


import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


lowerCamelCase						=		1E-4

if is_torch_available():
  import torch

  from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
  from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder





@require_torch
class  _a							:




 def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		int=16		, _SCREAMING_SNAKE_CASE						:		Dict=13		, _SCREAMING_SNAKE_CASE						:		List[str]=7		, _SCREAMING_SNAKE_CASE						:		Any=14		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=10		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=19		, _SCREAMING_SNAKE_CASE						:		str=5		, _SCREAMING_SNAKE_CASE						:		Tuple=4		, _SCREAMING_SNAKE_CASE						:		Optional[int]=True		, _SCREAMING_SNAKE_CASE						:		List[Any]=16		, _SCREAMING_SNAKE_CASE						:		List[Any]=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=4		, _SCREAMING_SNAKE_CASE						:		List[Any]=4		, _SCREAMING_SNAKE_CASE						:		int="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=[1, 2, 3, 4, 5]		, _SCREAMING_SNAKE_CASE						:		List[Any]=25		, _SCREAMING_SNAKE_CASE						:		List[Any]=5		, )->	Union[str, Any]:
   lowerCAmelCase__       :      List[str]   					= d_model
   lowerCAmelCase__       :      Dict   					= parent
   lowerCAmelCase__       :      Union[str, Any]   					= batch_size
   lowerCAmelCase__       :      Tuple   					= prediction_length
   lowerCAmelCase__       :      List[str]   					= context_length
   lowerCAmelCase__       :      Any   					= cardinality
   lowerCAmelCase__       :      int   					= num_time_features
   lowerCAmelCase__       :      int   					= lags_sequence
   lowerCAmelCase__       :      Any   					= embedding_dimension
   lowerCAmelCase__       :      str   					= is_training
   lowerCAmelCase__       :      List[str]   					= hidden_size
   lowerCAmelCase__       :      Optional[Any]   					= num_hidden_layers
   lowerCAmelCase__       :      List[Any]   					= num_attention_heads
   lowerCAmelCase__       :      Union[str, Any]   					= intermediate_size
   lowerCAmelCase__       :      Tuple   					= hidden_act
   lowerCAmelCase__       :      Union[str, Any]   					= hidden_dropout_prob
   lowerCAmelCase__       :      Union[str, Any]   					= attention_probs_dropout_prob

   lowerCAmelCase__       :      Dict   					= context_length
   lowerCAmelCase__       :      int   					= prediction_length + label_length
   lowerCAmelCase__       :      Optional[Any]   					= label_length

   lowerCAmelCase__       :      Optional[Any]   					= moving_average
   lowerCAmelCase__       :      Tuple   					= autocorrelation_factor




 def 							UpperCAmelCase__( self						:		Dict		)->	str:
   return AutoformerConfig(
       d_model=self.d_model		, encoder_layers=self.num_hidden_layers		, decoder_layers=self.num_hidden_layers		, encoder_attention_heads=self.num_attention_heads		, decoder_attention_heads=self.num_attention_heads		, encoder_ffn_dim=self.intermediate_size		, decoder_ffn_dim=self.intermediate_size		, dropout=self.hidden_dropout_prob		, attention_dropout=self.attention_probs_dropout_prob		, prediction_length=self.prediction_length		, context_length=self.context_length		, label_length=self.label_length		, lags_sequence=self.lags_sequence		, num_time_features=self.num_time_features		, num_static_categorical_features=1		, cardinality=[self.cardinality]		, embedding_dimension=[self.embedding_dimension]		, moving_average=self.moving_average		, )




 def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		int		)->	Any:
   lowerCAmelCase__       :      Dict   					= config.context_length + max(config.lags_sequence		)

   lowerCAmelCase__       :      List[str]   					= ids_tensor([self.batch_size, 1]		, config.cardinality[0]		)
   lowerCAmelCase__       :      int   					= floats_tensor([self.batch_size, _past_length, config.num_time_features]		)
   lowerCAmelCase__       :      List[Any]   					= floats_tensor([self.batch_size, _past_length]		)
   lowerCAmelCase__       :      Union[str, Any]   					= floats_tensor([self.batch_size, _past_length]		) > 0.5

