


'''simple docstring'''






import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
  import torch

  from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  List[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Optional[int]=13   ,						lowerCamelCase   :  Dict=7   ,						lowerCamelCase   :  List[str]=True   ,						lowerCamelCase   :  Optional[Any]=True   ,						lowerCamelCase   :  Union[str, Any]=False   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  Optional[Any]=99   ,						lowerCamelCase   :  Tuple=32   ,						lowerCamelCase   :  Union[str, Any]=5   ,						lowerCamelCase   :  Dict=4   ,						lowerCamelCase   :  Optional[int]=37   ,						lowerCamelCase   :  Any="gelu"   ,						lowerCamelCase   :  List[str]=0.1   ,						lowerCamelCase   :  Union[str, Any]=0.1   ,						lowerCamelCase   :  int=5_12   ,						lowerCamelCase   :  Any=16   ,						lowerCamelCase   :  List[Any]=2   ,						lowerCamelCase   :  str=0.02   ,						lowerCamelCase   :  Dict=3   ,						lowerCamelCase   :  Dict=4   ,						lowerCamelCase   :  int=None   ,						)       ->  Tuple:






      """simple docstring"""




      lowerCAmelCase_      :				List[Any]      =       parent
      lowerCAmelCase_      :				Optional[Any]      =       batch_size
      lowerCAmelCase_      :				Optional[Any]      =       seq_length
      lowerCAmelCase_      :				Any      =       is_training
      lowerCAmelCase_      :				Tuple      =       use_input_mask
      lowerCAmelCase_      :				List[str]      =       use_token_type_ids
      lowerCAmelCase_      :				Any      =       use_labels
      lowerCAmelCase_      :				Dict      =       vocab_size
      lowerCAmelCase_      :				str      =       hidden_size
      lowerCAmelCase_      :				Dict      =       num_hidden_layers
      lowerCAmelCase_      :				int      =       num_attention_heads
      lowerCAmelCase_      :				Tuple      =       intermediate_size
      lowerCAmelCase_      :				Any      =       hidden_act
      lowerCAmelCase_      :				Any      =       hidden_dropout_prob
      lowerCAmelCase_      :				Any      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				Union[str, Any]      =       max_position_embeddings
      lowerCAmelCase_      :				Dict      =       type_vocab_size
      lowerCAmelCase_      :				Tuple      =       type_sequence_label_size
      lowerCAmelCase_      :				Any      =       initializer_range
      lowerCAmelCase_      :				Any      =       num_labels
      lowerCAmelCase_      :				Optional[int]      =       num_choices
      lowerCAmelCase_      :				List[Any]      =       scope

    def __lowercase       (       self   :  List[str]		)       ->  Union[str, Any]:






      """simple docstring"""




      lowerCAmelCase_      :				Tuple      =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

      lowerCAmelCase_      :				Any      =       None
      if self.use_input_mask:
        lowerCAmelCase_      :				Optional[int]      =       random_attention_mask([self.batch_size, self.seq_length]		)

      lowerCAmelCase_      :				List[str]      =       None
      if self.use_token_type_ids:
        lowerCAmelCase_      :				List[str]      =       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size		)

      lowerCAmelCase_      :				Optional[Any]      =       None
      lowerCAmelCase_      :				Tuple      =       None
      lowerCAmelCase_      :				Tuple      =       None
      if self.use_labels:
        lowerCAmelCase_      :				str      =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
        lowerCAmelCase_      :				Union[str, Any]      =       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels		)
        lowerCAmelCase_      :				str      =       ids_tensor([self.batch_size]   ,						self.num_choices		)

      lowerCAmelCase_      :				Any      =       self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def __lowercase       (       self   :  Optional[int]		)       ->  Tuple:






      """simple docstring"""




      return OpenLlamaConfig(
          vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						type_vocab_size=self.type_vocab_size   ,						is_decoder=lowerCamelCase__   ,						initializer_range=self.initializer_range   ,						use_stable_embedding=lowerCamelCase__   ,						)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[Any]		)       ->  Union[str, Any]:






