
import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp






def 						lowerCamelCase__							(      a	,	a	,	a	,	a	,	a = 40_96   )   ->     Union[str, Any]:
   _A    ,						_A    ,						_A:						Tuple	    = key.shape[-3:]
   _A:						Tuple	    = value.shape[-1]
   _A:						Dict	    = min(a	,	a   )
   _A:						List[str]	    = query / jnp.sqrt(a   )

   @functools.partial(jax.checkpoint	,	prevent_cse=a   )
   def summarize_chunk(a	,	a	,	a   ):
      _A:						Tuple	    = jnp.einsum('''...qhd,...khd->...qhk'''	,	a	,	a	,	precision=a   )

      _A:						str	    = jnp.max(a	,	axis=-1	,	keepdims=a   )
      _A:						List[str]	    = jax.lax.stop_gradient(a   )
      _A:						List[Any]	    = jnp.exp(attn_weights - max_score   )

      _A:						Dict	    = jnp.einsum('''...vhf,...qhv->...qhf'''	,	a	,	a	,	precision=a   )
      _A:						Dict	    = jnp.einsum('''...qhk->...qh'''	,	a   )

      return (exp_values, exp_weights.sum(axis=-1   ), max_score)

   def chunk_scanner(a   ):
      # julienne key array
      _A:						Optional[Any]	    = jax.lax.dynamic_slice(
          operand=a	,	start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]	,	slice_sizes=list(key.shape[:-3]   ) + [key_chunk_size, num_heads, k_features]	,	)

      # julienne value array
      _A:						Union[str, Any]	    = jax.lax.dynamic_slice(
          operand=a	,	start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]	,	slice_sizes=list(value.shape[:-3]   ) + [key_chunk_size, num_heads, v_features]	,	)

      return summarize_chunk(a	,	a	,	a   )

   _A    ,						_A    ,						_A:						Optional[Any]	    = jax.lax.map(f=a	,	xs=jnp.arange(0	,	a	,	a   )   )

   _A:						Tuple	    = jnp.max(a	,	axis=0	,	keepdims=a   )
   _A:						Optional[Any]	    = jnp.exp(chunk_max - global_max   )

   chunk_values *= jnp.expand_dims(a	,	axis=-1   )
   chunk_weights *= max_diffs

   _A:						int	    = chunk_values.sum(axis=0   )
   _A:						List[Any]	    = jnp.expand_dims(a	,	-1   ).sum(axis=0   )

   return all_values / all_weights






def 						lowerCamelCase__							(      a	,	a	,	a	,	a=jax.lax.Precision.HIGHEST	,	a = 10_24	,	a = 40_96   )   ->     str:
   _A    ,						_A    ,						_A:						Any	    = query.shape[-3:]

   def chunk_scanner(a	,	a   ):
      # julienne query array
      _A:						Optional[int]	    = jax.lax.dynamic_slice(
          operand=a	,	start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]	,	slice_sizes=list(query.shape[:-3]   ) + [min(a	,	a   ), num_heads, q_features]	,	)

      return (
          chunk_idx + query_chunk_size,  # unused ignore it
          _query_chunk_attention(
              query=a	,	key=a	,	value=a	,	precision=a	,	key_chunk_size=a   ),
      )

   _A    ,						_A:						Optional[Any]	    = jax.lax.scan(
       f=a	,	init=0	,	xs=a	,	length=math.ceil(num_q / query_chunk_size   )   )

   return jnp.concatenate(a	,	axis=-3   )  # fuse the chunked result back






class UpperCAmelCase     (		nn.Module     ):





   '''simple docstring'''
   __UpperCamelCase	:					int
   __UpperCamelCase	:					int				       =					8
   __UpperCamelCase	:					int				       =					64
   __UpperCamelCase	:					float				       =					0.0
   __UpperCamelCase	:					bool				       =					False
   __UpperCamelCase	:					jnp.dtype				       =					jnp.floataa


   def 				__magic_name__       (      self  :							Any  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = self.dim_head * self.heads
      _A:						Optional[int]	    = self.dim_head**-0.5