   # decoder inputs
   lowerCAmelCase__       :      Dict   					= floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]		)
   lowerCAmelCase__       :      Dict   					= floats_tensor([self.batch_size, config.prediction_length]		)

   lowerCAmelCase__       :      Union[str, Any]   					= {
       '''past_values''': past_values,
       '''static_categorical_features''': static_categorical_features,
       '''past_time_features''': past_time_features,
       '''past_observed_mask''': past_observed_mask,
       '''future_time_features''': future_time_features,
       '''future_values''': future_values,
   }
   return inputs_dict




 def 							UpperCAmelCase__( self						:		Any		)->	List[str]:
   lowerCAmelCase__       :      List[Any]   					= self.get_config()
   lowerCAmelCase__       :      Optional[int]   					= self.prepare_autoformer_inputs_dict(_SCREAMING_SNAKE_CASE		)
   return config, inputs_dict




 def 							UpperCAmelCase__( self						:		str		)->	Union[str, Any]:
   lowerCAmelCase__       :      Tuple   					= self.prepare_config_and_inputs()
   return config, inputs_dict




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

   lowerCAmelCase__       :      Any   					= outputs.encoder_last_hidden_state
   lowerCAmelCase__       :      int   					= outputs.last_hidden_state

   with tempfile.TemporaryDirectory() as tmpdirname:
     lowerCAmelCase__       :      List[Any]   					= model.get_encoder()
     encoder.save_pretrained(_SCREAMING_SNAKE_CASE		)
     lowerCAmelCase__       :      Optional[int]   					= AutoformerEncoder.from_pretrained(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      Optional[Any]   					= model.create_network_inputs(**_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Any   					= model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]		)

   lowerCAmelCase__       :      List[str]   					= torch.cat(
       (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])		, dim=-1		, )
   lowerCAmelCase__       :      Union[str, Any]   					= encoder(inputs_embeds=_SCREAMING_SNAKE_CASE		)[0]
   self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

   lowerCAmelCase__       :      Dict   					= (
       torch.mean(transformer_inputs[:, : config.context_length, ...]		, dim=1		)
       .unsqueeze(1		)
       .repeat(1		, config.prediction_length		, 1		)
   )
   lowerCAmelCase__       :      Union[str, Any]   					= torch.zeros(
       [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]		, device=enc_input.device		, )

   lowerCAmelCase__       :      str   					= torch.cat(
       (
           torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)		, dim=1		),
           feature[:, config.context_length - config.label_length :, ...],
       )		, dim=-1		, )
   lowerCAmelCase__       :      Union[str, Any]   					= torch.cat(
       (
           torch.cat((trend_input[:, -config.label_length :, ...], mean)		, dim=1		),
           feature[:, config.context_length - config.label_length :, ...],
       )		, dim=-1		, )

   with tempfile.TemporaryDirectory() as tmpdirname:
     lowerCAmelCase__       :      str   					= model.get_decoder()
     decoder.save_pretrained(_SCREAMING_SNAKE_CASE		)
     lowerCAmelCase__       :      List[str]   					= AutoformerDecoder.from_pretrained(_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      Dict   					= decoder(
       trend=_SCREAMING_SNAKE_CASE		, inputs_embeds=_SCREAMING_SNAKE_CASE		, encoder_hidden_states=_SCREAMING_SNAKE_CASE		, )[0]

   self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)





@require_torch
class  _a							(    _lowercase					,						_lowercase					,						unittest.TestCase):
 _a  :  int				   =							(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
 _a  :  List[str]				   =							(AutoformerForPrediction,) if is_torch_available() else ()
 _a  :  Any				   =							{'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
 _a  :  Tuple				   =							False
 _a  :  Dict				   =							False
 _a  :  Tuple				   =							False
 _a  :  List[str]				   =							False
 _a  :  Optional[Any]				   =							False
 _a  :  str				   =							False




 def 							UpperCAmelCase__( self						:		str		)->	str:
   lowerCAmelCase__       :      Union[str, Any]   					= AutoformerModelTester(self		)
   lowerCAmelCase__       :      Any   					= ConfigTester(self		, config_class=_SCREAMING_SNAKE_CASE		, has_text_modality=_SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		List[Any]		)->	str:
   self.config_tester.run_common_tests()




 def 							UpperCAmelCase__( self						:		Any		)->	List[Any]:
   lowerCAmelCase__       :      List[Any]   					= self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     lowerCAmelCase__       :      Optional[int]   					= model_class(_SCREAMING_SNAKE_CASE		)

     with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(_SCREAMING_SNAKE_CASE		)
       lowerCAmelCase__       :      Optional[int]   					= model_class.from_pretrained(_SCREAMING_SNAKE_CASE		, output_loading_info=_SCREAMING_SNAKE_CASE		)
     self.assertEqual(info['''missing_keys''']		, []		)




