


import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import BertGenerationDecoder, BertGenerationEncoder





class  _a							:




 def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]=13		, _SCREAMING_SNAKE_CASE						:		Dict=7		, _SCREAMING_SNAKE_CASE						:		Optional[int]=True		, _SCREAMING_SNAKE_CASE						:		Tuple=True		, _SCREAMING_SNAKE_CASE						:		List[str]=99		, _SCREAMING_SNAKE_CASE						:		Dict=32		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=5		, _SCREAMING_SNAKE_CASE						:		List[Any]=4		, _SCREAMING_SNAKE_CASE						:		List[str]=37		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.1		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=50		, _SCREAMING_SNAKE_CASE						:		Dict=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=True		, _SCREAMING_SNAKE_CASE						:		Any=None		, )->	Any:
   lowerCAmelCase__       :      str   					= parent
   lowerCAmelCase__       :      Tuple   					= batch_size
   lowerCAmelCase__       :      Optional[int]   					= seq_length
   lowerCAmelCase__       :      Union[str, Any]   					= is_training
   lowerCAmelCase__       :      Optional[Any]   					= use_input_mask
   lowerCAmelCase__       :      Optional[Any]   					= vocab_size
   lowerCAmelCase__       :      Tuple   					= hidden_size
   lowerCAmelCase__       :      Tuple   					= num_hidden_layers
   lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
   lowerCAmelCase__       :      Optional[int]   					= intermediate_size
   lowerCAmelCase__       :      int   					= hidden_act
   lowerCAmelCase__       :      Any   					= hidden_dropout_prob
   lowerCAmelCase__       :      Any   					= attention_probs_dropout_prob
   lowerCAmelCase__       :      int   					= max_position_embeddings
   lowerCAmelCase__       :      Any   					= initializer_range
   lowerCAmelCase__       :      Tuple   					= use_labels
   lowerCAmelCase__       :      str   					= scope




 def 							UpperCAmelCase__( self						:		str		)->	Optional[Any]:
   lowerCAmelCase__       :      Union[str, Any]   					= ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size		)

   lowerCAmelCase__       :      List[str]   					= None
   if self.use_input_mask:
     lowerCAmelCase__       :      Dict   					= random_attention_mask([self.batch_size, self.seq_length]		)

   if self.use_labels:
     lowerCAmelCase__       :      Union[str, Any]   					= ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size		)

   lowerCAmelCase__       :      List[Any]   					= self.get_config()

   return config, input_ids, input_mask, token_labels




 def 							UpperCAmelCase__( self						:		int		)->	Dict:
   return BertGenerationConfig(
       vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, is_decoder=_SCREAMING_SNAKE_CASE		, initializer_range=self.initializer_range		, )




 def 							UpperCAmelCase__( self						:		Tuple		)->	Tuple:
   (
       lowerCAmelCase__     
   )       :      int   					= self.prepare_config_and_inputs()

   lowerCAmelCase__       :      Optional[Any]   					= True
   lowerCAmelCase__       :      Optional[int]   					= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
   lowerCAmelCase__       :      int   					= ids_tensor([self.batch_size, self.seq_length]		, vocab_size=2		)

   return (
       config,
       input_ids,
       input_mask,
       token_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )




 def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Dict		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	Optional[Any]:
   lowerCAmelCase__       :      Dict   					= BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE		)
   model.to(_SCREAMING_SNAKE_CASE		)
   model.eval()
   lowerCAmelCase__       :      Optional[Any]   					= model(_SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Any   					= model(_SCREAMING_SNAKE_CASE		)
   self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)		)




 def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		List[Any]		, **_SCREAMING_SNAKE_CASE						:		int		, )->	List[str]:
   lowerCAmelCase__       :      List[str]   					= True
   lowerCAmelCase__       :      Optional[Any]   					= BertGenerationEncoder(config=_SCREAMING_SNAKE_CASE		)
   model.to(_SCREAMING_SNAKE_CASE		)
   model.eval()
   lowerCAmelCase__       :      List[str]   					= model(
       _SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, encoder_hidden_states=_SCREAMING_SNAKE_CASE		, encoder_attention_mask=_SCREAMING_SNAKE_CASE		, )
   lowerCAmelCase__       :      Optional[Any]   					= model(
       _SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, encoder_hidden_states=_SCREAMING_SNAKE_CASE		, )
   self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)		)




 def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, **_SCREAMING_SNAKE_CASE						:		List[Any]		, )->	Any:
   lowerCAmelCase__       :      Optional[int]   					= True
   lowerCAmelCase__       :      int   					= True
   lowerCAmelCase__       :      Dict   					= BertGenerationDecoder(config=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		).eval()

   # first forward pass
   lowerCAmelCase__       :      List[Any]   					= model(
       _SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, encoder_hidden_states=_SCREAMING_SNAKE_CASE		, encoder_attention_mask=_SCREAMING_SNAKE_CASE		, use_cache=_SCREAMING_SNAKE_CASE		, )
   lowerCAmelCase__       :      Any   					= outputs.past_key_values