      # Weights were exported with old names {to_q, to_k, to_v, to_out}
      _A:						Optional[int]	    = nn.Dense(lowerCAmelCase_    ,    use_bias=lowerCAmelCase_    ,    dtype=self.dtype    ,    name='''to_q'''  )
      _A:						Optional[Any]	    = nn.Dense(lowerCAmelCase_    ,    use_bias=lowerCAmelCase_    ,    dtype=self.dtype    ,    name='''to_k'''  )
      _A:						Tuple	    = nn.Dense(lowerCAmelCase_    ,    use_bias=lowerCAmelCase_    ,    dtype=self.dtype    ,    name='''to_v'''  )

      _A:						str	    = nn.Dense(self.query_dim    ,    dtype=self.dtype    ,    name='''to_out_0'''  )
      _A:						List[str]	    = nn.Dropout(rate=self.dropout  )


   def 				__magic_name__       (      self  :							Optional[Any]    ,    lowerCAmelCase_  :							str  ):

      """simple docstring"""
      _A    ,						_A    ,						_A:						str	    = tensor.shape
      _A:						List[str]	    = self.heads
      _A:						str	    = tensor.reshape(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    dim // head_size  )
      _A:						Any	    = jnp.transpose(lowerCAmelCase_    ,    (0, 2, 1, 3)  )
      _A:						List[str]	    = tensor.reshape(batch_size * head_size    ,    lowerCAmelCase_    ,    dim // head_size  )
      return tensor


   def 				__magic_name__       (      self  :							List[str]    ,    lowerCAmelCase_  :							List[Any]  ):

      """simple docstring"""
      _A    ,						_A    ,						_A:						Tuple	    = tensor.shape
      _A:						List[str]	    = self.heads
      _A:						List[Any]	    = tensor.reshape(batch_size // head_size    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )
      _A:						Optional[int]	    = jnp.transpose(lowerCAmelCase_    ,    (0, 2, 1, 3)  )
      _A:						int	    = tensor.reshape(batch_size // head_size    ,    lowerCAmelCase_    ,    dim * head_size  )
      return tensor


   def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							List[Any]=None    ,    lowerCAmelCase_  :							Union[str, Any]=True  ):

      """simple docstring"""
      _A:						Optional[Any]	    = hidden_states if context is None else context

      _A:						str	    = self.query(lowerCAmelCase_  )
      _A:						List[Any]	    = self.key(lowerCAmelCase_  )
      _A:						Dict	    = self.value(lowerCAmelCase_  )

      _A:						Dict	    = self.reshape_heads_to_batch_dim(lowerCAmelCase_  )
      _A:						Tuple	    = self.reshape_heads_to_batch_dim(lowerCAmelCase_  )
      _A:						List[Any]	    = self.reshape_heads_to_batch_dim(lowerCAmelCase_  )

      if self.use_memory_efficient_attention:
         _A:						Union[str, Any]	    = query_states.transpose(1    ,    0    ,    2  )
         _A:						List[Any]	    = key_states.transpose(1    ,    0    ,    2  )
         _A:						Tuple	    = value_states.transpose(1    ,    0    ,    2  )

         # this if statement create a chunk size for each layer of the unet
         # the chunk size is equal to the query_length dimension of the deepest layer of the unet

         _A:						Tuple	    = query_states.shape[-3]
         if flatten_latent_dim % 6_4 == 0:
            _A:						Union[str, Any]	    = int(flatten_latent_dim / 6_4  )
         elif flatten_latent_dim % 1_6 == 0:
            _A:						Union[str, Any]	    = int(flatten_latent_dim / 1_6  )
         elif flatten_latent_dim % 4 == 0:
            _A:						List[str]	    = int(flatten_latent_dim / 4  )
         else:
            _A:						Optional[Any]	    = int(lowerCAmelCase_  )