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




 @unittest.skip(reason='''Model has no tokens embeddings'''		)
 def 							UpperCAmelCase__( self						:		Optional[Any]		)->	List[str]:
   pass




 def 							UpperCAmelCase__( self						:		List[str]		)->	Union[str, Any]:
   lowerCAmelCase__       :      Any   					= inspect.signature(getattr(_SCREAMING_SNAKE_CASE		, '''forward'''		)		)
   # The main input is the name of the argument after `self`
   lowerCAmelCase__       :      Optional[int]   					= list(model_signature.parameters.keys()		)[1]
   self.assertEqual(AutoformerModel.main_input_name		, _SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		int		)->	List[Any]:
   lowerCAmelCase__       :      Any   					= self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCAmelCase__       :      List[Any]   					= model_class(_SCREAMING_SNAKE_CASE		)
     lowerCAmelCase__       :      Optional[int]   					= inspect.signature(model.forward		)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowerCAmelCase__       :      str   					= [*signature.parameters.keys()]

     lowerCAmelCase__       :      Union[str, Any]   					= [
         '''past_values''',
         '''past_time_features''',
         '''past_observed_mask''',
         '''static_categorical_features''',
         '''static_real_features''',
         '''future_values''',
         '''future_time_features''',
     ]

     if model.__class__.__name__ in ["AutoformerForPrediction"]:
       expected_arg_names.append('''future_observed_mask'''		)

     expected_arg_names.extend(
         [
             '''decoder_attention_mask''',
             '''head_mask''',
             '''decoder_head_mask''',
             '''cross_attn_head_mask''',
             '''encoder_outputs''',
             '''past_key_values''',
             '''output_hidden_states''',
             '''output_attentions''',
             '''use_cache''',
             '''return_dict''',
         ]		)

     self.assertListEqual(arg_names[: len(_SCREAMING_SNAKE_CASE		)]		, _SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
   lowerCAmelCase__       :      Dict   					= self.model_tester.prepare_config_and_inputs_for_common()
   lowerCAmelCase__       :      Dict   					= True

   lowerCAmelCase__       :      Dict   					= getattr(self.model_tester		, '''seq_length'''		, _SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Union[str, Any]   					= getattr(self.model_tester		, '''decoder_seq_length'''		, _SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      List[Any]   					= getattr(self.model_tester		, '''encoder_seq_length'''		, _SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Optional[int]   					= getattr(self.model_tester		, '''d_model'''		, _SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      str   					= getattr(self.model_tester		, '''num_attention_heads'''		, _SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Union[str, Any]   					= d_model // num_attention_heads

   for model_class in self.all_model_classes:
     lowerCAmelCase__       :      int   					= True
     lowerCAmelCase__       :      Optional[Any]   					= False
     lowerCAmelCase__       :      Optional[int]   					= True
     lowerCAmelCase__       :      Union[str, Any]   					= model_class(_SCREAMING_SNAKE_CASE		)
     model.to(_SCREAMING_SNAKE_CASE		)
     model.eval()
     with torch.no_grad():
       lowerCAmelCase__       :      Tuple   					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)
     lowerCAmelCase__       :      Tuple   					= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     lowerCAmelCase__       :      int   					= True
     lowerCAmelCase__       :      Any   					= model_class(_SCREAMING_SNAKE_CASE		)
     model.to(_SCREAMING_SNAKE_CASE		)
     model.eval()
     with torch.no_grad():
       lowerCAmelCase__       :      int   					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)
     lowerCAmelCase__       :      List[str]   					= outputs.encoder_attentions
     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)

     self.assertListEqual(
         list(attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, encoder_seq_length, dim]		, )
     lowerCAmelCase__       :      Any   					= len(_SCREAMING_SNAKE_CASE		)

     lowerCAmelCase__       :      Any   					= 7

     if "last_hidden_state" in outputs:
       correct_outlen += 1

     if "trend" in outputs:
       correct_outlen += 1

     if "past_key_values" in outputs:
       correct_outlen += 1  # past_key_values have been returned

     if "loss" in outputs:
       correct_outlen += 1

     if "params" in outputs:
       correct_outlen += 1

     self.assertEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

     # decoder attentions
     lowerCAmelCase__       :      Union[str, Any]   					= outputs.decoder_attentions
     self.assertIsInstance(_SCREAMING_SNAKE_CASE		, (list, tuple)		)
     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)
     self.assertListEqual(
         list(decoder_attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, decoder_seq_length, dim]		, )

