





import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)

def     UpperCAmelCase_						(						_UpperCAmelCase       :str							)  ->							List[str]:
     '''simple docstring'''


     A_					      =					CLIPSegTextConfig()
     A_					      =					CLIPSegVisionConfig(patch_size=16							)

     A_					      =					True if '''refined''' in model_name else False
     A_					      =					16 if '''rd16''' in model_name else 64

     A_					      =					CLIPSegConfig.from_text_vision_configs(
         _UpperCAmelCase     ,	_UpperCAmelCase     ,	use_complex_transposed_convolution=_UpperCAmelCase     ,	reduce_dim=_UpperCAmelCase     ,	)
     return config

def     UpperCAmelCase_						(						_UpperCAmelCase       :str							)  ->							int:
     '''simple docstring'''


     if "clip_model" in name:
          A_					      =					name.replace('''clip_model'''     ,	'''clip'''							)
     if "transformer" in name:
          if "visual" in name:
               A_					      =					name.replace('''visual.transformer'''     ,	'''vision_model'''							)
          else:
               A_					      =					name.replace('''transformer'''     ,	'''text_model'''							)
     if "resblocks" in name:
          A_					      =					name.replace('''resblocks'''     ,	'''encoder.layers'''							)
     if "ln_1" in name:
          A_					      =					name.replace('''ln_1'''     ,	'''layer_norm1'''							)
     if "ln_2" in name:
          A_					      =					name.replace('''ln_2'''     ,	'''layer_norm2'''							)
     if "c_fc" in name:
          A_					      =					name.replace('''c_fc'''     ,	'''fc1'''							)
     if "c_proj" in name:
          A_					      =					name.replace('''c_proj'''     ,	'''fc2'''							)
     if "attn" in name and "self" not in name:
          A_					      =					name.replace('''attn'''     ,	'''self_attn'''							)
     # text encoder
     if "token_embedding" in name:
          A_					      =					name.replace('''token_embedding'''     ,	'''text_model.embeddings.token_embedding'''							)
     if "positional_embedding" in name and "visual" not in name:
          A_					      =					name.replace('''positional_embedding'''     ,	'''text_model.embeddings.position_embedding.weight'''							)
     if "ln_final" in name:
          A_					      =					name.replace('''ln_final'''     ,	'''text_model.final_layer_norm'''							)
     # vision encoder
     if "visual.class_embedding" in name:
          A_					      =					name.replace('''visual.class_embedding'''     ,	'''vision_model.embeddings.class_embedding'''							)
     if "visual.conv1" in name:
          A_					      =					name.replace('''visual.conv1'''     ,	'''vision_model.embeddings.patch_embedding'''							)
     if "visual.positional_embedding" in name:
          A_					      =					name.replace('''visual.positional_embedding'''     ,	'''vision_model.embeddings.position_embedding.weight'''							)
     if "visual.ln_pre" in name:
          A_					      =					name.replace('''visual.ln_pre'''     ,	'''vision_model.pre_layrnorm'''							)
     if "visual.ln_post" in name:
          A_					      =					name.replace('''visual.ln_post'''     ,	'''vision_model.post_layernorm'''							)
     # projection layers
     if "visual.proj" in name:
          A_					      =					name.replace('''visual.proj'''     ,	'''visual_projection.weight'''							)
     if "text_projection" in name:
          A_					      =					name.replace('''text_projection'''     ,	'''text_projection.weight'''							)
     # decoder
     if "trans_conv" in name:
          A_					      =					name.replace('''trans_conv'''     ,	'''transposed_convolution'''							)
     if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
          A_					      =					'''decoder.''' + name
     if "blocks" in name:
          A_					      =					name.replace('''blocks'''     ,	'''decoder.layers'''							)
     if "linear1" in name:
          A_					      =					name.replace('''linear1'''     ,	'''mlp.fc1'''							)
     if "linear2" in name:
          A_					      =					name.replace('''linear2'''     ,	'''mlp.fc2'''							)
     if "norm1" in name and "layer_" not in name:
          A_					      =					name.replace('''norm1'''     ,	'''layer_norm1'''							)
     if "norm2" in name and "layer_" not in name:
          A_					      =					name.replace('''norm2'''     ,	'''layer_norm2'''							)

     return name

def     UpperCAmelCase_						(						_UpperCAmelCase       :int     ,	_UpperCAmelCase       :List[Any]							)  ->							List[Any]:
     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          A_					      =					orig_state_dict.pop(_UpperCAmelCase							)

          if key.startswith('''clip_model'''							) and "attn.in_proj" in key:
               A_					      =					key.split('''.'''							)
               if "visual" in key:
                    A_					      =					int(key_split[4]							)
                    A_					      =					config.vision_config.hidden_size
                    A_					      =					'''vision_model'''
               else:
                    A_					      =					int(key_split[3]							)
                    A_					      =					config.text_config.hidden_size
                    A_					      =					'''text_model'''

               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:]
          elif "self_attn" in key and "out_proj" not in key:
               A_					      =					key.split('''.'''							)
               A_					      =					int(key_split[1]							)
               A_					      =					config.reduce_dim
               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_					      =					rename_key(_UpperCAmelCase							)
               if "visual_projection" in new_name or "text_projection" in new_name:
                    A_					      =					val.T
               A_					      =					val

     return orig_state_dict

def     UpperCAmelCase_						(						)  ->							List[Any]:
     '''simple docstring'''


     A_					      =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A_					      =					Image.open(requests.get(_UpperCAmelCase     ,	stream=_UpperCAmelCase							).raw							)
     return image

def     UpperCAmelCase_						(						_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :int     ,	_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :Optional[int]							)  ->							Optional[Any]:
     '''simple docstring'''