         _A:						List[str]	    = jax_memory_efficient_attention(
             lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    query_chunk_size=lowerCAmelCase_    ,    key_chunk_size=4_0_9_6 * 4  )

         _A:						List[str]	    = hidden_states.transpose(1    ,    0    ,    2  )
      else:
         # compute attentions
         _A:						Any	    = jnp.einsum('''b i d, b j d->b i j'''    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )
         _A:						Tuple	    = attention_scores * self.scale
         _A:						str	    = nn.softmax(lowerCAmelCase_    ,    axis=2  )

         # attend to values
         _A:						List[Any]	    = jnp.einsum('''b i j, b j d -> b i d'''    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )

      _A:						List[Any]	    = self.reshape_batch_dim_to_heads(lowerCAmelCase_  )
      _A:						List[Any]	    = self.proj_attn(lowerCAmelCase_  )
      return self.dropout_layer(lowerCAmelCase_    ,    deterministic=lowerCAmelCase_  )






class UpperCAmelCase     (		nn.Module     ):





   '''simple docstring'''
   __UpperCamelCase	:					int
   __UpperCamelCase	:					int
   __UpperCamelCase	:					int
   __UpperCamelCase	:					float				       =					0.0
   __UpperCamelCase	:					bool				       =					False
   __UpperCamelCase	:					jnp.dtype				       =					jnp.floataa
   __UpperCamelCase	:					bool				       =					False


   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      # self attention (or cross_attention if only_cross_attention is True)
      _A:						Dict	    = FlaxAttention(
          self.dim    ,    self.n_heads    ,    self.d_head    ,    self.dropout    ,    self.use_memory_efficient_attention    ,    dtype=self.dtype  )
      # cross attention
      _A:						Any	    = FlaxAttention(
          self.dim    ,    self.n_heads    ,    self.d_head    ,    self.dropout    ,    self.use_memory_efficient_attention    ,    dtype=self.dtype  )
      _A:						Optional[Any]	    = FlaxFeedForward(dim=self.dim    ,    dropout=self.dropout    ,    dtype=self.dtype  )
      _A:						Optional[Any]	    = nn.LayerNorm(epsilon=1e-5    ,    dtype=self.dtype  )
      _A:						int	    = nn.LayerNorm(epsilon=1e-5    ,    dtype=self.dtype  )
      _A:						Tuple	    = nn.LayerNorm(epsilon=1e-5    ,    dtype=self.dtype  )
      _A:						Optional[Any]	    = nn.Dropout(rate=self.dropout  )


   def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Any    ,    lowerCAmelCase_  :							List[str]    ,    lowerCAmelCase_  :							Tuple=True  ):

      """simple docstring"""
      # self attention
      _A:						Any	    = hidden_states
      if self.only_cross_attention:
         _A:						Tuple	    = self.attna(self.norma(lowerCAmelCase_  )    ,    lowerCAmelCase_    ,    deterministic=lowerCAmelCase_  )
      else:
         _A:						Any	    = self.attna(self.norma(lowerCAmelCase_  )    ,    deterministic=lowerCAmelCase_  )
      _A:						List[Any]	    = hidden_states + residual

      # cross attention
      _A:						Any	    = hidden_states
      _A:						Any	    = self.attna(self.norma(lowerCAmelCase_  )    ,    lowerCAmelCase_    ,    deterministic=lowerCAmelCase_  )
      _A:						List[Any]	    = hidden_states + residual

      # feed forward
      _A:						List[Any]	    = hidden_states
      _A:						List[Any]	    = self.ff(self.norma(lowerCAmelCase_  )    ,    deterministic=lowerCAmelCase_  )
      _A:						Tuple	    = hidden_states + residual

      return self.dropout_layer(lowerCAmelCase_    ,    deterministic=lowerCAmelCase_  )






class UpperCAmelCase     (		nn.Module     ):