      """simple docstring"""




      lowerCAmelCase_      :				Tuple      =       OpenLlamaModel(config=lowerCamelCase__		)
      model.to(lowerCamelCase__		)
      model.eval()
      lowerCAmelCase_      :				int      =       model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__		)
      lowerCAmelCase_      :				int      =       model(lowerCamelCase__		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[int]   ,						)       ->  List[Any]:






      """simple docstring"""




      lowerCAmelCase_      :				Optional[Any]      =       True
      lowerCAmelCase_      :				Optional[int]      =       OpenLlamaModel(lowerCamelCase__		)
      model.to(lowerCamelCase__		)
      model.eval()
      lowerCAmelCase_      :				Tuple      =       model(
          lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__   ,						encoder_attention_mask=lowerCamelCase__   ,						)
      lowerCAmelCase_      :				Optional[int]      =       model(
          lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__   ,						)
      lowerCAmelCase_      :				int      =       model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Tuple   ,						)       ->  Tuple:






      """simple docstring"""




      lowerCAmelCase_      :				Tuple      =       OpenLlamaForCausalLM(config=lowerCamelCase__		)
      model.to(lowerCamelCase__		)
      model.eval()
      lowerCAmelCase_      :				Optional[Any]      =       model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						labels=lowerCamelCase__		)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)		)

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  List[Any]   ,						)       ->  List[str]:






      """simple docstring"""




      lowerCAmelCase_      :				List[str]      =       True
      lowerCAmelCase_      :				List[Any]      =       True
      lowerCAmelCase_      :				List[str]      =       OpenLlamaForCausalLM(config=lowerCamelCase__		)
      model.to(lowerCamelCase__		)
      model.eval()

      # first forward pass
      lowerCAmelCase_      :				int      =       model(
          lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__   ,						encoder_attention_mask=lowerCamelCase__   ,						use_cache=lowerCamelCase__   ,						)
      lowerCAmelCase_      :				Union[str, Any]      =       outputs.past_key_values

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

      # append to next input_ids and
      lowerCAmelCase_      :				Optional[int]      =       torch.cat([input_ids, next_tokens]   ,						dim=-1		)
      lowerCAmelCase_      :				int      =       torch.cat([input_mask, next_mask]   ,						dim=-1		)

      lowerCAmelCase_      :				Optional[Any]      =       model(
          lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__   ,						encoder_attention_mask=lowerCamelCase__   ,						output_hidden_states=lowerCamelCase__   ,						)['''hidden_states'''][0]
      lowerCAmelCase_      :				Union[str, Any]      =       model(
          lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						encoder_hidden_states=lowerCamelCase__   ,						encoder_attention_mask=lowerCamelCase__   ,						past_key_values=lowerCamelCase__   ,						output_hidden_states=lowerCamelCase__   ,						)['''hidden_states'''][0]

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

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

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





    def __lowercase       (       self   :  Dict		)       ->  Any:






      """simple docstring"""




      lowerCAmelCase_      :				Optional[Any]      =       self.prepare_config_and_inputs()
      (
          lowerCAmelCase_			
      )      :				Dict      =       config_and_inputs
      lowerCAmelCase_      :				int      =       {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_torch
class        __snake_case	(       __lowerCAmelCase     ,__lowerCAmelCase     ,__lowerCAmelCase     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    lowercase       			= (OpenLlamaForCausalLM,) if is_torch_available() else ()
    lowercase       			= (
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Tuple		)       ->  Optional[int]:






      """simple docstring"""




      lowerCAmelCase_      :				Dict      =       OpenLlamaModelTester(self		)
      lowerCAmelCase_      :				int      =       ConfigTester(self   ,						config_class=lowerCamelCase__   ,						hidden_size=37		)

    def __lowercase       (       self   :  Tuple		)       ->  Tuple:






      """simple docstring"""




      self.config_tester.run_common_tests()

    def __lowercase       (       self   :  Any		)       ->  str:






      """simple docstring"""




      lowerCAmelCase_      :				List[Any]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__		)

    def __lowercase       (       self   :  str		)       ->  Optional[Any]:






      """simple docstring"""




      lowerCAmelCase_      :				Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        lowerCAmelCase_      :				List[Any]      =       type
        self.model_tester.create_and_check_model(*lowerCamelCase__		)

    def __lowercase       (       self   :  Tuple		)       ->  str:






      """simple docstring"""




      lowerCAmelCase_      :				Dict      =       self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase_      :				str      =       3
      lowerCAmelCase_      :				Optional[Any]      =       input_dict['''input_ids''']
      lowerCAmelCase_      :				str      =       input_ids.ne(1		).to(lowerCamelCase__		)
      lowerCAmelCase_      :				int      =       ids_tensor([self.model_tester.batch_size]   ,						self.model_tester.type_sequence_label_size		)
      lowerCAmelCase_      :				str      =       OpenLlamaForSequenceClassification(lowerCamelCase__		)
      model.to(lowerCamelCase__		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						labels=lowerCamelCase__		)
      self.assertEqual(result.logits.shape   ,						(self.model_tester.batch_size, self.model_tester.num_labels)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:






      """simple docstring"""




      lowerCAmelCase_      :				Dict      =       self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase_      :				str      =       3
      lowerCAmelCase_      :				Union[str, Any]      =       '''single_label_classification'''
      lowerCAmelCase_      :				Any      =       input_dict['''input_ids''']
      lowerCAmelCase_      :				str      =       input_ids.ne(1		).to(lowerCamelCase__		)
      lowerCAmelCase_      :				Dict      =       ids_tensor([self.model_tester.batch_size]   ,						self.model_tester.type_sequence_label_size		)
      lowerCAmelCase_      :				Dict      =       OpenLlamaForSequenceClassification(lowerCamelCase__		)
      model.to(lowerCamelCase__		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						labels=lowerCamelCase__		)
      self.assertEqual(result.logits.shape   ,						(self.model_tester.batch_size, self.model_tester.num_labels)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:






      """simple docstring"""




      lowerCAmelCase_      :				Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase_      :				Optional[Any]      =       3
      lowerCAmelCase_      :				Any      =       '''multi_label_classification'''
      lowerCAmelCase_      :				Union[str, Any]      =       input_dict['''input_ids''']
      lowerCAmelCase_      :				List[Any]      =       input_ids.ne(1		).to(lowerCamelCase__		)
      lowerCAmelCase_      :				str      =       ids_tensor(
          [self.model_tester.batch_size, config.num_labels]   ,						self.model_tester.type_sequence_label_size		).to(torch.float		)
      lowerCAmelCase_      :				Union[str, Any]      =       OpenLlamaForSequenceClassification(lowerCamelCase__		)
      model.to(lowerCamelCase__		)
      model.eval()
      lowerCAmelCase_      :				str      =       model(lowerCamelCase__   ,						attention_mask=lowerCamelCase__   ,						labels=lowerCamelCase__		)
      self.assertEqual(result.logits.shape   ,						(self.model_tester.batch_size, self.model_tester.num_labels)		)

    @unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test"""		)
    def __lowercase       (       self   :  Dict		)       ->  int:






      """simple docstring"""




      pass





    @parameterized.expand([("""linear""",), ("""dynamic""",)]		)
    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  Optional[Any]		)       ->  Union[str, Any]:






      """simple docstring"""




      lowerCAmelCase_      :				str      =       self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase_      :				Union[str, Any]      =       ids_tensor([1, 10]   ,						config.vocab_size		)
      lowerCAmelCase_      :				str      =       ids_tensor([1, int(config.max_position_embeddings * 1.5		)]   ,						config.vocab_size		)

      set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
      lowerCAmelCase_      :				Optional[Any]      =       OpenLlamaModel(lowerCamelCase__		)
      original_model.to(lowerCamelCase__		)
      original_model.eval()
      lowerCAmelCase_      :				List[str]      =       original_model(lowerCamelCase__		).last_hidden_state
      lowerCAmelCase_      :				Optional[Any]      =       original_model(lowerCamelCase__		).last_hidden_state

      set_seed(42		)  # Fixed seed at init time so the two models get the same random weights
      lowerCAmelCase_      :				List[Any]      =       {'''type''': scaling_type, '''factor''': 10.0}
      lowerCAmelCase_      :				List[str]      =       OpenLlamaModel(lowerCamelCase__		)
      scaled_model.to(lowerCamelCase__		)
      scaled_model.eval()
      lowerCAmelCase_      :				Any      =       scaled_model(lowerCamelCase__		).last_hidden_state
      lowerCAmelCase_      :				Tuple      =       scaled_model(lowerCamelCase__		).last_hidden_state

      # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
      # maximum sequence length, so the outputs for the short input should match.
      if scaling_type == "dynamic":
        self.assertTrue(torch.allclose(lowerCamelCase__   ,						lowerCamelCase__   ,						atol=1E-5		)		)
      else:
        self.assertFalse(torch.allclose(lowerCamelCase__   ,						lowerCamelCase__   ,						atol=1E-5		)		)

      # The output should be different for long inputs
      self.assertFalse(torch.allclose(lowerCamelCase__   ,						lowerCamelCase__   ,						atol=1E-5		)		)







'''simple docstring'''






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







def     UpperCamelCase_      (   A__ :       List[str] ):
  '''simple docstring'''





  print("""Loading config file...""" )

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

  lowerCAmelCase_      :				Optional[int]      =       argparse.Namespace()
  with open(A__							,   """r""" ) as yaml_file:
    try:
      lowerCAmelCase_      :				Any      =       yaml.load(A__							,   Loader=yaml.FullLoader )

      lowerCAmelCase_      :				Any      =       flatten_yaml_as_dict(A__ )
      for k, v in flat_cfg.items():
        setattr(A__							,   A__							,   A__ )
    except yaml.YAMLError as exc:
      logger.error("""Error while loading config file: {}. Error message: {}""".format(A__							,   str(A__ ) ) )
  return config







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





  lowerCAmelCase_      :				List[str]      =       MobileViTVaConfig()

  lowerCAmelCase_      :				Any      =       False

  # dataset
  if task_name.startswith("""imagenet1k_""" ):
    lowerCAmelCase_      :				Tuple      =       10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				List[str]      =       3_84
    else:
      lowerCAmelCase_      :				Optional[int]      =       2_56
    lowerCAmelCase_      :				int      =       """imagenet-1k-id2label.json"""
  elif task_name.startswith("""imagenet21k_to_1k_""" ):
    lowerCAmelCase_      :				int      =       2_10_00
    if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
      lowerCAmelCase_      :				int      =       3_84
    else:
      lowerCAmelCase_      :				int      =       2_56
    lowerCAmelCase_      :				Optional[Any]      =       """imagenet-22k-id2label.json"""
  elif task_name.startswith("""ade20k_""" ):
    lowerCAmelCase_      :				Dict      =       1_51
    lowerCAmelCase_      :				Any      =       5_12
    lowerCAmelCase_      :				int      =       """ade20k-id2label.json"""
    lowerCAmelCase_      :				Union[str, Any]      =       True
  elif task_name.startswith("""voc_""" ):
    lowerCAmelCase_      :				Any      =       21
    lowerCAmelCase_      :				List[str]      =       5_12
    lowerCAmelCase_      :				Union[str, Any]      =       """pascal-voc-id2label.json"""
    lowerCAmelCase_      :				int      =       True

