


import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  __A :  Any    							=				"0.12"  # assumed parallelism: 8


if is_torch_available():
  import torch







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Optional[int]							,   A__ :       List[str]=None ):
  '''simple docstring'''





  if rng is None:
    lowerCAmelCase_      :				List[str]      =       random.Random()

  lowerCAmelCase_      :				Optional[int]      =       1
  for dim in shape:
    total_dims *= dim

  lowerCAmelCase_      :				Optional[Any]      =       []
  for _ in range(A__ ):
    values.append(rng.randint(0							,   vocab_size - 1 ) )

  lowerCAmelCase_      :				Optional[Any]      =       np.array(A__							,   dtype=jnp.intaa ).reshape(A__ )

  return output







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       Union[str, Any]=None ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       ids_tensor(A__							,   vocab_size=2							,   rng=A__ )
  # make sure that at least one token is attended to for each batch
  lowerCAmelCase_      :				Union[str, Any]      =       1
  return attn_mask






@require_flax
class        __snake_case	:







    """simple docstring"""



    lowercase       			= None
    lowercase       			= ()

    def __lowercase       (       self   :  List[str]		)       ->  List[str]:
      lowerCAmelCase_      :				List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      # cut to half length & take max batch_size 3
      lowerCAmelCase_      :				int      =       2
      lowerCAmelCase_      :				Tuple      =       inputs["""input_ids"""].shape[-1] // 2
      lowerCAmelCase_      :				str      =       inputs["""input_ids"""][:max_batch_size, :sequence_length]

      lowerCAmelCase_      :				Dict      =       jnp.ones_like(lowerCamelCase		)
      lowerCAmelCase_      :				Optional[int]      =       attention_mask[:max_batch_size, :sequence_length]

      # generate max 5 tokens
      lowerCAmelCase_      :				Optional[Any]      =       input_ids.shape[-1] + 5
      if config.eos_token_id is not None and config.pad_token_id is None:
        # hack to allow generate for models such as GPT2 as is done in `generate()`
        lowerCAmelCase_      :				Optional[int]      =       config.eos_token_id
      return config, input_ids, attention_mask, max_length

    @is_pt_flax_cross_test
    def __lowercase       (       self   :  Optional[Any]		)       ->  str:
      lowerCAmelCase_      :				Optional[Any]      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				List[str]      =       False
      lowerCAmelCase_      :				Tuple      =       max_length
      lowerCAmelCase_      :				List[Any]      =       0

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Union[str, Any]      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				str      =       model_class.__name__[4:]  # Skip the "Flax" at the beginning
        lowerCAmelCase_      :				str      =       getattr(lowerCamelCase   ,						lowerCamelCase		)
        lowerCAmelCase_      :				int      =       pt_model_class(lowerCamelCase		).eval()
        lowerCAmelCase_      :				List[str]      =       load_flax_weights_in_pytorch_model(lowerCamelCase   ,						flax_model.params		)

        lowerCAmelCase_      :				List[str]      =       flax_model.generate(lowerCamelCase		).sequences
        lowerCAmelCase_      :				List[str]      =       pt_model.generate(torch.tensor(lowerCamelCase   ,						dtype=torch.long		)		)

        if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
          lowerCAmelCase_      :				Dict      =       flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

        self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,						flax_generation_outputs.tolist()		)

    def __lowercase       (       self   :  List[Any]		)       ->  Optional[int]:
      lowerCAmelCase_      :				Union[str, Any]      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				Tuple      =       False
      lowerCAmelCase_      :				Any      =       max_length

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Union[str, Any]      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				Tuple      =       model.generate(lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				str      =       jit(model.generate		)
        lowerCAmelCase_      :				int      =       jit_generate(lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)

    def __lowercase       (       self   :  List[Any]		)       ->  int:
      lowerCAmelCase_      :				Optional[int]      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				Dict      =       True
      lowerCAmelCase_      :				Dict      =       max_length

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				str      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				Optional[int]      =       model.generate(lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				Dict      =       jit(model.generate		)
        lowerCAmelCase_      :				Dict      =       jit_generate(lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)

    def __lowercase       (       self   :  Any		)       ->  Optional[int]:
      lowerCAmelCase_      :				str      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				List[Any]      =       False
      lowerCAmelCase_      :				Union[str, Any]      =       max_length
      lowerCAmelCase_      :				Any      =       2

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Tuple      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				Dict      =       model.generate(lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				List[str]      =       jit(model.generate		)
        lowerCAmelCase_      :				Any      =       jit_generate(lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Any:
      lowerCAmelCase_      :				List[Any]      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				Optional[Any]      =       False
      lowerCAmelCase_      :				str      =       max_length
      lowerCAmelCase_      :				Any      =       2
      lowerCAmelCase_      :				Optional[Any]      =       2

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				str      =       model.generate(lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[0]   ,						input_ids.shape[0] * config.num_return_sequences		)

