
import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class       _snake_case					(     snake_case				,						snake_case				):







			@register_to_config
			def __init__(				self						,   _a = 128						,   _a = 256						,   _a = 20_00.0						,   _a = 768						,   _a = 12						,   _a = 12						,   _a = 64						,   _a = 2_048						,   _a = 0.1						,   ):
				super().__init__()

				__magic_name__		:		Any	   =     nn.Sequential(
				    nn.Linear(_a						,   d_model * 4						,   bias=_a		)						,   nn.SiLU()						,   nn.Linear(d_model * 4						,   d_model * 4						,   bias=_a		)						,   nn.SiLU()						,   )

				__magic_name__		:		Optional[int]	   =     nn.Embedding(_a						,   _a		)
				__magic_name__		:		Optional[Any]	   =     False

				__magic_name__		:		Any	   =     nn.Linear(_a						,   _a						,   bias=_a		)

				__magic_name__		:		Union[str, Any]	   =     nn.Dropout(p=_a		)

				__magic_name__		:		Dict	   =     nn.ModuleList()
				for lyr_num in range(_a		):
					# FiLM conditional T5 decoder
					__magic_name__		:		Union[str, Any]	   =     DecoderLayer(d_model=_a						,   d_kv=_a						,   num_heads=_a						,   d_ff=_a						,   dropout_rate=_a		)
					self.decoders.append(_a		)

				__magic_name__		:		Tuple	   =     TaLayerNorm(_a		)

				__magic_name__		:		List[str]	   =     nn.Dropout(p=_a		)
				__magic_name__		:		str	   =     nn.Linear(_a						,   _a						,   bias=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				__magic_name__		:		Union[str, Any]	   =     torch.mul(query_input.unsqueeze(-1		)						,   key_input.unsqueeze(-2		)		)
				return mask.unsqueeze(-3		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     decoder_input_tokens.shape
				assert decoder_noise_time.shape == (batch,)

				# decoder_noise_time is in [0, 1), so rescale to expected timing range.
				__magic_name__		:		Any	   =     get_timestep_embedding(
				    decoder_noise_time * self.config.max_decoder_noise_time						,   embedding_dim=self.config.d_model						,   max_period=self.config.max_decoder_noise_time						,   ).to(dtype=self.dtype		)

				__magic_name__		:		Any	   =     self.conditioning_emb(_a		).unsqueeze(1		)

				assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

				__magic_name__		:		List[Any]	   =     decoder_input_tokens.shape[1]

				# If we want to use relative positions for audio context, we can just offset
				# this sequence by the length of encodings_and_masks.
				__magic_name__		:		str	   =     torch.broadcast_to(
				    torch.arange(_a						,   device=decoder_input_tokens.device		)						,   (batch, seq_length)						,   )

				__magic_name__		:		Optional[Any]	   =     self.position_encoding(_a		)

				__magic_name__		:		Dict	   =     self.continuous_inputs_projection(_a		)
				inputs += position_encodings
				__magic_name__		:		Optional[int]	   =     self.dropout(_a		)

				# decoder: No padding present.
				__magic_name__		:		Tuple	   =     torch.ones(
				    decoder_input_tokens.shape[:2]						,   device=decoder_input_tokens.device						,   dtype=inputs.dtype		)

				# Translate encoding masks to encoder-decoder masks.
				__magic_name__		:		int	   =     [(x, self.encoder_decoder_mask(_a						,   _a		)) for x, y in encodings_and_masks]

				# cross attend style: concat encodings
				__magic_name__		:		str	   =     torch.cat([x[0] for x in encodings_and_encdec_masks]						,   dim=1		)
				__magic_name__		:		Dict	   =     torch.cat([x[1] for x in encodings_and_encdec_masks]						,   dim=-1		)

				for lyr in self.decoders:
					__magic_name__		:		Dict	   =     lyr(
					    _a						,   conditioning_emb=_a						,   encoder_hidden_states=_a						,   encoder_attention_mask=_a						,   )[0]

				__magic_name__		:		int	   =     self.decoder_norm(_a		)
				__magic_name__		:		Union[str, Any]	   =     self.post_dropout(_a		)

				__magic_name__		:		Tuple	   =     self.spec_out(_a		)
				return spec_out







class       _snake_case					(     nn.Module				):







			def __init__(				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a=1e-6		):
				super().__init__()
				__magic_name__		:		int	   =     nn.ModuleList()