   # create hypothetical multiple next token and extent to next_input_ids
   lowerCAmelCase__       :      List[Any]   					= ids_tensor((self.batch_size, 3)		, config.vocab_size		)
   lowerCAmelCase__       :      int   					= ids_tensor((self.batch_size, 3)		, vocab_size=2		)

   # append to next input_ids and
   lowerCAmelCase__       :      List[Any]   					= torch.cat([input_ids, next_tokens]		, dim=-1		)
   lowerCAmelCase__       :      Dict   					= torch.cat([input_mask, next_mask]		, dim=-1		)

   lowerCAmelCase__       :      Tuple   					= model(
       _SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, encoder_hidden_states=_SCREAMING_SNAKE_CASE		, encoder_attention_mask=_SCREAMING_SNAKE_CASE		, output_hidden_states=_SCREAMING_SNAKE_CASE		, )['''hidden_states'''][0]
   lowerCAmelCase__       :      Optional[Any]   					= model(
       _SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, encoder_hidden_states=_SCREAMING_SNAKE_CASE		, encoder_attention_mask=_SCREAMING_SNAKE_CASE		, past_key_values=_SCREAMING_SNAKE_CASE		, output_hidden_states=_SCREAMING_SNAKE_CASE		, )['''hidden_states'''][0]

   # select random slice
   lowerCAmelCase__       :      Tuple   					= ids_tensor((1,)		, output_from_past.shape[-1]		).item()
   lowerCAmelCase__       :      Optional[Any]   					= output_from_no_past[:, -3:, random_slice_idx].detach()
   lowerCAmelCase__       :      List[str]   					= output_from_past[:, :, random_slice_idx].detach()

   self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

   # test that outputs are equal for slice
   self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, atol=1E-3		)		)




 def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, *_SCREAMING_SNAKE_CASE						:		List[str]		, )->	int:
   lowerCAmelCase__       :      Tuple   					= BertGenerationDecoder(_SCREAMING_SNAKE_CASE		)
   model.to(_SCREAMING_SNAKE_CASE		)
   model.eval()
   lowerCAmelCase__       :      Optional[Any]   					= model(_SCREAMING_SNAKE_CASE		, attention_mask=_SCREAMING_SNAKE_CASE		, labels=_SCREAMING_SNAKE_CASE		)
   self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)		)




 def 							UpperCAmelCase__( self						:		str		)->	str:
   lowerCAmelCase__       :      int   					= self.prepare_config_and_inputs()
   lowerCAmelCase__       :      str   					= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict





@require_torch
class  _a							(    _lowercase					,						_lowercase					,						_lowercase					,						unittest.TestCase):
 _a  :  str				   =							(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
 _a  :  Tuple				   =							(BertGenerationDecoder,) if is_torch_available() else ()
 _a  :  int				   =							(
     {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
     if is_torch_available()
     else {}
 )




 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	List[Any]:
   lowerCAmelCase__       :      Tuple   					= BertGenerationEncoderTester(self		)
   lowerCAmelCase__       :      Union[str, Any]   					= ConfigTester(self		, config_class=_SCREAMING_SNAKE_CASE		, hidden_size=37		)




 def 							UpperCAmelCase__( self						:		str		)->	Tuple:
   self.config_tester.run_common_tests()




 def 							UpperCAmelCase__( self						:		Optional[int]		)->	Optional[Any]:
   lowerCAmelCase__       :      Optional[Any]   					= self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		int		)->	Optional[Any]:
   lowerCAmelCase__       :      Optional[int]   					= self.model_tester.prepare_config_and_inputs()
   lowerCAmelCase__       :      Union[str, Any]   					= '''bert'''
   self.model_tester.create_and_check_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		Optional[Any]		)->	str:
   lowerCAmelCase__       :      Any   					= self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*_SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		Any		)->	Dict:
   lowerCAmelCase__       :      Optional[Any]   					= self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		List[str]		)->	Any:
   # This regression test was failing with PyTorch < 1.3
   (
       lowerCAmelCase__     
   )       :      Union[str, Any]   					= self.model_tester.prepare_config_and_inputs_for_decoder()

   lowerCAmelCase__       :      str   					= None

   self.model_tester.create_and_check_model_as_decoder(
       _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, )




 def 							UpperCAmelCase__( self						:		Tuple		)->	Any:
   lowerCAmelCase__       :      int   					= self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_for_causal_lm(*_SCREAMING_SNAKE_CASE		)




 @slow
 def 							UpperCAmelCase__( self						:		Tuple		)->	str:
   lowerCAmelCase__       :      List[str]   					= BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''		)
   self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)





@require_torch
class  _a							(    unittest.TestCase):




 @slow
 def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
   lowerCAmelCase__       :      Optional[int]   					= BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''		)
   lowerCAmelCase__       :      str   					= torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]		)
   with torch.no_grad():
     lowerCAmelCase__       :      Optional[int]   					= model(_SCREAMING_SNAKE_CASE		)[0]
   lowerCAmelCase__       :      int   					= torch.Size([1, 8, 1024]		)
   self.assertEqual(output.shape		, _SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      str   					= torch.tensor(
       [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]		)
   self.assertTrue(torch.allclose(output[:, :3, :3]		, _SCREAMING_SNAKE_CASE		, atol=1E-4		)		)