   '''simple docstring'''
   __UpperCamelCase	:					int
   __UpperCamelCase	:					int
   __UpperCamelCase	:					int
   __UpperCamelCase	:					int				       =					1
   __UpperCamelCase	:					float				       =					0.0
   __UpperCamelCase	:					bool				       =					False
   __UpperCamelCase	:					bool				       =					False
   __UpperCamelCase	:					jnp.dtype				       =					jnp.floataa
   __UpperCamelCase	:					bool				       =					False


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      _A:						Any	    = nn.GroupNorm(num_groups=3_2    ,    epsilon=1e-5  )

      _A:						int	    = self.n_heads * self.d_head
      if self.use_linear_projection:
         _A:						Optional[int]	    = nn.Dense(lowerCAmelCase_    ,    dtype=self.dtype  )
      else:
         _A:						List[Any]	    = nn.Conv(
             lowerCAmelCase_    ,    kernel_size=(1, 1)    ,    strides=(1, 1)    ,    padding='''VALID'''    ,    dtype=self.dtype    ,    )

      _A:						int	    = [
          FlaxBasicTransformerBlock(
              lowerCAmelCase_    ,    self.n_heads    ,    self.d_head    ,    dropout=self.dropout    ,    only_cross_attention=self.only_cross_attention    ,    dtype=self.dtype    ,    use_memory_efficient_attention=self.use_memory_efficient_attention    ,    )
          for _ in range(self.depth  )
      ]

      if self.use_linear_projection:
         _A:						List[str]	    = nn.Dense(lowerCAmelCase_    ,    dtype=self.dtype  )
      else:
         _A:						str	    = nn.Conv(
             lowerCAmelCase_    ,    kernel_size=(1, 1)    ,    strides=(1, 1)    ,    padding='''VALID'''    ,    dtype=self.dtype    ,    )

      _A:						List[str]	    = nn.Dropout(rate=self.dropout  )


   def __call__(      self  :							Optional[Any]    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							Any    ,    lowerCAmelCase_  :							Optional[Any]=True  ):

      """simple docstring"""
      _A    ,						_A    ,						_A    ,						_A:						List[Any]	    = hidden_states.shape
      _A:						List[str]	    = hidden_states
      _A:						List[str]	    = self.norm(lowerCAmelCase_  )
      if self.use_linear_projection:
         _A:						Tuple	    = hidden_states.reshape(lowerCAmelCase_    ,    height * width    ,    lowerCAmelCase_  )
         _A:						Union[str, Any]	    = self.proj_in(lowerCAmelCase_  )
      else:
         _A:						int	    = self.proj_in(lowerCAmelCase_  )
         _A:						Dict	    = hidden_states.reshape(lowerCAmelCase_    ,    height * width    ,    lowerCAmelCase_  )

      for transformer_block in self.transformer_blocks:
         _A:						str	    = transformer_block(lowerCAmelCase_    ,    lowerCAmelCase_    ,    deterministic=lowerCAmelCase_  )

      if self.use_linear_projection:
         _A:						Dict	    = self.proj_out(lowerCAmelCase_  )
         _A:						List[str]	    = hidden_states.reshape(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )
      else:
         _A:						Optional[Any]	    = hidden_states.reshape(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )
         _A:						Optional[int]	    = self.proj_out(lowerCAmelCase_  )

      _A:						Tuple	    = hidden_states + residual
      return self.dropout_layer(lowerCAmelCase_    ,    deterministic=lowerCAmelCase_  )






class UpperCAmelCase     (		nn.Module     ):





   '''simple docstring'''
   __UpperCamelCase	:					int
   __UpperCamelCase	:					float				       =					0.0
   __UpperCamelCase	:					jnp.dtype				       =					jnp.floataa