				# cond self attention: layer 0
				self.layer.append(
				    TaLayerSelfAttentionCond(d_model=_a						,   d_kv=_a						,   num_heads=_a						,   dropout_rate=_a		)		)

				# cross attention: layer 1
				self.layer.append(
				    TaLayerCrossAttention(
				        d_model=_a						,   d_kv=_a						,   num_heads=_a						,   dropout_rate=_a						,   layer_norm_epsilon=_a						,   )		)

				# Film Cond MLP + dropout: last layer
				self.layer.append(
				    TaLayerFFCond(d_model=_a						,   d_ff=_a						,   dropout_rate=_a						,   layer_norm_epsilon=_a		)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   ):
				__magic_name__		:		Optional[Any]	   =     self.layer[0](
				    _a						,   conditioning_emb=_a						,   attention_mask=_a						,   )

				if encoder_hidden_states is not None:
					__magic_name__		:		Optional[Any]	   =     torch.where(encoder_attention_mask > 0						,   0						,   -1e10		).to(
					    encoder_hidden_states.dtype		)

					__magic_name__		:		List[Any]	   =     self.layer[1](
					    _a						,   key_value_states=_a						,   attention_mask=_a						,   )

				# Apply Film Conditional Feed Forward layer
				__magic_name__		:		Dict	   =     self.layer[-1](_a						,   _a		)

				return (hidden_states,)







class       _snake_case					(     nn.Module				):







			def __init__(				self						,   _a						,   _a						,   _a						,   _a		):
				super().__init__()
				__magic_name__		:		Optional[int]	   =     TaLayerNorm(_a		)
				__magic_name__		:		Optional[int]	   =     TaFiLMLayer(in_features=d_model * 4						,   out_features=_a		)
				__magic_name__		:		List[str]	   =     Attention(query_dim=_a						,   heads=_a						,   dim_head=_a						,   out_bias=_a						,   scale_qk=_a		)
				__magic_name__		:		Tuple	   =     nn.Dropout(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None						,   _a=None						,   ):
				# pre_self_attention_layer_norm
				__magic_name__		:		int	   =     self.layer_norm(_a		)

				if conditioning_emb is not None:
					__magic_name__		:		Optional[int]	   =     self.FiLMLayer(_a						,   _a		)

				# Self-attention block
				__magic_name__		:		int	   =     self.attention(_a		)

				__magic_name__		:		str	   =     hidden_states + self.dropout(_a		)

				return hidden_states







class       _snake_case					(     nn.Module				):







			def __init__(				self						,   _a						,   _a						,   _a						,   _a						,   _a		):
				super().__init__()
				__magic_name__		:		List[Any]	   =     Attention(query_dim=_a						,   heads=_a						,   dim_head=_a						,   out_bias=_a						,   scale_qk=_a		)
				__magic_name__		:		Union[str, Any]	   =     TaLayerNorm(_a						,   eps=_a		)
				__magic_name__		:		Optional[Any]	   =     nn.Dropout(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None						,   _a=None						,   ):
				__magic_name__		:		Any	   =     self.layer_norm(_a		)
				__magic_name__		:		Dict	   =     self.attention(
				    _a						,   encoder_hidden_states=_a						,   attention_mask=attention_mask.squeeze(1		)						,   )
				__magic_name__		:		Optional[Any]	   =     hidden_states + self.dropout(_a		)
				return layer_output







class       _snake_case					(     nn.Module				):







			def __init__(				self						,   _a						,   _a						,   _a						,   _a		):
				super().__init__()
				__magic_name__		:		Any	   =     TaDenseGatedActDense(d_model=_a						,   d_ff=_a						,   dropout_rate=_a		)
				__magic_name__		:		Union[str, Any]	   =     TaFiLMLayer(in_features=d_model * 4						,   out_features=_a		)
				__magic_name__		:		int	   =     TaLayerNorm(_a						,   eps=_a		)
				__magic_name__		:		List[Any]	   =     nn.Dropout(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=None		):
				__magic_name__		:		List[Any]	   =     self.layer_norm(_a		)
				if conditioning_emb is not None:
					__magic_name__		:		Optional[Any]	   =     self.film(_a						,   _a		)

