






"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional



class      SCREAMING_SNAKE_CASE  (				snake_case__						):





	"""simple docstring"""




	def __init__(self							):
		# test for the above condition
		self.test()





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   0
		A_		:						List[str]	    =   False
		while not completed:
			if counter == 1:
				self.reset()
			A_		:						Optional[Any]	    =   self.advance()
			if not self.does_advance(lowercase_							):
				raise Exception(
				    """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true."""							)

			A_		:						List[str]	    =   self.update(lowercase_							)
			counter += 1

			if counter > 10000:
				raise Exception("""update() does not fulfill the constraint."""							)

		if self.remaining() != 0:
			raise Exception("""Custom Constraint is not defined correctly."""							)





	@abstractmethod
	def    lowerCamelCase(self							):
		raise NotImplementedError(
		    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""							)





	@abstractmethod
	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		raise NotImplementedError(
		    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""							)





	@abstractmethod
	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		raise NotImplementedError(
		    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""							)





	@abstractmethod
	def    lowerCamelCase(self							):
		raise NotImplementedError(
		    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""							)





	@abstractmethod
	def    lowerCamelCase(self							):
		raise NotImplementedError(
		    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""							)





	@abstractmethod
	def    lowerCamelCase(self     ,     lowerCAmelCase_=False							):
		raise NotImplementedError(
		    f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""							)






class      SCREAMING_SNAKE_CASE  (				snake_case__						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_							):
		super(lowercase_     ,     self							).__init__()

		if not isinstance(lowercase_     ,     lowercase_							) or len(lowercase_							) == 0:
			raise ValueError(f"""`token_ids` has to be a non-empty list, but is {token_ids}."""							)
		if any((not isinstance(lowercase_     ,     lowercase_							) or token_id < 0) for token_id in token_ids							):
			raise ValueError(f"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}."""							)

		A_		:						str	    =   token_ids

		A_		:						Dict	    =   len(self.token_ids							)
		A_		:						str	    =   -1  # the index of the currently fulfilled step
		A_		:						Dict	    =   False





	def    lowerCamelCase(self							):
		if self.completed:
			return None
		return self.token_ids[self.fulfilled_idx + 1]





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if not isinstance(lowercase_     ,     lowercase_							):
			raise ValueError(f"""`token_id` has to be an `int`, but is {token_id} of type {type(lowercase_							)}"""							)

		if self.completed:
			return False

		return token_id == self.token_ids[self.fulfilled_idx + 1]





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if not isinstance(lowercase_     ,     lowercase_							):
			raise ValueError(f"""`token_id` has to be an `int`, but is {token_id} of type {type(lowercase_							)}"""							)

		A_		:						Any	    =   False
		A_		:						Union[str, Any]	    =   False
		A_		:						int	    =   False

		if self.does_advance(lowercase_							):
			self.fulfilled_idx += 1
			A_		:						List[str]	    =   True
			if self.fulfilled_idx == (self.seqlen - 1):
				A_		:						Union[str, Any]	    =   True
			A_		:						str	    =   completed
		else:
			# failed to make progress.
			A_		:						int	    =   True
			self.reset()
		return stepped, completed, reset





	def    lowerCamelCase(self							):
		A_		:						int	    =   False
		A_		:						Union[str, Any]	    =   0





	def    lowerCamelCase(self							):
		return self.seqlen - (self.fulfilled_idx + 1)





	def    lowerCamelCase(self     ,     lowerCAmelCase_=False							):
		A_		:						str	    =   PhrasalConstraint(self.token_ids							)

		if stateful:
			A_		:						int	    =   self.seqlen
			A_		:						int	    =   self.fulfilled_idx
			A_		:						List[str]	    =   self.completed

		return new_constraint






class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=True							):
		A_		:						List[str]	    =   max([len(lowercase_							) for one in nested_token_ids]							)