   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      # The second linear layer needs to be called
      # net_2 for now to match the index of the Sequential layer
      _A:						Any	    = FlaxGEGLU(self.dim    ,    self.dropout    ,    self.dtype  )
      _A:						Optional[int]	    = nn.Dense(self.dim    ,    dtype=self.dtype  )


   def __call__(      self  :							Optional[int]    ,    lowerCAmelCase_  :							int    ,    lowerCAmelCase_  :							List[str]=True  ):

      """simple docstring"""
      _A:						Optional[int]	    = self.net_a(lowerCAmelCase_    ,    deterministic=lowerCAmelCase_  )
      _A:						Any	    = self.net_a(lowerCAmelCase_  )
      return hidden_states






class UpperCAmelCase     (		nn.Module     ):





   '''simple docstring'''
   __UpperCamelCase	:					int
   __UpperCamelCase	:					float				       =					0.0
   __UpperCamelCase	:					jnp.dtype				       =					jnp.floataa


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      _A:						str	    = self.dim * 4
      _A:						Dict	    = nn.Dense(inner_dim * 2    ,    dtype=self.dtype  )
      _A:						List[str]	    = nn.Dropout(rate=self.dropout  )


   def __call__(      self  :							Tuple    ,    lowerCAmelCase_  :							List[Any]    ,    lowerCAmelCase_  :							Union[str, Any]=True  ):

      """simple docstring"""
      _A:						Optional[int]	    = self.proj(lowerCAmelCase_  )
      _A    ,						_A:						Optional[Any]	    = jnp.split(lowerCAmelCase_    ,    2    ,    axis=2  )
      return self.dropout_layer(hidden_linear * nn.gelu(lowerCAmelCase_  )    ,    deterministic=lowerCAmelCase_  )





import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


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






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					List[nn.Module]				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					list				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							Any    ,    lowerCAmelCase_  :							Tensor    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						Optional[Any]	    = len(list(m.modules()  )  ) == 1 or isinstance(lowerCAmelCase_    ,    nn.Convad  ) or isinstance(lowerCAmelCase_    ,    nn.BatchNormad  )
      if has_not_submodules:
         if not isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ):
            self.traced.append(lowerCAmelCase_  )


   def __call__(      self  :							str    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook  )  )
      self.module(lowerCAmelCase_  )
      [x.remove() for x in self.handles]
      return self


   @property
   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda lowerCAmelCase_  : len(list(x.state_dict().keys()  )  ) > 0    ,    self.traced  )  )






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					int				       =					0
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						int	    = Tracker(self.dest  )(lowerCAmelCase_  ).parametrized
      _A:						Union[str, Any]	    = Tracker(self.src  )(lowerCAmelCase_  ).parametrized

      _A:						Any	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.src_skip    ,    lowerCAmelCase_  )  )
      _A:						int	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.dest_skip    ,    lowerCAmelCase_  )  )

      if len(lowerCAmelCase_  ) != len(lowerCAmelCase_  ):
         raise Exception(
             F"""Numbers of operations are different. Source module has {len(lowerCAmelCase_  )} operations while"""
             F""" destination module has {len(lowerCAmelCase_  )}."""  )

      for dest_m, src_m in zip(lowerCAmelCase_    ,    lowerCAmelCase_  ):
         dest_m.load_state_dict(src_m.state_dict()  )
         if self.verbose == 1:
            print(F"""Transfered from={src_m} to={dest_m}"""  )






def 						lowerCamelCase__							(      a	,	a   )   ->     nn.Module:
   # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
   _A:						Tuple	    = from_model.state_dict()
   _A:						Dict	    = our_model.state_dict()
   _A:						str	    = our_model.config
   _A:						List[str]	    = []
   for stage_idx in range(len(config.hidden_sizes   )   ):
      for block_id in range(config.depths[stage_idx]   ):
         _A:						Tuple	    = f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
         _A:						List[str]	    = f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