				__magic_name__		:		Optional[Any]	   =     self.DenseReluDense(_a		)
				__magic_name__		:		Union[str, Any]	   =     hidden_states + self.dropout(_a		)
				return hidden_states







class       _snake_case					(     nn.Module				):







			def __init__(				self						,   _a						,   _a						,   _a		):
				super().__init__()
				__magic_name__		:		int	   =     nn.Linear(_a						,   _a						,   bias=_a		)
				__magic_name__		:		int	   =     nn.Linear(_a						,   _a						,   bias=_a		)
				__magic_name__		:		Dict	   =     nn.Linear(_a						,   _a						,   bias=_a		)
				__magic_name__		:		int	   =     nn.Dropout(_a		)
				__magic_name__		:		Any	   =     NewGELUActivation()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Any	   =     self.act(self.wi_a(_a		)		)
				__magic_name__		:		List[str]	   =     self.wi_a(_a		)
				__magic_name__		:		Tuple	   =     hidden_gelu * hidden_linear
				__magic_name__		:		Tuple	   =     self.dropout(_a		)

				__magic_name__		:		Optional[Any]	   =     self.wo(_a		)
				return hidden_states







class       _snake_case					(     nn.Module				):







			def __init__(				self						,   _a						,   _a=1e-6		):
				super().__init__()
				__magic_name__		:		Tuple	   =     nn.Parameter(torch.ones(_a		)		)
				__magic_name__		:		Union[str, Any]	   =     eps







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
				# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
				# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
				# half-precision inputs is done in fp32

				__magic_name__		:		str	   =     hidden_states.to(torch.floataa		).pow(2		).mean(-1						,   keepdim=_a		)
				__magic_name__		:		List[str]	   =     hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

				# convert into half-precision if necessary
				if self.weight.dtype in [torch.floataa, torch.bfloataa]:
					__magic_name__		:		Optional[int]	   =     hidden_states.to(self.weight.dtype		)

				return self.weight * hidden_states







class       _snake_case					(     nn.Module				):







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(_a						,   3.0		))		))







class       _snake_case					(     nn.Module				):







			def __init__(				self						,   _a						,   _a		):
				super().__init__()
				__magic_name__		:		List[Any]	   =     nn.Linear(_a						,   out_features * 2						,   bias=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				__magic_name__		:		Union[str, Any]	   =     self.scale_bias(_a		)
				__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     torch.chunk(_a						,   2						,   -1		)
				__magic_name__		:		Any	   =     x * (1 + scale) + shift
				return x


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()
snake_case :		Optional[Any]							      =  logging.get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]=False   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     []

	# 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"
		__magic_name__		:		int	   =     [(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   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict=False   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		int	   =     dct.pop(_snake_case   )
	__magic_name__		:		List[Any]	   =     val






def   lowerCAmelCase_						(					)				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		List[str]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  int=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     BitConfig(
	    global_padding="same"      ,   layer_type="bottleneck"      ,   depths=(3, 4, 9)      ,   out_features=["stage3"]      ,   embedding_dynamic_padding=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     ViTHybridConfig(backbone_config=_snake_case      ,   image_size=384      ,   num_labels=1000   )
	__magic_name__		:		str	   =     False

	# load original model from timm
	__magic_name__		:		Union[str, Any]	   =     timm.create_model(_snake_case      ,   pretrained=_snake_case   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__magic_name__		:		List[Any]	   =     timm_model.state_dict()
	if base_model:
		remove_classification_head_(_snake_case   )
	__magic_name__		:		Tuple	   =     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   )

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__magic_name__		:		List[str]	   =     ViTHybridModel(_snake_case   ).eval()
	else:
		__magic_name__		:		str	   =     ViTHybridForImageClassification(_snake_case   ).eval()
	model.load_state_dict(_snake_case   )

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

	__magic_name__		:		List[str]	   =     {
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	__magic_name__		:		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()      ,   )

	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     transform(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		Tuple	   =     processor(_snake_case      ,   return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(_snake_case   )
		__magic_name__		:		List[str]	   =     outputs.logits

	print("Predicted class:"      ,   logits.argmax(-1   ).item()   )
	if base_model:
		__magic_name__		:		List[str]	   =     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:
		__magic_name__		:		Any	   =     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__":
				snake_case :		Any							      =  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."
				)

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