		A_		:						List[str]	    =   {}
		for token_ids in nested_token_ids:
			A_		:						Optional[Any]	    =   root
			for tidx, token_id in enumerate(lowercase_							):
				if token_id not in level:
					A_		:						Dict	    =   {}

				A_		:						List[Any]	    =   level[token_id]

		if no_subsets and self.has_subsets(lowercase_     ,     lowercase_							):
			raise ValueError(
			    """Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
			    f""" {nested_token_ids}."""							)

		A_		:						Optional[int]	    =   root





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   self.trie

		for current_token in current_seq:
			A_		:						Union[str, Any]	    =   start[current_token]

		A_		:						str	    =   list(start.keys()							)

		return next_tokens





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   self.next_tokens(lowercase_							)

		return len(lowercase_							) == 0





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Optional[int]	    =   list(root.values()							)
		if len(lowercase_							) == 0:
			return 1
		else:
			return sum([self.count_leaves(lowercase_							) for nn in next_nodes]							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   self.count_leaves(lowercase_							)
		return len(lowercase_							) != leaf_count






class      SCREAMING_SNAKE_CASE  (				snake_case__						):





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_							):
		super(lowercase_     ,     self							).__init__()

		if not isinstance(lowercase_     ,     lowercase_							) or len(lowercase_							) == 0:
			raise ValueError(f"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}."""							)
		if any(not isinstance(lowercase_     ,     lowercase_							) for token_ids in nested_token_ids							):
			raise ValueError(f"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}."""							)
		if any(
		    any((not isinstance(lowercase_     ,     lowercase_							) or token_id < 0) for token_id in token_ids							)
		    for token_ids in nested_token_ids							):
			raise ValueError(
			    f"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}."""							)

		A_		:						List[str]	    =   DisjunctiveTrie(lowercase_							)
		A_		:						Union[str, Any]	    =   nested_token_ids

		A_		:						Union[str, Any]	    =   self.trie.max_height
		A_		:						int	    =   []
		A_		:						int	    =   False





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.trie.next_tokens(self.current_seq							)

		if len(lowercase_							) == 0:
			return None
		else:
			return token_list





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if not isinstance(lowercase_     ,     lowercase_							):
			raise ValueError(f"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowercase_							)}"""							)

		A_		:						Optional[int]	    =   self.trie.next_tokens(self.current_seq							)

		return token_id in next_tokens





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if not isinstance(lowercase_     ,     lowercase_							):
			raise ValueError(f"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowercase_							)}"""							)

		A_		:						Union[str, Any]	    =   False
		A_		:						List[str]	    =   False
		A_		:						Optional[int]	    =   False

		if self.does_advance(lowercase_							):
			self.current_seq.append(lowercase_							)
			A_		:						List[Any]	    =   True
		else:
			A_		:						str	    =   True
			self.reset()

		A_		:						str	    =   self.trie.reached_leaf(self.current_seq							)
		A_		:						Dict	    =   completed

		return stepped, completed, reset





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   False
		A_		:						Dict	    =   []





	def    lowerCamelCase(self							):
		if self.completed:
			# since this can be completed without reaching max height
			return 0
		else:
			return self.seqlen - len(self.current_seq							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_=False							):
		A_		:						List[str]	    =   DisjunctiveConstraint(self.token_ids							)

		if stateful:
			A_		:						Optional[int]	    =   self.seqlen
			A_		:						Union[str, Any]	    =   self.current_seq
			A_		:						Tuple	    =   self.completed

		return new_constraint






class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   constraints

		# max # of steps required to fulfill a given constraint
		A_		:						int	    =   max([c.seqlen for c in constraints]							)
		A_		:						int	    =   len(lowercase_							)
		A_		:						Union[str, Any]	    =   False

		self.init_state()