@require_torch
class  _a							(    unittest.TestCase):




 @slow
 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	str:
   lowerCAmelCase__       :      Union[str, Any]   					= BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''		)
   lowerCAmelCase__       :      List[Any]   					= torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]		)
   with torch.no_grad():
     lowerCAmelCase__       :      int   					= model(_SCREAMING_SNAKE_CASE		)[0]
   lowerCAmelCase__       :      Optional[int]   					= torch.Size([1, 8, 5_0358]		)
   self.assertEqual(output.shape		, _SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      str   					= torch.tensor(
       [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]		)
   self.assertTrue(torch.allclose(output[:, :3, :3]		, _SCREAMING_SNAKE_CASE		, atol=1E-4		)		)








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()
lowerCamelCase						=		logging.get_logger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= []
		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__       :      Tuple   					= [(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 					lowerCamelCase_   (	_a     ,       _a     ,       _a=False	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(_a     ,       _a	)






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= dct.pop(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= val






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__       :      Tuple   					= Image.open(requests.get(_a     ,       stream=_a	).raw	)
		return im






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= ViTConfig()
		lowerCAmelCase__       :      Optional[Any]   					= False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[Any]   					= True
				lowerCAmelCase__       :      Any   					= int(vit_name[-12:-10]	)
				lowerCAmelCase__       :      int   					= int(vit_name[-9:-6]	)
		else:
				lowerCAmelCase__       :      Dict   					= 1_000
				lowerCAmelCase__       :      str   					= '''huggingface/label-files'''
				lowerCAmelCase__       :      Dict   					= '''imagenet-1k-id2label.json'''
				lowerCAmelCase__       :      str   					= json.load(open(hf_hub_download(_a     ,       _a     ,       repo_type='''dataset'''	)     ,       '''r'''	)	)
				lowerCAmelCase__       :      Any   					= {int(_a	): v for k, v in idalabel.items()}
				lowerCAmelCase__       :      Optional[Any]   					= idalabel
				lowerCAmelCase__       :      List[str]   					= {v: k for k, v in idalabel.items()}
				lowerCAmelCase__       :      Tuple   					= int(vit_name[-6:-4]	)
				lowerCAmelCase__       :      Union[str, Any]   					= int(vit_name[-3:]	)
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('''tiny'''	):
						lowerCAmelCase__       :      List[str]   					= 192
						lowerCAmelCase__       :      Tuple   					= 768
						lowerCAmelCase__       :      Optional[int]   					= 12
						lowerCAmelCase__       :      List[Any]   					= 3
				elif vit_name[9:].startswith('''small'''	):
						lowerCAmelCase__       :      Any   					= 384
						lowerCAmelCase__       :      Optional[int]   					= 1_536
						lowerCAmelCase__       :      List[Any]   					= 12
						lowerCAmelCase__       :      Tuple   					= 6
				else:
						pass
		else:
				if vit_name[4:].startswith('''small'''	):
						lowerCAmelCase__       :      List[str]   					= 768
						lowerCAmelCase__       :      Tuple   					= 2_304
						lowerCAmelCase__       :      Any   					= 8
						lowerCAmelCase__       :      Union[str, Any]   					= 8
				elif vit_name[4:].startswith('''base'''	):
						pass
				elif vit_name[4:].startswith('''large'''	):
						lowerCAmelCase__       :      str   					= 1_024
						lowerCAmelCase__       :      Optional[Any]   					= 4_096
						lowerCAmelCase__       :      Optional[int]   					= 24
						lowerCAmelCase__       :      Tuple   					= 16
				elif vit_name[4:].startswith('''huge'''	):
						lowerCAmelCase__       :      Tuple   					= 1_280
						lowerCAmelCase__       :      Tuple   					= 5_120
						lowerCAmelCase__       :      Optional[int]   					= 32
						lowerCAmelCase__       :      List[Any]   					= 16

    # load original model from timm
		lowerCAmelCase__       :      Tuple   					= timm.create_model(_a     ,       pretrained=_a	)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowerCAmelCase__       :      Any   					= timm_model.state_dict()
		if base_model:
				remove_classification_head_(_a	)
		lowerCAmelCase__       :      List[Any]   					= 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__       :      List[str]   					= ViTModel(_a	).eval()
		else:
				lowerCAmelCase__       :      Any   					= ViTForImageClassification(_a	).eval()
		model.load_state_dict(_a	)

		# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
		if "deit" in vit_name:
				lowerCAmelCase__       :      Dict   					= DeiTImageProcessor(size=config.image_size	)
		else:
				lowerCAmelCase__       :      int   					= ViTImageProcessor(size=config.image_size	)
		lowerCAmelCase__       :      Optional[Any]   					= image_processor(images=prepare_img()     ,       return_tensors='''pt'''	)
		lowerCAmelCase__       :      List[str]   					= encoding['''pixel_values''']
		lowerCAmelCase__       :      List[Any]   					= model(_a	)

		if base_model:
				lowerCAmelCase__       :      Optional[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__":
		lowerCamelCase						=		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.'''
		)

		lowerCamelCase						=		parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