    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Dict      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				Optional[Any]      =       True
      lowerCAmelCase_      :				List[str]      =       max_length
      lowerCAmelCase_      :				List[Any]      =       0.8
      lowerCAmelCase_      :				Optional[int]      =       10
      lowerCAmelCase_      :				List[Any]      =       0.3
      lowerCAmelCase_      :				List[Any]      =       1
      lowerCAmelCase_      :				int      =       8
      lowerCAmelCase_      :				Optional[int]      =       9

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				str      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				Any      =       model.generate(lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				str      =       jit(model.generate		)
        lowerCAmelCase_      :				Dict      =       jit_generate(lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)

    def __lowercase       (       self   :  int		)       ->  Dict:
      lowerCAmelCase_      :				Optional[int]      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				Dict      =       max_length
      lowerCAmelCase_      :				Optional[Any]      =       1
      lowerCAmelCase_      :				List[str]      =       8
      lowerCAmelCase_      :				Optional[int]      =       9

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				str      =       model.generate(lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				Tuple      =       jit(model.generate		)
        lowerCAmelCase_      :				Any      =       jit_generate(lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)

    def __lowercase       (       self   :  List[str]		)       ->  Optional[int]:
      lowerCAmelCase_      :				List[Any]      =       self._get_input_ids_and_config()
      lowerCAmelCase_      :				Dict      =       max_length
      lowerCAmelCase_      :				List[str]      =       2
      lowerCAmelCase_      :				Optional[int]      =       1
      lowerCAmelCase_      :				Union[str, Any]      =       8
      lowerCAmelCase_      :				Optional[int]      =       9

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Optional[Any]      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				Tuple      =       model.generate(lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				str      =       jit(model.generate		)
        lowerCAmelCase_      :				Optional[int]      =       jit_generate(lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)

    def __lowercase       (       self   :  int		)       ->  Dict:
      lowerCAmelCase_      :				Any      =       self._get_input_ids_and_config()

      # pad attention mask on the left
      lowerCAmelCase_      :				Tuple      =       attention_mask.at[(0, 0)].set(0		)

      lowerCAmelCase_      :				Dict      =       False
      lowerCAmelCase_      :				Any      =       max_length

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Dict      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				Dict      =       model.generate(lowerCamelCase   ,						attention_mask=lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				List[str]      =       jit(model.generate		)
        lowerCAmelCase_      :				Union[str, Any]      =       jit_generate(lowerCamelCase   ,						attention_mask=lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)

    def __lowercase       (       self   :  Any		)       ->  List[Any]:
      lowerCAmelCase_      :				Any      =       self._get_input_ids_and_config()

      # pad attention mask on the left
      lowerCAmelCase_      :				Union[str, Any]      =       attention_mask.at[(0, 0)].set(0		)

      lowerCAmelCase_      :				Tuple      =       True
      lowerCAmelCase_      :				List[Any]      =       max_length

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				int      =       model.generate(lowerCamelCase   ,						attention_mask=lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				str      =       jit(model.generate		)
        lowerCAmelCase_      :				int      =       jit_generate(lowerCamelCase   ,						attention_mask=lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)





    def __lowercase       (       self   :  Optional[Any]		)       ->  Optional[int]:
      lowerCAmelCase_      :				Dict      =       self._get_input_ids_and_config()

      # pad attention mask on the left
      lowerCAmelCase_      :				List[Any]      =       attention_mask.at[(0, 0)].set(0		)

      lowerCAmelCase_      :				str      =       2
      lowerCAmelCase_      :				Tuple      =       max_length

      for model_class in self.all_generative_model_classes:
        lowerCAmelCase_      :				Optional[Any]      =       model_class(lowerCamelCase		)

        lowerCAmelCase_      :				Optional[Any]      =       model.generate(lowerCamelCase   ,						attention_mask=lowerCamelCase		).sequences
        self.assertEqual(generation_outputs.shape[-1]   ,						lowerCamelCase		)

        lowerCAmelCase_      :				Optional[int]      =       jit(model.generate		)
        lowerCAmelCase_      :				Tuple      =       jit_generate(lowerCamelCase   ,						attention_mask=lowerCamelCase		).sequences

        self.assertListEqual(generation_outputs.tolist()   ,						jit_generation_outputs.tolist()		)







@require_flax
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Any		)       ->  List[str]:
      lowerCAmelCase_      :				int      =       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""		)
      lowerCAmelCase_      :				Optional[int]      =       FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""		)

      lowerCAmelCase_      :				Any      =       """Hello world"""
      lowerCAmelCase_      :				List[str]      =       tokenizer(lowerCamelCase   ,						return_tensors="""np"""		).input_ids

      # typos are quickly detected (the correct argument is `do_sample`)
      with self.assertRaisesRegex(lowerCamelCase   ,						"""do_samples"""		):
        model.generate(lowerCamelCase   ,						do_samples=lowerCamelCase		)

      # arbitrary arguments that will not be used anywhere are also not accepted
      with self.assertRaisesRegex(lowerCamelCase   ,						"""foo"""		):
        lowerCAmelCase_      :				Optional[Any]      =       {"""foo""": """bar"""}
        model.generate(lowerCamelCase   ,						**lowerCamelCase		)







