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





def  _lowerCAmelCase     (  lowerCamelCase__				:				Optional[int]					)	->     Optional[int]:
  _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    _SCREAMING_SNAKE_CASE :					Tuple				     =			[1_4_4, 1_9_2, 2_4_0]
    _SCREAMING_SNAKE_CASE :					Any				     =			[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
  elif "mobilevit_xs" in mobilevit_name:
    _SCREAMING_SNAKE_CASE :					int				     =			[9_6, 1_2_0, 1_4_4]
    _SCREAMING_SNAKE_CASE :					List[str]				     =			[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
  elif "mobilevit_xxs" in mobilevit_name:
    _SCREAMING_SNAKE_CASE :					Dict				     =			[6_4, 8_0, 9_6]
    _SCREAMING_SNAKE_CASE :					Optional[int]				     =			[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
    _SCREAMING_SNAKE_CASE :					Dict				     =			0.05
    _SCREAMING_SNAKE_CASE :					Optional[int]				     =			2.0

  if mobilevit_name.startswith("deeplabv3_"					):
    _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			5_1_2
    _SCREAMING_SNAKE_CASE :					List[Any]				     =			1_6
    _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			2_1
    _SCREAMING_SNAKE_CASE :					Any				     =			"pascal-voc-id2label.json"
  else:
    _SCREAMING_SNAKE_CASE :					str				     =			1_0_0_0
    _SCREAMING_SNAKE_CASE :					Any				     =			"imagenet-1k-id2label.json"

  _SCREAMING_SNAKE_CASE :					Optional[int]				     =			"huggingface/label-files"
  _SCREAMING_SNAKE_CASE :					Any				     =			json.load(open(hf_hub_download(lowerCamelCase__,    lowerCamelCase__,    repo_type="dataset"					),    "r"					)					)
  _SCREAMING_SNAKE_CASE :					Dict				     =			{int(lowerCamelCase__					): v for k, v in idalabel.items()}
  _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			idalabel
  _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			{v: k for k, v in idalabel.items()}

  return config





def  _lowerCAmelCase     (  lowerCamelCase__				:				Optional[int],    lowerCamelCase__				:				str=False					)	->     Dict:
  for i in range(1,    6					):
    if f'''layer_{i}.''' in name:
      _SCREAMING_SNAKE_CASE :					Optional[int]				     =			name.replace(f'''layer_{i}.''',    f'''encoder.layer.{i - 1}.'''					)

  if "conv_1." in name:
    _SCREAMING_SNAKE_CASE :					int				     =			name.replace("conv_1.",    "conv_stem."					)
  if ".block." in name:
    _SCREAMING_SNAKE_CASE :					Tuple				     =			name.replace(".block.",    "."					)

  if "exp_1x1" in name:
    _SCREAMING_SNAKE_CASE :					int				     =			name.replace("exp_1x1",    "expand_1x1"					)
  if "red_1x1" in name:
    _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			name.replace("red_1x1",    "reduce_1x1"					)
  if ".local_rep.conv_3x3." in name:
    _SCREAMING_SNAKE_CASE :					Tuple				     =			name.replace(".local_rep.conv_3x3.",    ".conv_kxk."					)
  if ".local_rep.conv_1x1." in name:
    _SCREAMING_SNAKE_CASE :					Dict				     =			name.replace(".local_rep.conv_1x1.",    ".conv_1x1."					)
  if ".norm." in name:
    _SCREAMING_SNAKE_CASE :					Dict				     =			name.replace(".norm.",    ".normalization."					)
  if ".conv." in name:
    _SCREAMING_SNAKE_CASE :					List[Any]				     =			name.replace(".conv.",    ".convolution."					)
  if ".conv_proj." in name:
    _SCREAMING_SNAKE_CASE :					str				     =			name.replace(".conv_proj.",    ".conv_projection."					)

  for i in range(0,    2					):
    for j in range(0,    4					):
      if f'''.{i}.{j}.''' in name:
        _SCREAMING_SNAKE_CASE :					List[str]				     =			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:
        _SCREAMING_SNAKE_CASE :					List[str]				     =			name.replace(f'''.{i}.{j}.''',    f'''.{i}.'''					)
        if "expand_1x1" in name:
          _SCREAMING_SNAKE_CASE :					Any				     =			name.replace("expand_1x1",    "downsampling_layer.expand_1x1"					)
        if "conv_3x3" in name:
          _SCREAMING_SNAKE_CASE :					int				     =			name.replace("conv_3x3",    "downsampling_layer.conv_3x3"					)
        if "reduce_1x1" in name:
          _SCREAMING_SNAKE_CASE :					str				     =			name.replace("reduce_1x1",    "downsampling_layer.reduce_1x1"					)

