'''simple docstring'''




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


__SCREAMING_SNAKE_CASE       :     List[Any]     =  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 	lowerCamelCase_	:
      '''simple docstring'''

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

             _UpperCAmelCase     :  Optional[int]							      =    context_length
             _UpperCAmelCase     :  Dict							      =    prediction_length + label_length
             _UpperCAmelCase     :  Union[str, Any]							      =    label_length

             _UpperCAmelCase     :  Tuple							      =    moving_average
             _UpperCAmelCase     :  Dict							      =    autocorrelation_factor




      def      _A	(       self						:				Dict       ):
             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      _A	(       self						:				List[Any]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    config.context_length + max(config.lags_sequence       )

             _UpperCAmelCase     :  Any							      =    ids_tensor([self.batch_size, 1]						,					config.cardinality[0]       )
             _UpperCAmelCase     :  int							      =    floats_tensor([self.batch_size, _past_length, config.num_time_features]       )
             _UpperCAmelCase     :  Optional[int]							      =    floats_tensor([self.batch_size, _past_length]       )
             _UpperCAmelCase     :  Any							      =    floats_tensor([self.batch_size, _past_length]       ) > 0.5

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

             _UpperCAmelCase     :  Optional[int]							      =    {
                 "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      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Tuple							      =    self.get_config()
             _UpperCAmelCase     :  List[str]							      =    self.prepare_autoformer_inputs_dict(A       )
             return config, inputs_dict




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Any							      =    self.prepare_config_and_inputs()
             return config, inputs_dict





      def      _A	(       self						:				Any						,					A						:				Dict						,					A						:				List[Any]       ):
             _UpperCAmelCase     :  int							      =    AutoformerModel(config=A       ).to(A       ).eval()
             _UpperCAmelCase     :  Any							      =    model(**A       )

             _UpperCAmelCase     :  Union[str, Any]							      =    outputs.encoder_last_hidden_state
             _UpperCAmelCase     :  List[Any]							      =    outputs.last_hidden_state

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _UpperCAmelCase     :  Optional[Any]							      =    model.get_encoder()
                    encoder.save_pretrained(A       )
                    _UpperCAmelCase     :  Any							      =    AutoformerEncoder.from_pretrained(A       ).to(A       )

             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    model.create_network_inputs(**A       )
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]       )

             _UpperCAmelCase     :  Tuple							      =    torch.cat(
                 (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])						,					dim=-1						,					)
             _UpperCAmelCase     :  Tuple							      =    encoder(inputs_embeds=A       )[0]
             self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3       )

             _UpperCAmelCase     :  Optional[int]							      =    (
                 torch.mean(transformer_inputs[:, : config.context_length, ...]						,					dim=1       )
                 .unsqueeze(1       )
                 .repeat(1						,					config.prediction_length						,					1       )
             )
             _UpperCAmelCase     :  int							      =    torch.zeros(
                 [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]						,					device=enc_input.device						,					)

             _UpperCAmelCase     :  Optional[int]							      =    torch.cat(
                 (
                     torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)						,					dim=1       ),
                     feature[:, config.context_length - config.label_length :, ...],
                 )						,					dim=-1						,					)
             _UpperCAmelCase     :  List[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:
                    _UpperCAmelCase     :  Union[str, Any]							      =    model.get_decoder()
                    decoder.save_pretrained(A       )
                    _UpperCAmelCase     :  List[Any]							      =    AutoformerDecoder.from_pretrained(A       ).to(A       )

             _UpperCAmelCase     :  Tuple							      =    decoder(
                 trend=A						,					inputs_embeds=A						,					encoder_hidden_states=A						,					)[0]

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

@require_torch
class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
      __UpperCamelCase:  Optional[int]						=						(AutoformerForPrediction,) if is_torch_available() else ()
      __UpperCamelCase:  Tuple						=						{"feature-extraction": AutoformerModel} if is_torch_available() else {}
      __UpperCamelCase:  Optional[int]						=						False
      __UpperCamelCase:  Optional[int]						=						False
      __UpperCamelCase:  Any						=						False
      __UpperCamelCase:  Dict						=						False
      __UpperCamelCase:  Any						=						False
      __UpperCamelCase:  Optional[Any]						=						False