'''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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       Tuple=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       []
  for i in range(config.num_hidden_layers ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight') )
    rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias') )
    rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight') )
    rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias') )
    rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight') )
    rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias') )
    rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight') )
    rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias') )
    rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight') )
    rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias') )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""cls_token""", """vit.embeddings.cls_token"""),
          ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
          ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
          ("""pos_embed""", """vit.embeddings.position_embeddings"""),
      ] )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("""norm.weight""", """layernorm.weight"""),
            ("""norm.bias""", """layernorm.bias"""),
            ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
            ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
        ] )

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    lowerCAmelCase_      :				List[str]      =       [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("""norm.weight""", """vit.layernorm.weight"""),
            ("""norm.bias""", """vit.layernorm.bias"""),
            ("""head.weight""", """classifier.weight"""),
            ("""head.bias""", """classifier.bias"""),
        ] )

  return rename_keys







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Any							,   A__ :       Tuple=False ):
  '''simple docstring'''





  for i in range(config.num_hidden_layers ):
    if base_model:
      lowerCAmelCase_      :				Optional[Any]      =       """"""
    else:
      lowerCAmelCase_      :				Optional[Any]      =       """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCAmelCase_      :				List[Any]      =       state_dict.pop(f'blocks.{i}.attn.qkv.weight' )
    lowerCAmelCase_      :				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
    lowerCAmelCase_      :				Dict      =       in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase_      :				List[Any]      =       in_proj_bias[: config.hidden_size]
    lowerCAmelCase_      :				Union[str, Any]      =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase_      :				List[str]      =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCAmelCase_      :				Any      =       in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase_      :				Union[str, Any]      =       in_proj_bias[-config.hidden_size :]







def     UpperCamelCase_      (   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Dict      =       ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(A__							,   A__ )







def     UpperCamelCase_      (   A__ :       List[Any]							,   A__ :       Optional[Any]							,   A__ :       Dict ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       dct.pop(A__ )
  lowerCAmelCase_      :				Tuple      =       val







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				Optional[int]      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Union[str, Any]							,   A__ :       List[Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       ViTConfig()
  lowerCAmelCase_      :				Any      =       False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       True
    lowerCAmelCase_      :				Tuple      =       int(vit_name[-12:-10] )
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-9:-6] )
  else:
    lowerCAmelCase_      :				Optional[int]      =       10_00
    lowerCAmelCase_      :				Tuple      =       """huggingface/label-files"""
    lowerCAmelCase_      :				Any      =       """imagenet-1k-id2label.json"""
    lowerCAmelCase_      :				Dict      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
    lowerCAmelCase_      :				Union[str, Any]      =       {int(A__ ): v for k, v in idalabel.items()}
    lowerCAmelCase_      :				Union[str, Any]      =       idalabel
    lowerCAmelCase_      :				Union[str, Any]      =       {v: k for k, v in idalabel.items()}
    lowerCAmelCase_      :				Optional[int]      =       int(vit_name[-6:-4] )
    lowerCAmelCase_      :				Dict      =       int(vit_name[-3:] )
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("""tiny""" ):
      lowerCAmelCase_      :				int      =       1_92
      lowerCAmelCase_      :				List[str]      =       7_68
      lowerCAmelCase_      :				List[str]      =       12
      lowerCAmelCase_      :				int      =       3
    elif vit_name[9:].startswith("""small""" ):
      lowerCAmelCase_      :				Optional[Any]      =       3_84
      lowerCAmelCase_      :				Optional[int]      =       15_36
      lowerCAmelCase_      :				Dict      =       12
      lowerCAmelCase_      :				str      =       6
    else:
      pass
  else:
    if vit_name[4:].startswith("""small""" ):
      lowerCAmelCase_      :				Tuple      =       7_68
      lowerCAmelCase_      :				Any      =       23_04
      lowerCAmelCase_      :				List[str]      =       8
      lowerCAmelCase_      :				List[str]      =       8
    elif vit_name[4:].startswith("""base""" ):
      pass
    elif vit_name[4:].startswith("""large""" ):
      lowerCAmelCase_      :				Dict      =       10_24
      lowerCAmelCase_      :				List[Any]      =       40_96
      lowerCAmelCase_      :				Any      =       24
      lowerCAmelCase_      :				List[str]      =       16
    elif vit_name[4:].startswith("""huge""" ):
      lowerCAmelCase_      :				Optional[int]      =       12_80
      lowerCAmelCase_      :				Dict      =       51_20
      lowerCAmelCase_      :				Union[str, Any]      =       32
      lowerCAmelCase_      :				Optional[int]      =       16

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

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

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_      :				int      =       ViTModel(A__ ).eval()
  else:
    lowerCAmelCase_      :				Optional[int]      =       ViTForImageClassification(A__ ).eval()
  model.load_state_dict(A__ )

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

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

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


if __name__ == "__main__":
  __A :  Optional[int]    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_patch16_224",
      type=str,
      help="Name of the ViT timm model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

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



