




"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

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



def 			A__						(  lowercase_    :  Union[str, Any]	)     ->       Tuple:
   __lowerCamelCase								=				CLIPSegTextConfig()
   __lowerCamelCase								=				CLIPSegVisionConfig(patch_size=16	)

   __lowerCamelCase								=				True if "refined" in model_name else False
   __lowerCamelCase								=				16 if "rd16" in model_name else 64

   __lowerCamelCase								=				CLIPSegConfig.from_text_vision_configs(
       lowercase__			,   lowercase__			,   use_complex_transposed_convolution=lowercase__			,   reduce_dim=lowercase__			,   )
   return config



def 			A__						(  lowercase_    :  Optional[Any]	)     ->       Dict:
   # update prefixes
   if "clip_model" in name:
      __lowerCamelCase								=				name.replace('clip_model'			,   'clip'	)
   if "transformer" in name:
      if "visual" in name:
         __lowerCamelCase								=				name.replace('visual.transformer'			,   'vision_model'	)
      else:
         __lowerCamelCase								=				name.replace('transformer'			,   'text_model'	)
   if "resblocks" in name:
      __lowerCamelCase								=				name.replace('resblocks'			,   'encoder.layers'	)
   if "ln_1" in name:
      __lowerCamelCase								=				name.replace('ln_1'			,   'layer_norm1'	)
   if "ln_2" in name:
      __lowerCamelCase								=				name.replace('ln_2'			,   'layer_norm2'	)
   if "c_fc" in name:
      __lowerCamelCase								=				name.replace('c_fc'			,   'fc1'	)
   if "c_proj" in name:
      __lowerCamelCase								=				name.replace('c_proj'			,   'fc2'	)
   if "attn" in name and "self" not in name:
      __lowerCamelCase								=				name.replace('attn'			,   'self_attn'	)
   # text encoder
   if "token_embedding" in name:
      __lowerCamelCase								=				name.replace('token_embedding'			,   'text_model.embeddings.token_embedding'	)
   if "positional_embedding" in name and "visual" not in name:
      __lowerCamelCase								=				name.replace('positional_embedding'			,   'text_model.embeddings.position_embedding.weight'	)
   if "ln_final" in name:
      __lowerCamelCase								=				name.replace('ln_final'			,   'text_model.final_layer_norm'	)
   # vision encoder
   if "visual.class_embedding" in name:
      __lowerCamelCase								=				name.replace('visual.class_embedding'			,   'vision_model.embeddings.class_embedding'	)
   if "visual.conv1" in name:
      __lowerCamelCase								=				name.replace('visual.conv1'			,   'vision_model.embeddings.patch_embedding'	)
   if "visual.positional_embedding" in name:
      __lowerCamelCase								=				name.replace('visual.positional_embedding'			,   'vision_model.embeddings.position_embedding.weight'	)
   if "visual.ln_pre" in name:
      __lowerCamelCase								=				name.replace('visual.ln_pre'			,   'vision_model.pre_layrnorm'	)
   if "visual.ln_post" in name:
      __lowerCamelCase								=				name.replace('visual.ln_post'			,   'vision_model.post_layernorm'	)
   # projection layers
   if "visual.proj" in name:
      __lowerCamelCase								=				name.replace('visual.proj'			,   'visual_projection.weight'	)
   if "text_projection" in name:
      __lowerCamelCase								=				name.replace('text_projection'			,   'text_projection.weight'	)
   # decoder
   if "trans_conv" in name:
      __lowerCamelCase								=				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:
      __lowerCamelCase								=				"decoder." + name
   if "blocks" in name:
      __lowerCamelCase								=				name.replace('blocks'			,   'decoder.layers'	)
   if "linear1" in name:
      __lowerCamelCase								=				name.replace('linear1'			,   'mlp.fc1'	)
   if "linear2" in name:
      __lowerCamelCase								=				name.replace('linear2'			,   'mlp.fc2'	)
   if "norm1" in name and "layer_" not in name:
      __lowerCamelCase								=				name.replace('norm1'			,   'layer_norm1'	)
   if "norm2" in name and "layer_" not in name:
      __lowerCamelCase								=				name.replace('norm2'			,   'layer_norm2'	)

   return name



def 			A__						(  lowercase_    :  str			,   lowercase_    :  List[str]	)     ->       Optional[int]:
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase								=				orig_state_dict.pop(lowercase__	)