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    AutoformerModelTester(self       )
             _UpperCAmelCase     :  Optional[int]							      =    ConfigTester(self						,					config_class=A						,					has_text_modality=A       )




      def      _A	(       self						:				Any       ):
             self.config_tester.run_common_tests()




      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    _UpperCAmelCase     :  int							      =    model_class(A       )

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(A       )
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    model_class.from_pretrained(A						,					output_loading_info=A       )
                    self.assertEqual(info["missing_keys"]						,					[]       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_encoder_decoder_model_standalone(*A       )




      @unittest.skip(reason="Model has no tokens embeddings"       )
      def      _A	(       self						:				List[Any]       ):
             pass




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Tuple							      =    inspect.signature(getattr(A						,					"forward"       )       )
             # The main input is the name of the argument after `self`
             _UpperCAmelCase     :  Union[str, Any]							      =    list(model_signature.parameters.keys()       )[1]
             self.assertEqual(AutoformerModel.main_input_name						,					A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    self.model_tester.prepare_config_and_inputs_for_common()

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

                    _UpperCAmelCase     :  int							      =    [
                        "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(A       )]						,					A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase     :  int							      =    True

             _UpperCAmelCase     :  List[str]							      =    getattr(self.model_tester						,					"seq_length"						,					A       )
             _UpperCAmelCase     :  Optional[Any]							      =    getattr(self.model_tester						,					"decoder_seq_length"						,					A       )
             _UpperCAmelCase     :  Optional[int]							      =    getattr(self.model_tester						,					"encoder_seq_length"						,					A       )
             _UpperCAmelCase     :  Optional[Any]							      =    getattr(self.model_tester						,					"d_model"						,					A       )
             _UpperCAmelCase     :  Optional[Any]							      =    getattr(self.model_tester						,					"num_attention_heads"						,					A       )
             _UpperCAmelCase     :  Tuple							      =    d_model // num_attention_heads

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

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

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]       )						,					[self.model_tester.num_attention_heads, encoder_seq_length, dim]						,					)
                    _UpperCAmelCase     :  Tuple							      =    len(A       )

                    _UpperCAmelCase     :  Optional[int]							      =    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(A						,					A       )

                    # decoder attentions
                    _UpperCAmelCase     :  Optional[int]							      =    outputs.decoder_attentions
                    self.assertIsInstance(A						,					(list, tuple)       )
                    self.assertEqual(len(A       )						,					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
                    _UpperCAmelCase     :  List[str]							      =    outputs.cross_attentions
                    self.assertIsInstance(A						,					(list, tuple)       )
                    self.assertEqual(len(A       )						,					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
             _UpperCAmelCase     :  str							      =    True
             _UpperCAmelCase     :  int							      =    True
             _UpperCAmelCase     :  List[Any]							      =    model_class(A       )
             model.to(A       )
             model.eval()
             with torch.no_grad():
                    _UpperCAmelCase     :  Union[str, Any]							      =    model(**self._prepare_for_class(A						,					A       )       )

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

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

             self.assertEqual(len(A       )						,					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      _A	(       self						:				List[str]       ):
             super().test_retain_grad_hidden_states_attentions()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int="train-batch.pt"    )    ->    Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  Dict							      =    hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch"			,	filename=UpperCAmelCase_			,	repo_type="dataset"    )
       _UpperCAmelCase     :  Optional[Any]							      =    torch.load(UpperCAmelCase_			,	map_location=UpperCAmelCase_    )
       return batch


@require_torch
@slow
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  int							      =    AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly"       ).to(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_batch()

             with torch.no_grad():
                    _UpperCAmelCase     :  Optional[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"]						,					future_values=batch["future_values"]						,					future_time_features=batch["future_time_features"]						,					)[0]

             _UpperCAmelCase     :  Dict							      =    torch.Size(
                 (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)       )
             self.assertEqual(output.shape						,					A       )

             _UpperCAmelCase     :  Tuple							      =    torch.tensor(
                 [[0.3_593, -1.3_398, 0.6_330], [0.2_279, 1.5_396, -0.1_792], [0.0_450, 1.3_225, -0.2_335]]						,					device=A       )
             self.assertTrue(torch.allclose(output[0, :3, :3]						,					A						,					atol=A       )       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  List[str]							      =    AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"       ).to(A       )
             _UpperCAmelCase     :  Tuple							      =    prepare_batch("val-batch.pt"       )
             with torch.no_grad():
                    _UpperCAmelCase     :  Optional[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
             _UpperCAmelCase     :  Optional[int]							      =    torch.Size((64, model.config.context_length, model.config.d_model)       )
             self.assertEqual(output.shape						,					A       )

             _UpperCAmelCase     :  int							      =    torch.tensor(
                 [[-0.0_734, -0.9_036, 0.8_358], [4.7_186, 2.4_113, 1.9_581], [1.7_953, 2.3_558, 1.2_970]]						,					device=A       )
             self.assertTrue(torch.allclose(output[0, :3, :3]						,					A						,					atol=A       )       )





      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  List[str]							      =    AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly"       ).to(A       )
             _UpperCAmelCase     :  Dict							      =    prepare_batch("val-batch.pt"       )
             with torch.no_grad():
                    _UpperCAmelCase     :  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"]						,					)
             _UpperCAmelCase     :  str							      =    torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)       )
             self.assertEqual(outputs.sequences.shape						,					A       )

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





'''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_  :				int						= logging.get_logger(__name__)





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





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_					,				UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      ):
 lowerCamelCase_      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_      =  Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_		).raw		)
 return im





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  ViTConfig()
 lowerCamelCase_      =  False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  True
  lowerCamelCase_      =  int(vit_name[-12:-10]		)
  lowerCamelCase_      =  int(vit_name[-9:-6]		)
 else:
  lowerCamelCase_      =  1000
  lowerCamelCase_      =  "huggingface/label-files"
  lowerCamelCase_      =  "imagenet-1k-id2label.json"
  lowerCamelCase_      =  json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type="dataset"		)					,				"r"		)		)
  lowerCamelCase_      =  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  lowerCamelCase_      =  idalabel
  lowerCamelCase_      =  {v: k for k, v in idalabel.items()}
  lowerCamelCase_      =  int(vit_name[-6:-4]		)
  lowerCamelCase_      =  int(vit_name[-3:]		)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("tiny"		):
   lowerCamelCase_      =  192
   lowerCamelCase_      =  768
   lowerCamelCase_      =  12
   lowerCamelCase_      =  3
  elif vit_name[9:].startswith("small"		):
   lowerCamelCase_      =  384
   lowerCamelCase_      =  1536
   lowerCamelCase_      =  12
   lowerCamelCase_      =  6
  else:
   pass
 else:
  if vit_name[4:].startswith("small"		):
   lowerCamelCase_      =  768
   lowerCamelCase_      =  2304
   lowerCamelCase_      =  8
   lowerCamelCase_      =  8
  elif vit_name[4:].startswith("base"		):
   pass
  elif vit_name[4:].startswith("large"		):
   lowerCamelCase_      =  1024
   lowerCamelCase_      =  4096
   lowerCamelCase_      =  24
   lowerCamelCase_      =  16
  elif vit_name[4:].startswith("huge"		):
   lowerCamelCase_      =  1280
   lowerCamelCase_      =  5120
   lowerCamelCase_      =  32
   lowerCamelCase_      =  16

    # load original model from timm
 lowerCamelCase_      =  timm.create_model(UpperCAmelCase_					,				pretrained=UpperCAmelCase_		)
 timm_model.eval()

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  ViTModel(UpperCAmelCase_		).eval()
 else:
  lowerCamelCase_      =  ViTForImageClassification(UpperCAmelCase_		).eval()
 model.load_state_dict(UpperCAmelCase_		)

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

 if base_model:
  lowerCamelCase_      =  timm_model.forward_features(UpperCAmelCase_		)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.pooler_output					,				atol=1E-3		)
 else:
  lowerCamelCase_      =  timm_model(UpperCAmelCase_		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.logits					,				atol=1E-3		)

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


if __name__ == "__main__":
     a_  :				Union[str, Any]						= 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_  :				List[str]						= parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