  for i in range(2,    5					):
    if f'''.global_rep.{i}.weight''' in name:
      _SCREAMING_SNAKE_CASE :					Any				     =			name.replace(f'''.global_rep.{i}.weight''',    ".layernorm.weight"					)
    if f'''.global_rep.{i}.bias''' in name:
      _SCREAMING_SNAKE_CASE :					List[Any]				     =			name.replace(f'''.global_rep.{i}.bias''',    ".layernorm.bias"					)

  if ".global_rep." in name:
    _SCREAMING_SNAKE_CASE :					str				     =			name.replace(".global_rep.",    ".transformer."					)
  if ".pre_norm_mha.0." in name:
    _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			name.replace(".pre_norm_mha.0.",    ".layernorm_before."					)
  if ".pre_norm_mha.1.out_proj." in name:
    _SCREAMING_SNAKE_CASE :					Any				     =			name.replace(".pre_norm_mha.1.out_proj.",    ".attention.output.dense."					)
  if ".pre_norm_ffn.0." in name:
    _SCREAMING_SNAKE_CASE :					List[str]				     =			name.replace(".pre_norm_ffn.0.",    ".layernorm_after."					)
  if ".pre_norm_ffn.1." in name:
    _SCREAMING_SNAKE_CASE :					List[str]				     =			name.replace(".pre_norm_ffn.1.",    ".intermediate.dense."					)
  if ".pre_norm_ffn.4." in name:
    _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			name.replace(".pre_norm_ffn.4.",    ".output.dense."					)
  if ".transformer." in name:
    _SCREAMING_SNAKE_CASE :					str				     =			name.replace(".transformer.",    ".transformer.layer."					)

  if ".aspp_layer." in name:
    _SCREAMING_SNAKE_CASE :					int				     =			name.replace(".aspp_layer.",    "."					)
  if ".aspp_pool." in name:
    _SCREAMING_SNAKE_CASE :					List[Any]				     =			name.replace(".aspp_pool.",    "."					)
  if "seg_head." in name:
    _SCREAMING_SNAKE_CASE :					int				     =			name.replace("seg_head.",    "segmentation_head."					)
  if "segmentation_head.classifier.classifier." in name:
    _SCREAMING_SNAKE_CASE :					Dict				     =			name.replace("segmentation_head.classifier.classifier.",    "segmentation_head.classifier."					)

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

  return name





def  _lowerCAmelCase     (  lowerCamelCase__				:				Optional[int],    lowerCamelCase__				:				Dict,    lowerCamelCase__				:				str=False					)	->     str:
  if base_model:
    _SCREAMING_SNAKE_CASE :					Dict				     =			""
  else:
    _SCREAMING_SNAKE_CASE :					Optional[int]				     =			"mobilevit."

  for key in orig_state_dict.copy().keys():
    _SCREAMING_SNAKE_CASE :					List[Any]				     =			orig_state_dict.pop(lowerCamelCase__					)

    if key[:8] == "encoder.":
      _SCREAMING_SNAKE_CASE :					str				     =			key[8:]

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

  return orig_state_dict





def  _lowerCAmelCase     (  )	->     int:
  _SCREAMING_SNAKE_CASE :					Optional[int]				     =			"http://images.cocodataset.org/val2017/000000039769.jpg"
  _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			Image.open(requests.get(lowerCamelCase__,    stream=lowerCamelCase__					).raw					)
  return im





@torch.no_grad()
def  _lowerCAmelCase     (  lowerCamelCase__				:				int,    lowerCamelCase__				:				str,    lowerCamelCase__				:				str,    lowerCamelCase__				:				Tuple=False					)	->     Union[str, Any]:
  _SCREAMING_SNAKE_CASE :					Any				     =			get_mobilevit_config(lowerCamelCase__					)

  # load original state_dict
  _SCREAMING_SNAKE_CASE :					List[Any]				     =			torch.load(lowerCamelCase__,    map_location="cpu"					)

  # load 🤗 model
  if mobilevit_name.startswith("deeplabv3_"					):
    _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			MobileViTForSemanticSegmentation(lowerCamelCase__					).eval()
  else:
    _SCREAMING_SNAKE_CASE :					List[str]				     =			MobileViTForImageClassification(lowerCamelCase__					).eval()

  _SCREAMING_SNAKE_CASE :					List[str]				     =			convert_state_dict(lowerCamelCase__,    lowerCamelCase__					)
  model.load_state_dict(lowerCamelCase__					)