     # cross attentions
     lowerCAmelCase__       :      int   					= outputs.cross_attentions
     self.assertIsInstance(_SCREAMING_SNAKE_CASE		, (list, tuple)		)
     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)
     self.assertListEqual(
         list(cross_attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, decoder_seq_length, dim]		, )

   # Check attention is always last and order is fine
   lowerCAmelCase__       :      Tuple   					= True
   lowerCAmelCase__       :      Optional[Any]   					= True
   lowerCAmelCase__       :      Dict   					= model_class(_SCREAMING_SNAKE_CASE		)
   model.to(_SCREAMING_SNAKE_CASE		)
   model.eval()
   with torch.no_grad():
     lowerCAmelCase__       :      Union[str, Any]   					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)

   self.assertEqual(out_len + 2		, len(_SCREAMING_SNAKE_CASE		)		)

   lowerCAmelCase__       :      List[str]   					= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

   self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, self.model_tester.num_hidden_layers		)
   self.assertListEqual(
       list(self_attentions[0].shape[-3:]		)		, [self.model_tester.num_attention_heads, encoder_seq_length, dim]		, )




 @is_flaky()
 def 							UpperCAmelCase__( self						:		str		)->	List[str]:
   super().test_retain_grad_hidden_states_attentions()






def 					lowerCamelCase_   (	_a="train-batch.pt"	):




  """simple docstring"""






  lowerCAmelCase__       :      str   					= hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''     ,       filename=_a     ,       repo_type='''dataset'''	)
  lowerCAmelCase__       :      List[str]   					= torch.load(_a     ,       map_location=_a	)
  return batch





@require_torch
@slow
class  _a							(    unittest.TestCase):




 def 							UpperCAmelCase__( self						:		str		)->	Dict:
   lowerCAmelCase__       :      Any   					= AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Optional[int]   					= prepare_batch()

   with torch.no_grad():
     lowerCAmelCase__       :      Tuple   					= model(
         past_values=batch['''past_values''']		, past_time_features=batch['''past_time_features''']		, past_observed_mask=batch['''past_observed_mask''']		, static_categorical_features=batch['''static_categorical_features''']		, future_values=batch['''future_values''']		, future_time_features=batch['''future_time_features''']		, )[0]

   lowerCAmelCase__       :      str   					= torch.Size(
       (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)		)
   self.assertEqual(output.shape		, _SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      int   					= torch.tensor(
       [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]		, device=_SCREAMING_SNAKE_CASE		)
   self.assertTrue(torch.allclose(output[0, :3, :3]		, _SCREAMING_SNAKE_CASE		, atol=_SCREAMING_SNAKE_CASE		)		)




 def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
   lowerCAmelCase__       :      Any   					= AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Tuple   					= prepare_batch('''val-batch.pt'''		)
   with torch.no_grad():
     lowerCAmelCase__       :      int   					= model(
         past_values=batch['''past_values''']		, past_time_features=batch['''past_time_features''']		, past_observed_mask=batch['''past_observed_mask''']		, static_categorical_features=batch['''static_categorical_features''']		, ).encoder_last_hidden_state
   lowerCAmelCase__       :      str   					= torch.Size((64, model.config.context_length, model.config.d_model)		)
   self.assertEqual(output.shape		, _SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      str   					= torch.tensor(
       [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]		, device=_SCREAMING_SNAKE_CASE		)
   self.assertTrue(torch.allclose(output[0, :3, :3]		, _SCREAMING_SNAKE_CASE		, atol=_SCREAMING_SNAKE_CASE		)		)




 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Tuple:
   lowerCAmelCase__       :      str   					= AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Union[str, Any]   					= prepare_batch('''val-batch.pt'''		)
   with torch.no_grad():
     lowerCAmelCase__       :      Union[str, Any]   					= model.generate(
         static_categorical_features=batch['''static_categorical_features''']		, past_time_features=batch['''past_time_features''']		, past_values=batch['''past_values''']		, future_time_features=batch['''future_time_features''']		, past_observed_mask=batch['''past_observed_mask''']		, )
   lowerCAmelCase__       :      int   					= torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)		)
   self.assertEqual(outputs.sequences.shape		, _SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      Union[str, Any]   					= torch.tensor([3130.6763, 4056.5293, 7053.0786]		, device=_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      Optional[Any]   					= outputs.sequences.mean(dim=1		)
   self.assertTrue(torch.allclose(mean_prediction[0, -3:]		, _SCREAMING_SNAKE_CASE		, rtol=1E-1		)		)








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)