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   []
		A_		:						Dict	    =   None
		A_		:						List[Any]	    =   [constraint.copy(stateful=lowercase_							) for constraint in self.constraints]





	def    lowerCamelCase(self							):
		A_		:						Any	    =   0
		if self.inprogress_constraint:
			# extra points for having a constraint mid-fulfilled
			add += self.max_seqlen - self.inprogress_constraint.remaining()

		return (len(self.complete_constraints							) * self.max_seqlen) + add





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   []
		if self.inprogress_constraint is None:
			for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
				A_		:						Any	    =   constraint.advance()
				if isinstance(lowercase_     ,     lowercase_							):
					token_list.append(lowercase_							)
				elif isinstance(lowercase_     ,     lowercase_							):
					token_list.extend(lowercase_							)
		else:
			A_		:						Tuple	    =   self.inprogress_constraint.advance()
			if isinstance(lowercase_     ,     lowercase_							):
				token_list.append(lowercase_							)
			elif isinstance(lowercase_     ,     lowercase_							):
				token_list.extend(lowercase_							)

		if len(lowercase_							) == 0:
			return None
		else:
			return token_list





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		self.init_state()

		if token_ids is not None:
			for token in token_ids:
				# completes or steps **one** constraint
				A_		:						Dict	    =   self.add(lowercase_							)

				# the entire list of constraints are fulfilled
				if self.completed:
					break





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if not isinstance(lowercase_     ,     lowercase_							):
			raise ValueError(f"""`token_id` should be an `int`, but is `{token_id}`."""							)

		A_		:						Optional[Any]	    =   False, False

		if self.completed:
			A_		:						int	    =   True
			A_		:						List[str]	    =   False
			return complete, stepped

		if self.inprogress_constraint is not None:
			# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
			# job, simply update the state

			A_		:						List[Any]	    =   self.inprogress_constraint.update(lowercase_							)
			if reset:
				# 1. If the next token breaks the progress, then we must restart.
				#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

				#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
				#     constraint, not the full list of constraints.

				self.pending_constraints.append(self.inprogress_constraint.copy(stateful=lowercase_							)							)
				A_		:						List[str]	    =   None

			if complete:
				# 2. If the next token completes the constraint, move it to completed list, set
				#     inprogress to None. If there are no pending constraints either, then this full list of constraints
				#     is complete.

				self.complete_constraints.append(self.inprogress_constraint							)
				A_		:						Tuple	    =   None

				if len(self.pending_constraints							) == 0:
					# we're done!
					A_		:						Any	    =   True

		else:
			# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
			# of constraints?

			for cidx, pending_constraint in enumerate(self.pending_constraints							):
				if pending_constraint.does_advance(lowercase_							):
					A_		:						str	    =   pending_constraint.update(lowercase_							)

					if not stepped:
						raise Exception(
						    """`constraint.update(token_id)` is not yielding incremental progress, """
						    """even though `constraint.does_advance(token_id)` is true."""							)

					if complete:
						self.complete_constraints.append(lowercase_							)
						A_		:						Dict	    =   None

					if not complete and stepped:
						A_		:						List[Any]	    =   pending_constraint

					if complete or stepped:
						# If we made any progress at all, then it's at least not a "pending constraint".

						A_		:						int	    =   (
						    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
						)

						if len(self.pending_constraints							) == 0 and self.inprogress_constraint is None:
							# If there's no longer any pending after this and no inprogress either, then we must be
							# complete.

							A_		:						Union[str, Any]	    =   True

						break  # prevent accidentally stepping through multiple constraints with just one token.

		return complete, stepped





	def    lowerCamelCase(self     ,     lowerCAmelCase_=True							):
		A_		:						Union[str, Any]	    =   ConstraintListState(self.constraints							)  # we actually never though self.constraints objects
		# throughout this process. So it's at initialization state.

		if stateful:
			A_		:						Any	    =   [
			    constraint.copy(stateful=lowercase_							) for constraint in self.complete_constraints
			]
			if self.inprogress_constraint is not None:
				A_		:						str	    =   self.inprogress_constraint.copy(stateful=lowercase_							)
			A_		:						Any	    =   [constraint.copy() for constraint in self.pending_constraints]

		return new_state









"""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)