  # Check outputs on an image, prepared by MobileViTImageProcessor
  _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			MobileViTImageProcessor(crop_size=config.image_size,    size=config.image_size + 3_2					)
  _SCREAMING_SNAKE_CASE :					int				     =			image_processor(images=prepare_img(),    return_tensors="pt"					)
  _SCREAMING_SNAKE_CASE :					List[Any]				     =			model(**lowerCamelCase__					)
  _SCREAMING_SNAKE_CASE :					List[str]				     =			outputs.logits

  if mobilevit_name.startswith("deeplabv3_"					):
    assert logits.shape == (1, 2_1, 3_2, 3_2)

    if mobilevit_name == "deeplabv3_mobilevit_s":
      _SCREAMING_SNAKE_CASE :					int				     =			torch.tensor(
          [
              [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
              [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
              [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
          ]					)
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      _SCREAMING_SNAKE_CASE :					Any				     =			torch.tensor(
          [
              [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
              [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
              [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
          ]					)
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      _SCREAMING_SNAKE_CASE :					str				     =			torch.tensor(
          [
              [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
              [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
              [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
          ]					)
    else:
      raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''					)

    assert torch.allclose(logits[0, :3, :3, :3],    lowerCamelCase__,    atol=1E-4					)
  else:
    assert logits.shape == (1, 1_0_0_0)

    if mobilevit_name == "mobilevit_s":
      _SCREAMING_SNAKE_CASE :					List[Any]				     =			torch.tensor([-0.9866, 0.2392, -1.1241]					)
    elif mobilevit_name == "mobilevit_xs":
      _SCREAMING_SNAKE_CASE :					List[Any]				     =			torch.tensor([-2.4761, -0.9399, -1.9587]					)
    elif mobilevit_name == "mobilevit_xxs":
      _SCREAMING_SNAKE_CASE :					str				     =			torch.tensor([-1.9364, -1.2327, -0.4653]					)
    else:
      raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''					)

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

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

  if push_to_hub:
    _SCREAMING_SNAKE_CASE :					List[str]				     =			{
        "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..."					)
    _SCREAMING_SNAKE_CASE :					List[Any]				     =			model_mapping[mobilevit_name]
    image_processor.push_to_hub(lowerCamelCase__,    organization="apple"					)
    model.push_to_hub(lowerCamelCase__,    organization="apple"					)


if __name__ == "__main__":
      lowercase_							:    Optional[Any]         =						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_							:    List[Any]         =						parser.parse_args()
      convert_movilevit_checkpoint(
          args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )





"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list







@require_torchaudio
@require_sentencepiece
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			'laion/clap-htsat-unfused'
         a__		=			tempfile.mkdtemp()



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return RobertaTokenizer.from_pretrained(self.checkpoint     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         return ClapFeatureExtractor.from_pretrained(self.checkpoint     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         shutil.rmtree(self.tmpdirname   )



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







         """simple docstring"""



         a__		=			self.get_tokenizer()
         a__		=			self.get_feature_extractor()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         processor.save_pretrained(self.tmpdirname   )
         a__		=			ClapProcessor.from_pretrained(self.tmpdirname   )

         self.assertEqual(processor.tokenizer.get_vocab()     ,				tokenizer.get_vocab()   )
         self.assertIsInstance(processor.tokenizer     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,				feature_extractor.to_json_string()   )
         self.assertIsInstance(processor.feature_extractor     ,				__SCREAMING_SNAKE_CASE   )



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







         """simple docstring"""



         a__		=			ClapProcessor(tokenizer=self.get_tokenizer()     ,				feature_extractor=self.get_feature_extractor()   )
         processor.save_pretrained(self.tmpdirname   )

         a__		=			self.get_tokenizer(bos_token='(BOS)'     ,				eos_token='(EOS)'   )
         a__		=			self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE     ,				padding_value=1.0   )

         a__		=			ClapProcessor.from_pretrained(
             self.tmpdirname     ,				bos_token='(BOS)'     ,				eos_token='(EOS)'     ,				do_normalize=__SCREAMING_SNAKE_CASE     ,				padding_value=1.0   )

         self.assertEqual(processor.tokenizer.get_vocab()     ,				tokenizer_add_kwargs.get_vocab()   )
         self.assertIsInstance(processor.tokenizer     ,				__SCREAMING_SNAKE_CASE   )

         self.assertEqual(processor.feature_extractor.to_json_string()     ,				feature_extractor_add_kwargs.to_json_string()   )
         self.assertIsInstance(processor.feature_extractor     ,				__SCREAMING_SNAKE_CASE   )



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







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			floats_list((3, 1_0_0_0)   )

         a__		=			feature_extractor(__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )
         a__		=			processor(audios=__SCREAMING_SNAKE_CASE     ,				return_tensors='np'   )

         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()     ,				input_processor[key].sum()     ,				delta=1e-2   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			'This is a test string'

         a__		=			processor(text=__SCREAMING_SNAKE_CASE   )

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE   )

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]     ,				encoded_processor[key]   )



     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         a__		=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         a__		=			processor.batch_decode(__SCREAMING_SNAKE_CASE   )
         a__		=			tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )




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







         """simple docstring"""



         a__		=			self.get_feature_extractor()
         a__		=			self.get_tokenizer()

         a__		=			ClapProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,				feature_extractor=__SCREAMING_SNAKE_CASE   )

         self.assertListEqual(
             processor.model_input_names[2:]     ,				feature_extractor.model_input_names     ,				msg='`processor` and `feature_extractor` model input names do not match'     ,				)