         all_keys.append((from_key, to_key)   )
         _A:						str	    = f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
         _A:						Union[str, Any]	    = f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

         all_keys.append((from_key, to_key)   )

   for from_key, to_key in all_keys:
      _A:						Union[str, Any]	    = from_state_dict.pop(a   )

   our_model.load_state_dict(a   )
   return our_model






def 						lowerCamelCase__							(      a	,	a	,	a	,	a	,	a	,	a = True	,	)   ->     Dict:
   print(f"""Downloading weights for {name}..."""   )
   _A:						Dict	    = cached_download(a   )
   print(f"""Converting {name}..."""   )
   _A:						int	    = torch.load(a   )['''state_dict''']
   from_model.load_state_dict(a   )
   from_model.eval()
   with torch.no_grad():
      _A:						Any	    = VanForImageClassification(a   ).eval()
      _A:						Tuple	    = ModuleTransfer(src=a	,	dest=a   )
      _A:						List[str]	    = torch.randn((1, 3, 2_24, 2_24)   )
      module_transfer(a   )
      _A:						Tuple	    = copy_parameters(a	,	a   )

   if not torch.allclose(from_model(a   )	,	our_model(a   ).logits   ):
      raise ValueError('''The model logits don\'t match the original one.'''   )

   _A:						str	    = name
   print(a   )

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add model'''	,	use_temp_dir=a	,	)

      # we can use the convnext one
      _A:						Tuple	    = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''   )
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add image processor'''	,	use_temp_dir=a	,	)

      print(f"""Pushed {checkpoint_name}"""   )






def 						lowerCamelCase__							(      a	,	a = None	,	a = True   )   ->     int:
   _A:						str	    = '''imagenet-1k-id2label.json'''
   _A:						Optional[int]	    = 10_00

   _A:						str	    = '''huggingface/label-files'''
   _A:						List[str]	    = num_labels
   _A:						Union[str, Any]	    = json.load(open(hf_hub_download(a	,	a	,	repo_type='''dataset'''   )	,	'''r'''   )   )
   _A:						List[str]	    = {int(a   ): v for k, v in idalabel.items()}

   _A:						Any	    = idalabel
   _A:						Union[str, Any]	    = {v: k for k, v in idalabel.items()}

   _A:						Optional[int]	    = partial(a	,	num_labels=a	,	idalabel=a	,	labelaid=a   )

   _A:						Optional[int]	    = {
       '''van-tiny''': ImageNetPreTrainedConfig(
           hidden_sizes=[32, 64, 1_60, 2_56]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-small''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-base''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-large''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
   }

   _A:						str	    = {
       '''van-tiny''': van_tiny,
       '''van-small''': van_small,
       '''van-base''': van_base,
       '''van-large''': van_large,
   }

   _A:						Union[str, Any]	    = {
       '''van-tiny''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
       ),
       '''van-small''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
       ),
       '''van-base''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
       ),
       '''van-large''': (
           '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
       ),
   }

   if model_name:
      convert_weight_and_push(
          a	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=a	,	push_to_hub=a	,	)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(
             a	,	a	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=a	,	push_to_hub=a	,	)


if __name__ == "__main__":
   UpperCAmelCase__	:  Tuple      =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model-name',
       default=None,
       type=str,
       help=(
           'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
           ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=Path,
       required=True,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument(
       '--van_dir',
       required=True,
       type=Path,
       help=(
           'A path to VAN\'s original implementation directory. You can download from here:'
           ' https://github.com/Visual-Attention-Network/VAN-Classification'
       ),
   )
   parser.add_argument(
       '--push_to_hub',
       default=True,
       type=bool,
       required=False,
       help='If True, push model and image processor to the hub.',
   )

   UpperCAmelCase__	:  Dict      =   parser.parse_args()
   UpperCAmelCase__	:  Path      =   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   UpperCAmelCase__	:  Optional[int]      =   args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



