["""decoder/decoder_norm/scale"""]

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			A_		:						Union[str, Any]	    =   old["""decoder/logits_dense/kernel"""].T

	return new






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						str	    =   collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		A_		:						List[str]	    =   state_dict["""shared.weight"""]

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			A_		:						Any	    =   state_dict["""shared.weight"""]

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print("""Using shared word embeddings as lm_head."""					)
			A_		:						str	    =   state_dict["""shared.weight"""]

	return state_dict






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Any	    =   checkpoints.load_tax_checkpoint(snake_case__					)
	A_		:						Union[str, Any]	    =   convert_tax_to_pytorch(
	    snake_case__   ,   num_layers=config.num_layers   ,   is_encoder_only=snake_case__   ,   scalable_attention=snake_case__					)
	A_		:						int	    =   make_state_dict(snake_case__   ,   snake_case__					)
	model.load_state_dict(snake_case__   ,   strict=snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__ = False   ,   snake_case__ = False   ,   ):
	A_		:						Any	    =   MTaConfig.from_json_file(snake_case__					)
	print(F"""Building PyTorch model from configuration: {config}"""					)
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		A_		:						Optional[Any]	    =   UMTaEncoderModel(snake_case__					)
	else:
		A_		:						Optional[Any]	    =   UMTaForConditionalGeneration(snake_case__					)

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					)

	# Save pytorch-model
	print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
	model.save_pretrained(snake_case__					)

	# Verify that we can load the checkpoint.
	model.from_pretrained(snake_case__					)
	print("""Done"""					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
							# Required parameters
							parser.add_argument(
							    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							parser.add_argument(
							    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
							)
							parser.add_argument(
							    "--scalable_attention",
							    action="store_true",
							    help="Whether the model uses scaled attention (umt5 model)",
							    default=False,
							)
							_lowerCAmelCase         =							parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)









"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase         =							logging.get_logger(__name__)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=False					):
	A_		:						Dict	    =   []

	# fmt: off
	# stem:
	rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")					)
	rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")					)

	rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")					)
	rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")					)

	# backbone
	rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")					)
	rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")					)
	rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")					)

	for stage_idx in range(len(config.backbone_config.depths					)					):
		for layer_idx in range(config.backbone_config.depths[stage_idx]					):
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")					)
			rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")					)

		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")					)
		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")					)
		rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")					)

	# transformer encoder
	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""")					)

	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_		:						Dict	    =   [(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"""),
		    ]					)
	# fmt: on

	return rename_keys






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






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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Union[str, Any]	    =   dct.pop(snake_case__					)
	A_		:						List[Any]	    =   val






def 			__UpperCamelCase	(		):
	A_		:						Any	    =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:						str	    =   Image.open(requests.get(snake_case__   ,   stream=snake_case__					).raw					)
	return im






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=False					):
	A_		:						str	    =   BitConfig(
	    global_padding="""same"""   ,   layer_type="""bottleneck"""   ,   depths=(3, 4, 9)   ,   out_features=["""stage3"""]   ,   embedding_dynamic_padding=snake_case__   ,   )
	A_		:						int	    =   ViTHybridConfig(backbone_config=snake_case__   ,   image_size=384   ,   num_labels=1_000					)
	A_		:						Any	    =   False

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

	# load state_dict of original model, remove and rename some keys
	A_		:						Optional[Any]	    =   timm_model.state_dict()
	if base_model:
		remove_classification_head_(snake_case__					)
	A_		:						Dict	    =   create_rename_keys(snake_case__   ,   snake_case__					)
	for src, dest in rename_keys:
		rename_key(snake_case__   ,   snake_case__   ,   snake_case__					)
	read_in_q_k_v(snake_case__   ,   snake_case__   ,   snake_case__					)

	A_		:						Any	    =   """huggingface/label-files"""
	A_		:						Any	    =   """imagenet-1k-id2label.json"""
	A_		:						Any	    =   json.load(open(hf_hub_download(snake_case__   ,   snake_case__   ,   repo_type="""dataset"""					)   ,   """r"""					)					)
	A_		:						Optional[int]	    =   {int(snake_case__					): v for k, v in idalabel.items()}
	A_		:						Optional[int]	    =   idalabel
	A_		:						Dict	    =   {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		A_		:						int	    =   ViTHybridModel(snake_case__					).eval()
	else:
		A_		:						Union[str, Any]	    =   ViTHybridForImageClassification(snake_case__					).eval()
	model.load_state_dict(snake_case__					)

	# create image processor
	A_		:						Any	    =   create_transform(**resolve_data_config({}   ,   model=snake_case__					)					)
	A_		:						List[Any]	    =   transform.transforms

	A_		:						int	    =   {
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

	A_		:						Optional[int]	    =   ViTHybridImageProcessor(
	    do_resize=snake_case__   ,   size={"""shortest_edge""": timm_transforms[0].size}   ,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]   ,   do_center_crop=snake_case__   ,   crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}   ,   do_normalize=snake_case__   ,   image_mean=timm_transforms[-1].mean.tolist()   ,   image_std=timm_transforms[-1].std.tolist()   ,   )

	A_		:						Tuple	    =   prepare_img()
	A_		:						List[str]	    =   transform(snake_case__					).unsqueeze(0					)
	A_		:						int	    =   processor(snake_case__   ,   return_tensors="""pt"""					).pixel_values

	# verify pixel values
	assert torch.allclose(snake_case__   ,   snake_case__					)

	# verify logits
	with torch.no_grad():
		A_		:						List[str]	    =   model(snake_case__					)
		A_		:						Any	    =   outputs.logits

	print("""Predicted class:"""   ,   logits.argmax(-1					).item()					)
	if base_model:
		A_		:						int	    =   timm_model.forward_features(snake_case__					)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(snake_case__   ,   outputs.pooler_output   ,   atol=1E-3					)
	else:
		A_		:						Optional[int]	    =   timm_model(snake_case__					)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(snake_case__   ,   outputs.logits   ,   atol=1E-3					)
	print("""Looks ok!"""					)

	if pytorch_dump_folder_path is not None:
		Path(snake_case__					).mkdir(exist_ok=snake_case__					)
		print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""					)
		model.save_pretrained(snake_case__					)
		print(F"""Saving processor to {pytorch_dump_folder_path}"""					)
		processor.save_pretrained(snake_case__					)

	if push_to_hub:
		print(F"""Pushing model and processor to the hub {vit_name}"""					)
		model.push_to_hub(F"""ybelkada/{vit_name}"""					)
		processor.push_to_hub(F"""ybelkada/{vit_name}"""					)


if __name__ == "__main__":
							_lowerCAmelCase         =							argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--vit_name",
							    default="vit_base_r50_s16_384",
							    type=str,
							    help="Name of the hybrid 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."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
							)

							_lowerCAmelCase         =							parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)