     A_					      =					get_clipseg_config(_UpperCAmelCase							)
     A_					      =					CLIPSegForImageSegmentation(_UpperCAmelCase							)
     model.eval()

     A_					      =					torch.load(_UpperCAmelCase     ,	map_location='''cpu'''							)

     # remove some keys
     for key in state_dict.copy().keys():
          if key.startswith('''model'''							):
               state_dict.pop(_UpperCAmelCase     ,	_UpperCAmelCase							)

    # rename some keys
     A_					      =					convert_state_dict(_UpperCAmelCase     ,	_UpperCAmelCase							)
     A_      ,						A_					      =					model.load_state_dict(_UpperCAmelCase     ,	strict=_UpperCAmelCase							)

     if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
          raise ValueError('''Missing keys that are not expected: {}'''.format(_UpperCAmelCase							)							)
     if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
          raise ValueError(f'Unexpected keys: {unexpected_keys}'							)

     A_					      =					ViTImageProcessor(size=352							)
     A_					      =					CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
     A_					      =					CLIPSegProcessor(image_processor=_UpperCAmelCase     ,	tokenizer=_UpperCAmelCase							)

     A_					      =					prepare_img()
     A_					      =					['''a glass''', '''something to fill''', '''wood''', '''a jar''']

     A_					      =					processor(text=_UpperCAmelCase     ,	images=[image] * len(_UpperCAmelCase							)     ,	padding='''max_length'''     ,	return_tensors='''pt'''							)

     with torch.no_grad():
          A_					      =					model(**_UpperCAmelCase							)

     # verify values
     A_					      =					torch.tensor([0.1110, -0.1882, 0.1645]							)
     A_					      =					torch.tensor([0.2692, -0.7197, -0.1328]							)
     if model_name == "clipseg-rd64-refined":
          A_					      =					torch.tensor(
              [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]							)
     elif model_name == "clipseg-rd64":
          A_					      =					torch.tensor(
              [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]							)
     elif model_name == "clipseg-rd16":
          A_					      =					torch.tensor(
              [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]							)
     else:
          raise ValueError(f'Model name {model_name} not supported.'							)

     assert torch.allclose(outputs.logits[0, :3, :3]     ,	_UpperCAmelCase     ,	atol=1e-3							)
     assert torch.allclose(outputs.conditional_embeddings[0, :3]     ,	_UpperCAmelCase     ,	atol=1e-3							)
     assert torch.allclose(outputs.pooled_output[0, :3]     ,	_UpperCAmelCase     ,	atol=1e-3							)
     print('''Looks ok!'''							)

     if pytorch_dump_folder_path is not None:
          print(f'Saving model and processor to {pytorch_dump_folder_path}'							)
          model.save_pretrained(_UpperCAmelCase							)
          processor.save_pretrained(_UpperCAmelCase							)

     if push_to_hub:
          print(f'Pushing model and processor for {model_name} to the hub'							)
          model.push_to_hub(f'CIDAS/{model_name}'							)
          processor.push_to_hub(f'CIDAS/{model_name}'							)


if __name__ == "__main__":
      a__			:    Optional[Any]			      =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='clipseg-rd64',
          type=str,
          choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
          help=(
              'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
              ' reduce dimension)'
          ),
      )
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
          type=str,
          help=(
              'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
              ' the decoder weights.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, 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.'
      )

      a__			:    Union[str, Any]			      =		parser.parse_args()
      convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


a__			:    Optional[Any]			      =		get_tests_dir('fixtures/test_sentencepiece.model')





@require_torch
@require_torchaudio
@require_sentencepiece
class UpperCAmelCase_							(   unittest.TestCase   ):


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					tempfile.mkdtemp()

       A_					      =					['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       A_					      =					dict(zip(__snake_case   ,range(len(__snake_case   )   )   )   )
       A_					      =					Path(self.tmpdirname   )
       save_json(__snake_case   ,save_dir / VOCAB_FILES_NAMES['''vocab_file''']   )
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
            copyfile(__snake_case   ,save_dir / VOCAB_FILES_NAMES['''spm_file''']   )

       A_					      =					SpeechaTextTokenizer.from_pretrained(self.tmpdirname   )
       tokenizer.save_pretrained(self.tmpdirname   )

       A_					      =					{
           '''feature_size''': 2_4,
           '''num_mel_bins''': 2_4,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_6_0_0_0,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }
       save_json(__snake_case   ,save_dir / FEATURE_EXTRACTOR_NAME   )


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,**__snake_case   )


  def 	__UpperCAmelCase    (       self   ,**__snake_case   ):



       """simple docstring"""





       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,**__snake_case   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_tokenizer()
       A_					      =					self.get_feature_extractor()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       processor.save_pretrained(self.tmpdirname   )
       A_					      =					SpeechaTextProcessor.from_pretrained(self.tmpdirname   )

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

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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					SpeechaTextProcessor(
           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=__snake_case   ,padding_value=1.0   )

       A_					      =					SpeechaTextProcessor.from_pretrained(
           self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=__snake_case   ,padding_value=1.0   )

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

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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					floats_list((3, 1_0_0_0)   )

       A_					      =					feature_extractor(__snake_case   ,return_tensors='''np'''   )
       A_					      =					processor(__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 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

       A_					      =					'''This is a test string'''

       A_					      =					processor(text=__snake_case   )

       A_					      =					tokenizer(__snake_case   )

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


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

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

       A_					      =					processor.batch_decode(__snake_case   )
       A_					      =					tokenizer.batch_decode(__snake_case   )

       self.assertListEqual(__snake_case   ,__snake_case   )




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.get_feature_extractor()
       A_					      =					self.get_tokenizer()

       A_					      =					SpeechaTextProcessor(tokenizer=__snake_case   ,feature_extractor=__snake_case   )

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



