




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


_snake_case										=       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 						lowercase							:

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

           _A							:     Any			=						context_length
           _A							:     str			=						prediction_length + label_length
           _A							:     int			=						label_length

           _A							:     List[str]			=						moving_average
           _A							:     Dict			=						autocorrelation_factor

     def 					a__	(  self				)						->							List[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 					a__	(  self		,     _a				)						->							Optional[int]:
           _A							:     int			=						config.context_length + max(config.lags_sequence				)

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

           # decoder inputs
           _A							:     Union[str, Any]			=						floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]				)
           _A							:     Union[str, Any]			=						floats_tensor([self.batch_size, config.prediction_length]				)

           _A							:     str			=						{
               """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				)						->							Tuple:
           _A							:     List[Any]			=						self.get_config()
           _A							:     int			=						self.prepare_autoformer_inputs_dict(_a				)
           return config, inputs_dict

     def 					a__	(  self				)						->							Optional[int]:
           _A			,   _A							:     Tuple			=						self.prepare_config_and_inputs()
           return config, inputs_dict



     def 					a__	(  self		,     _a		,     _a				)						->							Optional[Any]:
           _A							:     Dict			=						AutoformerModel(config=_a				).to(_a				).eval()
           _A							:     int			=						model(**_a				)

           _A							:     str			=						outputs.encoder_last_hidden_state
           _A							:     Optional[Any]			=						outputs.last_hidden_state

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

           _A			,   _A			,   _A			,   _A			,   _A							:     Optional[int]			=						model.create_network_inputs(**_a				)
           _A			,   _A							:     str			=						model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]				)

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

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

           _A							:     Tuple			=						torch.cat(
               (
                   torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)		,     dim=1				),
                   feature[:, config.context_length - config.label_length :, ...],
               )		,     dim=-1		,     )
           _A							:     int			=						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:
                 _A							:     Tuple			=						model.get_decoder()
                 decoder.save_pretrained(_a				)
                 _A							:     Tuple			=						AutoformerDecoder.from_pretrained(_a				).to(_a				)

           _A							:     List[Any]			=						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 						lowercase							(   UpperCamelCase__,UpperCamelCase__,unittest.TestCase		):
     _a							 =						(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
     _a							 =						(AutoformerForPrediction,) if is_torch_available() else ()
     _a							 =						{"feature-extraction": AutoformerModel} if is_torch_available() else {}
     _a							 =						False
     _a							 =						False
     _a							 =						False
     _a							 =						False
     _a							 =						False
     _a							 =						False

     def 					a__	(  self				)						->							Dict:
           _A							:     Optional[int]			=						AutoformerModelTester(self				)
           _A							:     Union[str, Any]			=						ConfigTester(self		,     config_class=_a		,     has_text_modality=_a				)

     def 					a__	(  self				)						->							int:
           self.config_tester.run_common_tests()

     def 					a__	(  self				)						->							Optional[int]:
           _A			,   _A							:     Any			=						self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 _A							:     Dict			=						model_class(_a				)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(_a				)
                       _A			,   _A							:     Any			=						model_class.from_pretrained(_a		,     output_loading_info=_a				)
                 self.assertEqual(info["""missing_keys"""]		,     []				)

     def 					a__	(  self				)						->							str:
           _A							:     Dict			=						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				)						->							Optional[int]:
           pass

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

     def 					a__	(  self				)						->							List[Any]:
           _A			,   _A							:     str			=						self.model_tester.prepare_config_and_inputs_for_common()

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

                 _A							:     Tuple			=						[
                     """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				)						->							Optional[Any]:
           _A			,   _A							:     Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_common()
           _A							:     Any			=						True

           _A							:     str			=						getattr(self.model_tester		,     """seq_length"""		,     _a				)
           _A							:     Dict			=						getattr(self.model_tester		,     """decoder_seq_length"""		,     _a				)
           _A							:     str			=						getattr(self.model_tester		,     """encoder_seq_length"""		,     _a				)
           _A							:     List[Any]			=						getattr(self.model_tester		,     """d_model"""		,     _a				)
           _A							:     Optional[int]			=						getattr(self.model_tester		,     """num_attention_heads"""		,     _a				)
           _A							:     List[str]			=						d_model // num_attention_heads

           for model_class in self.all_model_classes:
                 _A							:     Optional[Any]			=						True
                 _A							:     List[str]			=						False
                 _A							:     Optional[int]			=						True
                 _A							:     Union[str, Any]			=						model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 with torch.no_grad():
                       _A							:     List[Any]			=						model(**self._prepare_for_class(_a		,     _a				)				)
                 _A							:     List[str]			=						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"]
                 _A							:     Dict			=						True
                 _A							:     List[Any]			=						model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 with torch.no_grad():
                       _A							:     int			=						model(**self._prepare_for_class(_a		,     _a				)				)
                 _A							:     Tuple			=						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]		,     )
                 _A							:     List[str]			=						len(_a				)

                 _A							:     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
                 _A							:     Dict			=						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
                 _A							:     Optional[Any]			=						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
           _A							:     Dict			=						True
           _A							:     Any			=						True
           _A							:     str			=						model_class(_a				)
           model.to(_a				)
           model.eval()
           with torch.no_grad():
                 _A							:     List[str]			=						model(**self._prepare_for_class(_a		,     _a				)				)

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

           _A							:     Optional[Any]			=						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				)						->							int:
           super().test_retain_grad_hidden_states_attentions()







def 				lowerCAmelCase_	(     snake_case_="train-batch.pt"     ):
      _A							:     Optional[int]			=						hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch""",filename=snake_case_,repo_type="""dataset"""     )
      _A							:     List[str]			=						torch.load(snake_case_,map_location=snake_case_     )
      return batch







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

     def 					a__	(  self				)						->							Any:
           _A							:     Optional[int]			=						AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly"""				).to(_a				)
           _A							:     Any			=						prepare_batch()

           with torch.no_grad():
                 _A							:     Union[str, Any]			=						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]

           _A							:     List[Any]			=						torch.Size(
               (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)				)
           self.assertEqual(output.shape		,     _a				)

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

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

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



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

           _A							:     int			=						torch.tensor([3130.6763, 4056.5293, 7053.0786]		,     device=_a				)
           _A							:     Dict			=						outputs.sequences.mean(dim=1				)
           self.assertTrue(torch.allclose(mean_prediction[0, -3:]		,     _a		,     rtol=1e-1				)				)







import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)







def 				lowerCAmelCase_	(     snake_case_     ):
      print("""Loading config file..."""     )

      def flatten_yaml_as_dict(snake_case_,snake_case_="",snake_case_="."     ):
            _A							:     Union[str, Any]			=						[]
            for k, v in d.items():
                  _A							:     Optional[int]			=						parent_key + sep + k if parent_key else k
                  if isinstance(snake_case_,collections.abc.MutableMapping     ):
                        items.extend(flatten_yaml_as_dict(snake_case_,snake_case_,sep=snake_case_     ).items()     )
                  else:
                        items.append((new_key, v)     )
            return dict(snake_case_     )

      _A							:     List[Any]			=						argparse.Namespace()
      with open(snake_case_,"""r"""     ) as yaml_file:
            try:
                  _A							:     List[Any]			=						yaml.load(snake_case_,Loader=yaml.FullLoader     )

                  _A							:     Optional[int]			=						flatten_yaml_as_dict(snake_case_     )
                  for k, v in flat_cfg.items():
                        setattr(snake_case_,snake_case_,snake_case_     )
            except yaml.YAMLError as exc:
                  logger.error("""Error while loading config file: {}. Error message: {}""".format(snake_case_,str(snake_case_     )     )     )
      return config







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _A							:     Optional[Any]			=						MobileViTVaConfig()

      _A							:     Tuple			=						False

      # dataset
      if task_name.startswith("""imagenet1k_"""     ):
            _A							:     Dict			=						1000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     int			=						384
            else:
                  _A							:     int			=						256
            _A							:     List[str]			=						"""imagenet-1k-id2label.json"""
      elif task_name.startswith("""imagenet21k_to_1k_"""     ):
            _A							:     Union[str, Any]			=						21000
            if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
                  _A							:     str			=						384
            else:
                  _A							:     List[Any]			=						256
            _A							:     List[str]			=						"""imagenet-22k-id2label.json"""
      elif task_name.startswith("""ade20k_"""     ):
            _A							:     int			=						151
            _A							:     int			=						512
            _A							:     Optional[int]			=						"""ade20k-id2label.json"""
            _A							:     Any			=						True
      elif task_name.startswith("""voc_"""     ):
            _A							:     List[Any]			=						21
            _A							:     Dict			=						512
            _A							:     Dict			=						"""pascal-voc-id2label.json"""
            _A							:     int			=						True

      # orig_config
      _A							:     Any			=						load_orig_config_file(snake_case_     )
      assert getattr(snake_case_,"""model.classification.name""",-1     ) == "mobilevit_v2", "Invalid model"
      _A							:     List[Any]			=						getattr(snake_case_,"""model.classification.mitv2.width_multiplier""",1.0     )
      assert (
          getattr(snake_case_,"""model.classification.mitv2.attn_norm_layer""",-1     ) == "layer_norm_2d"
      ), "Norm layers other than layer_norm_2d is not supported"
      _A							:     str			=						getattr(snake_case_,"""model.classification.activation.name""","""swish"""     )
      # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

      if is_segmentation_model:
            _A							:     Optional[int]			=						getattr(snake_case_,"""model.segmentation.output_stride""",16     )
            if "_deeplabv3" in task_name:
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_rates""",[12, 24, 36]     )
                  _A							:     int			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_out_channels""",512     )
                  _A							:     str			=						getattr(snake_case_,"""model.segmentation.deeplabv3.aspp_dropout""",0.1     )

    # id2label
      _A							:     List[Any]			=						"""huggingface/label-files"""
      _A							:     List[Any]			=						json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""     ),"""r"""     )     )
      _A							:     str			=						{int(snake_case_     ): v for k, v in idalabel.items()}
      _A							:     str			=						idalabel
      _A							:     Dict			=						{v: k for k, v in idalabel.items()}

      return config







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Any			=						dct.pop(snake_case_     )
      _A							:     Union[str, Any]			=						val







def 				lowerCAmelCase_	(     snake_case_,snake_case_=False     ):
      if base_model:
            _A							:     Optional[int]			=						""""""
      else:
            _A							:     Dict			=						"""mobilevitv2."""

      _A							:     int			=						[]
      for k in state_dict.keys():
            if k[:8] == "encoder.":
                  _A							:     Any			=						k[8:]
            else:
                  _A							:     List[str]			=						k

            if ".block." in k:
                  _A							:     Any			=						k_new.replace(""".block.""","""."""     )
            if ".conv." in k:
                  _A							:     List[Any]			=						k_new.replace(""".conv.""",""".convolution."""     )
            if ".norm." in k:
                  _A							:     Any			=						k_new.replace(""".norm.""",""".normalization."""     )

            if "conv_1." in k:
                  _A							:     int			=						k_new.replace("""conv_1.""",f'''{model_prefix}conv_stem.'''     )
            for i in [1, 2]:
                  if f'''layer_{i}.''' in k:
                        _A							:     Optional[Any]			=						k_new.replace(f'''layer_{i}.''',f'''{model_prefix}encoder.layer.{i-1}.layer.'''     )
            if ".exp_1x1." in k:
                  _A							:     Tuple			=						k_new.replace(""".exp_1x1.""",""".expand_1x1."""     )
            if ".red_1x1." in k:
                  _A							:     Optional[int]			=						k_new.replace(""".red_1x1.""",""".reduce_1x1."""     )

            for i in [3, 4, 5]:
                  if f'''layer_{i}.0.''' in k:
                        _A							:     Optional[int]			=						k_new.replace(f'''layer_{i}.0.''',f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''     )
                  if f'''layer_{i}.1.local_rep.0.''' in k:
                        _A							:     Union[str, Any]			=						k_new.replace(f'''layer_{i}.1.local_rep.0.''',f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''     )
                  if f'''layer_{i}.1.local_rep.1.''' in k:
                        _A							:     str			=						k_new.replace(f'''layer_{i}.1.local_rep.1.''',f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''     )

            for i in [3, 4, 5]:
                  if i == 3:
                        _A							:     Optional[int]			=						[0, 1]
                  elif i == 4:
                        _A							:     Union[str, Any]			=						[0, 1, 2, 3]
                  elif i == 5:
                        _A							:     Optional[Any]			=						[0, 1, 2]

                  for j in j_in:
                        if f'''layer_{i}.1.global_rep.{j}.''' in k:
                              _A							:     Union[str, Any]			=						k_new.replace(
                                  f'''layer_{i}.1.global_rep.{j}.''',f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''     )
                  if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
                        _A							:     List[str]			=						k_new.replace(
                            f'''layer_{i}.1.global_rep.{j+1}.''',f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''     )

                  if f'''layer_{i}.1.conv_proj.''' in k:
                        _A							:     Optional[Any]			=						k_new.replace(f'''layer_{i}.1.conv_proj.''',f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''     )

            if "pre_norm_attn.0." in k:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_attn.0.""","""layernorm_before."""     )
            if "pre_norm_attn.1." in k:
                  _A							:     str			=						k_new.replace("""pre_norm_attn.1.""","""attention."""     )
            if "pre_norm_ffn.0." in k:
                  _A							:     Optional[Any]			=						k_new.replace("""pre_norm_ffn.0.""","""layernorm_after."""     )
            if "pre_norm_ffn.1." in k:
                  _A							:     Dict			=						k_new.replace("""pre_norm_ffn.1.""","""ffn.conv1."""     )
            if "pre_norm_ffn.3." in k:
                  _A							:     List[str]			=						k_new.replace("""pre_norm_ffn.3.""","""ffn.conv2."""     )

            if "classifier.1." in k:
                  _A							:     List[str]			=						k_new.replace("""classifier.1.""","""classifier."""     )

            if "seg_head." in k:
                  _A							:     List[Any]			=						k_new.replace("""seg_head.""","""segmentation_head."""     )
            if ".aspp_layer." in k:
                  _A							:     List[Any]			=						k_new.replace(""".aspp_layer.""","""."""     )
            if ".aspp_pool." in k:
                  _A							:     Optional[Any]			=						k_new.replace(""".aspp_pool.""","""."""     )

            rename_keys.append((k, k_new)     )
      return rename_keys







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Tuple			=						[]
      for k in state_dict.keys():
            if k.startswith("""seg_head.aux_head."""     ):
                  keys_to_ignore.append(snake_case_     )
      for k in keys_to_ignore:
            state_dict.pop(snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Dict			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
      _A							:     List[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_     ):
      _A							:     List[Any]			=						get_mobilevitva_config(snake_case_,snake_case_     )

      # load original state_dict
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )

      # load huggingface model
      if task_name.startswith("""ade20k_"""     ) or task_name.startswith("""voc_"""     ):
            _A							:     Optional[Any]			=						MobileViTVaForSemanticSegmentation(snake_case_     ).eval()
            _A							:     str			=						False
      else:
            _A							:     int			=						MobileViTVaForImageClassification(snake_case_     ).eval()
            _A							:     List[Any]			=						False

      # remove and rename some keys of load the original model
      _A							:     List[Any]			=						checkpoint
      remove_unused_keys(snake_case_     )
      _A							:     Optional[Any]			=						create_rename_keys(snake_case_,base_model=snake_case_     )
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(snake_case_,snake_case_,snake_case_     )

      # load modified state_dict
      model.load_state_dict(snake_case_     )

      # Check outputs on an image, prepared by MobileViTImageProcessor
      _A							:     str			=						MobileViTImageProcessor(crop_size=config.image_size,size=config.image_size + 32     )
      _A							:     List[Any]			=						image_processor(images=prepare_img(),return_tensors="""pt"""     )
      _A							:     Optional[Any]			=						model(**snake_case_     )

      # verify classification model
      if task_name.startswith("""imagenet"""     ):
            _A							:     List[Any]			=						outputs.logits
            _A							:     Optional[int]			=						logits.argmax(-1     ).item()
            print("""Predicted class:""",model.config.idalabel[predicted_class_idx]     )
            if task_name.startswith("""imagenet1k_256"""     ) and config.width_multiplier == 1.0:
                  # expected_logits for base variant
                  _A							:     int			=						torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]     )
                  assert torch.allclose(logits[0, :3],snake_case_,atol=1e-4     )

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


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--task",
      default="imagenet1k_256",
      type=str,
      help=(
          "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
          "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
      ),
      choices=[
          "imagenet1k_256",
          "imagenet1k_384",
          "imagenet21k_to_1k_256",
          "imagenet21k_to_1k_384",
          "ade20k_deeplabv3",
          "voc_deeplabv3",
      ],
  )

  parser.add_argument(
      "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )

  _snake_case										=       parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )