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

  if is_segmentation_model:
    lowerCAmelCase_      :				List[str]      =       getattr(A__							,   """model.segmentation.output_stride"""							,   16 )
    if "_deeplabv3" in task_name:
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_rates"""							,   [12, 24, 36] )
      lowerCAmelCase_      :				Optional[Any]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_out_channels"""							,   5_12 )
      lowerCAmelCase_      :				Optional[int]      =       getattr(A__							,   """model.segmentation.deeplabv3.aspp_dropout"""							,   0.1 )

    # id2label
  lowerCAmelCase_      :				Any      =       """huggingface/label-files"""
  lowerCAmelCase_      :				Any      =       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[int]      =       idalabel
  lowerCAmelCase_      :				Tuple      =       {v: k for k, v in idalabel.items()}

  return config







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Dict							,   A__ :       Tuple ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       dct.pop(A__ )
  lowerCAmelCase_      :				Optional[int]      =       val







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





  if base_model:
    lowerCAmelCase_      :				List[Any]      =       """"""
  else:
    lowerCAmelCase_      :				Any      =       """mobilevitv2."""

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

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

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

    for i in [3, 4, 5]:
      if f'layer_{i}.0.' in k:
        lowerCAmelCase_      :				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:
        lowerCAmelCase_      :				str      =       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:
        lowerCAmelCase_      :				Optional[Any]      =       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:
        lowerCAmelCase_      :				Optional[Any]      =       [0, 1]
      elif i == 4:
        lowerCAmelCase_      :				Any      =       [0, 1, 2, 3]
      elif i == 5:
        lowerCAmelCase_      :				Any      =       [0, 1, 2]

      for j in j_in:
        if f'layer_{i}.1.global_rep.{j}.' in k:
          lowerCAmelCase_      :				Optional[int]      =       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:
        lowerCAmelCase_      :				Optional[Any]      =       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:
        lowerCAmelCase_      :				str      =       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:
      lowerCAmelCase_      :				Dict      =       k_new.replace("""pre_norm_attn.0."""							,   """layernorm_before.""" )
    if "pre_norm_attn.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""pre_norm_attn.1."""							,   """attention.""" )
    if "pre_norm_ffn.0." in k:
      lowerCAmelCase_      :				Any      =       k_new.replace("""pre_norm_ffn.0."""							,   """layernorm_after.""" )
    if "pre_norm_ffn.1." in k:
      lowerCAmelCase_      :				int      =       k_new.replace("""pre_norm_ffn.1."""							,   """ffn.conv1.""" )
    if "pre_norm_ffn.3." in k:
      lowerCAmelCase_      :				List[Any]      =       k_new.replace("""pre_norm_ffn.3."""							,   """ffn.conv2.""" )

    if "classifier.1." in k:
      lowerCAmelCase_      :				Optional[int]      =       k_new.replace("""classifier.1."""							,   """classifier.""" )

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

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







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





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







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





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







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





  lowerCAmelCase_      :				int      =       get_mobilevitva_config(A__							,   A__ )

  # load original state_dict
  lowerCAmelCase_      :				int      =       torch.load(A__							,   map_location="""cpu""" )

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

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

  # load modified state_dict
  model.load_state_dict(A__ )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  lowerCAmelCase_      :				Dict      =       MobileViTImageProcessor(crop_size=config.image_size							,   size=config.image_size + 32 )
  lowerCAmelCase_      :				Dict      =       image_processor(images=prepare_img()							,   return_tensors="""pt""" )
  lowerCAmelCase_      :				int      =       model(**A__ )

  # verify classification model
  if task_name.startswith("""imagenet""" ):
    lowerCAmelCase_      :				Optional[Any]      =       outputs.logits
    lowerCAmelCase_      :				Tuple      =       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
      lowerCAmelCase_      :				int      =       torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01] )
      assert torch.allclose(logits[0, :3]							,   A__							,   atol=1E-4 )

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


if __name__ == "__main__":
  __A :  Dict    							=				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."
  )

  __A :  Union[str, Any]    							=				parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )



