



'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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




def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						MobileViTConfig()

     # size of the architecture
     if "mobilevit_s" in mobilevit_name:
          _a											=						[144, 192, 240]
          _a											=						[16, 32, 64, 96, 128, 160, 640]
     elif "mobilevit_xs" in mobilevit_name:
          _a											=						[96, 120, 144]
          _a											=						[16, 32, 48, 64, 80, 96, 384]
     elif "mobilevit_xxs" in mobilevit_name:
          _a											=						[64, 80, 96]
          _a											=						[16, 16, 24, 48, 64, 80, 320]
          _a											=						0.05
          _a											=						2.0

     if mobilevit_name.startswith('''deeplabv3_'''):
          _a											=						512
          _a											=						16
          _a											=						21
          _a											=						'''pascal-voc-id2label.json'''
     else:
          _a											=						1_000
          _a											=						'''imagenet-1k-id2label.json'''

     _a											=						'''huggingface/label-files'''
     _a											=						json.load(open(hf_hub_download(__A						,			__A						,			repo_type='''dataset''')						,			'''r'''))
     _a											=						{int(__A): v for k, v in idalabel.items()}
     _a											=						idalabel
     _a											=						{v: k for k, v in idalabel.items()}

     return config




def lowerCAmelCase						(__A						,			__A=False):



     """simple docstring"""



     for i in range(1						,			6):
          if F'''layer_{i}.''' in name:
               _a											=						name.replace(F'''layer_{i}.'''						,			F'''encoder.layer.{i - 1}.''')

     if "conv_1." in name:
          _a											=						name.replace('''conv_1.'''						,			'''conv_stem.''')
     if ".block." in name:
          _a											=						name.replace('''.block.'''						,			'''.''')

     if "exp_1x1" in name:
          _a											=						name.replace('''exp_1x1'''						,			'''expand_1x1''')
     if "red_1x1" in name:
          _a											=						name.replace('''red_1x1'''						,			'''reduce_1x1''')
     if ".local_rep.conv_3x3." in name:
          _a											=						name.replace('''.local_rep.conv_3x3.'''						,			'''.conv_kxk.''')
     if ".local_rep.conv_1x1." in name:
          _a											=						name.replace('''.local_rep.conv_1x1.'''						,			'''.conv_1x1.''')
     if ".norm." in name:
          _a											=						name.replace('''.norm.'''						,			'''.normalization.''')
     if ".conv." in name:
          _a											=						name.replace('''.conv.'''						,			'''.convolution.''')
     if ".conv_proj." in name:
          _a											=						name.replace('''.conv_proj.'''						,			'''.conv_projection.''')

     for i in range(0						,			2):
          for j in range(0						,			4):
               if F'''.{i}.{j}.''' in name:
                    _a											=						name.replace(F'''.{i}.{j}.'''						,			F'''.{i}.layer.{j}.''')

     for i in range(2						,			6):
          for j in range(0						,			4):
               if F'''.{i}.{j}.''' in name:
                    _a											=						name.replace(F'''.{i}.{j}.'''						,			F'''.{i}.''')
                    if "expand_1x1" in name:
                         _a											=						name.replace('''expand_1x1'''						,			'''downsampling_layer.expand_1x1''')
                    if "conv_3x3" in name:
                         _a											=						name.replace('''conv_3x3'''						,			'''downsampling_layer.conv_3x3''')
                    if "reduce_1x1" in name:
                         _a											=						name.replace('''reduce_1x1'''						,			'''downsampling_layer.reduce_1x1''')

     for i in range(2						,			5):
          if F'''.global_rep.{i}.weight''' in name:
               _a											=						name.replace(F'''.global_rep.{i}.weight'''						,			'''.layernorm.weight''')
          if F'''.global_rep.{i}.bias''' in name:
               _a											=						name.replace(F'''.global_rep.{i}.bias'''						,			'''.layernorm.bias''')

     if ".global_rep." in name:
          _a											=						name.replace('''.global_rep.'''						,			'''.transformer.''')
     if ".pre_norm_mha.0." in name:
          _a											=						name.replace('''.pre_norm_mha.0.'''						,			'''.layernorm_before.''')
     if ".pre_norm_mha.1.out_proj." in name:
          _a											=						name.replace('''.pre_norm_mha.1.out_proj.'''						,			'''.attention.output.dense.''')
     if ".pre_norm_ffn.0." in name:
          _a											=						name.replace('''.pre_norm_ffn.0.'''						,			'''.layernorm_after.''')
     if ".pre_norm_ffn.1." in name:
          _a											=						name.replace('''.pre_norm_ffn.1.'''						,			'''.intermediate.dense.''')
     if ".pre_norm_ffn.4." in name:
          _a											=						name.replace('''.pre_norm_ffn.4.'''						,			'''.output.dense.''')
     if ".transformer." in name:
          _a											=						name.replace('''.transformer.'''						,			'''.transformer.layer.''')

     if ".aspp_layer." in name:
          _a											=						name.replace('''.aspp_layer.'''						,			'''.''')
     if ".aspp_pool." in name:
          _a											=						name.replace('''.aspp_pool.'''						,			'''.''')
     if "seg_head." in name:
          _a											=						name.replace('''seg_head.'''						,			'''segmentation_head.''')
     if "segmentation_head.classifier.classifier." in name:
          _a											=						name.replace('''segmentation_head.classifier.classifier.'''						,			'''segmentation_head.classifier.''')

     if "classifier.fc." in name:
          _a											=						name.replace('''classifier.fc.'''						,			'''classifier.''')
     elif (not base_model) and ("segmentation_head." not in name):
          _a											=						'''mobilevit.''' + name

     return name




def lowerCAmelCase						(__A						,			__A						,			__A=False):



     """simple docstring"""



     if base_model:
          _a											=						''''''
     else:
          _a											=						'''mobilevit.'''

     for key in orig_state_dict.copy().keys():
          _a											=						orig_state_dict.pop(__A)

          if key[:8] == "encoder.":
               _a											=						key[8:]

          if "qkv" in key:
               _a											=						key.split('''.''')
               _a											=						int(key_split[0][6:]) - 1
               _a											=						int(key_split[3])
               _a											=						model.get_submodule(F'''{model_prefix}encoder.layer.{layer_num}''')
               _a											=						layer.transformer.layer[transformer_num].attention.attention.all_head_size
               _a											=						(
                   F'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
               )
               if "weight" in key:
                    _a											=						val[:dim, :]
                    _a											=						val[dim : dim * 2, :]
                    _a											=						val[-dim:, :]
               else:
                    _a											=						val[:dim]
                    _a											=						val[dim : dim * 2]
                    _a											=						val[-dim:]
          else:
               _a											=						val

     return orig_state_dict




def lowerCAmelCase						():



     """simple docstring"""



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




@torch.no_grad()
def lowerCAmelCase						(__A						,			__A						,			__A						,			__A=False):



     """simple docstring"""



     _a											=						get_mobilevit_config(__A)

     # load original state_dict
     _a											=						torch.load(__A						,			map_location='''cpu''')

     # load 🤗 model
     if mobilevit_name.startswith('''deeplabv3_'''):
          _a											=						MobileViTForSemanticSegmentation(__A).eval()
     else:
          _a											=						MobileViTForImageClassification(__A).eval()

     _a											=						convert_state_dict(__A						,			__A)
     model.load_state_dict(__A)

     # Check outputs on an image, prepared by MobileViTImageProcessor
     _a											=						MobileViTImageProcessor(crop_size=config.image_size						,			size=config.image_size + 32)
     _a											=						image_processor(images=prepare_img()						,			return_tensors='''pt''')
     _a											=						model(**__A)
     _a											=						outputs.logits

     if mobilevit_name.startswith('''deeplabv3_'''):
          assert logits.shape == (1, 21, 32, 32)

          if mobilevit_name == "deeplabv3_mobilevit_s":
               _a											=						torch.tensor(
                   [
                       [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                       [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                       [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                   ])
          elif mobilevit_name == "deeplabv3_mobilevit_xs":
               _a											=						torch.tensor(
                   [
                       [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                       [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                       [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                   ])
          elif mobilevit_name == "deeplabv3_mobilevit_xxs":
               _a											=						torch.tensor(
                   [
                       [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                       [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                       [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                   ])
          else:
               raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''')

          assert torch.allclose(logits[0, :3, :3, :3]						,			__A						,			atol=1e-4)
     else:
          assert logits.shape == (1, 1_000)

          if mobilevit_name == "mobilevit_s":
               _a											=						torch.tensor([-0.98_66, 0.23_92, -1.12_41])
          elif mobilevit_name == "mobilevit_xs":
               _a											=						torch.tensor([-2.47_61, -0.93_99, -1.95_87])
          elif mobilevit_name == "mobilevit_xxs":
               _a											=						torch.tensor([-1.93_64, -1.23_27, -0.46_53])
          else:
               raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}''')

          assert torch.allclose(logits[0, :3]						,			__A						,			atol=1e-4)

     Path(__A).mkdir(exist_ok=__A)
     print(F'''Saving model {mobilevit_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 push_to_hub:
          _a											=						{
              '''mobilevit_s''': '''mobilevit-small''',
              '''mobilevit_xs''': '''mobilevit-x-small''',
              '''mobilevit_xxs''': '''mobilevit-xx-small''',
              '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
              '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
              '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
          }

          print('''Pushing to the hub...''')
          _a											=						model_mapping[mobilevit_name]
          image_processor.push_to_hub(__A						,			organization='''apple''')
          model.push_to_hub(__A						,			organization='''apple''')


if __name__ == "__main__":
  lowercase_	 =						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--mobilevit_name",
      default="mobilevit_s",
      type=str,
      help=(
          "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
          " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
      ),
  )
  parser.add_argument(
      "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  lowercase_	 =						parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )








'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint






@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						[[1, 2, 4], [1, 2, 3, 4]]
      _a											=						DisjunctiveConstraint(A )
      self.assertTrue(isinstance(dc.token_ids				,     A ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						[[1, 2], [1, 2, 3, 4]]

      with self.assertRaises(A ):
           DisjunctiveConstraint(A )  # fails here



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(3 )
      _a											=						stepped is True and completed is True and reset is False
      self.assertTrue(A )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 3] )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(4 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2, 4] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

      dc.reset()

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 3 )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 2 )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.remaining() == 0 )
      self.assertTrue(dc.current_seq == [1, 2, 5] )