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

         if "weight" in key:
            __lowerCamelCase								=				val[:dim, :]
            __lowerCamelCase								=				val[
                dim : dim * 2, :
            ]
            __lowerCamelCase								=				val[-dim:, :]
         else:
            __lowerCamelCase								=				val[:dim]
            __lowerCamelCase								=				val[dim : dim * 2]
            __lowerCamelCase								=				val[-dim:]
      elif "self_attn" in key and "out_proj" not in key:
         __lowerCamelCase								=				key.split('.'	)
         __lowerCamelCase								=				int(key_split[1]	)
         __lowerCamelCase								=				config.reduce_dim
         if "weight" in key:
            __lowerCamelCase								=				val[:dim, :]
            __lowerCamelCase								=				val[dim : dim * 2, :]
            __lowerCamelCase								=				val[-dim:, :]
         else:
            __lowerCamelCase								=				val[:dim]
            __lowerCamelCase								=				val[dim : dim * 2]
            __lowerCamelCase								=				val[-dim:]
      else:
         __lowerCamelCase								=				rename_key(lowercase__	)
         if "visual_projection" in new_name or "text_projection" in new_name:
            __lowerCamelCase								=				val.T
         __lowerCamelCase								=				val

   return orig_state_dict



def 			A__						(  )     ->       Union[str, Any]:
   __lowerCamelCase								=				"http://images.cocodataset.org/val2017/000000039769.jpg"
   __lowerCamelCase								=				Image.open(requests.get(lowercase__			,   stream=lowercase__	).raw	)
   return image



def 			A__						(  lowercase_    :  Dict			,   lowercase_    :  Tuple			,   lowercase_    :  str			,   lowercase_    :  Tuple	)     ->       List[str]:
   __lowerCamelCase								=				get_clipseg_config(lowercase__	)
   __lowerCamelCase								=				CLIPSegForImageSegmentation(lowercase__	)
   model.eval()

   __lowerCamelCase								=				torch.load(lowercase__			,   map_location='cpu'	)

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

    # rename some keys
   __lowerCamelCase								=				convert_state_dict(lowercase__			,   lowercase__	)
   __lowerCamelCase								=				model.load_state_dict(lowercase__			,   strict=lowercase__	)

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

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

   __lowerCamelCase								=				prepare_img()
   __lowerCamelCase								=				["a glass", "something to fill", "wood", "a jar"]

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

   with torch.no_grad():
      __lowerCamelCase								=				model(**lowercase__	)

   # verify values
   __lowerCamelCase								=				torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]	)
   __lowerCamelCase								=				torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]	)
   if model_name == "clipseg-rd64-refined":
      __lowerCamelCase								=				torch.tensor(
          [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]	)
   elif model_name == "clipseg-rd64":
      __lowerCamelCase								=				torch.tensor(
          [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]	)
   elif model_name == "clipseg-rd16":
      __lowerCamelCase								=				torch.tensor(
          [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]	)
   else:
      raise ValueError(F'''Model name {model_name} not supported.'''	)

   assert torch.allclose(outputs.logits[0, :3, :3]			,   lowercase__			,   atol=1e-3	)
   assert torch.allclose(outputs.conditional_embeddings[0, :3]			,   lowercase__			,   atol=1e-3	)
   assert torch.allclose(outputs.pooled_output[0, :3]			,   lowercase__			,   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(lowercase__	)
      processor.save_pretrained(lowercase__	)

   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__":
     _SCREAMING_SNAKE_CASE  :		Optional[int]        =      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.'''
     )

     _SCREAMING_SNAKE_CASE  :		List[Any]        =      parser.parse_args()
     convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""







import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode



def 			A__						(  lowercase_    :  Dict			,   **lowercase_    :  Union[str, Any]	)     ->       Dict:
   return RunBeamCommand(
       args.dataset			,   args.name			,   args.cache_dir			,   args.beam_pipeline_options			,   args.data_dir			,   args.all_configs			,   args.save_info or args.save_infos			,   args.ignore_verifications			,   args.force_redownload			,   **lowercase_			,   )






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   @staticmethod
   def       _A							(  _SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				parser.add_parser('run_beam'   ,							help='Run a Beam dataset processing pipeline'  )
      run_beam_parser.add_argument('dataset'   ,							type=_SCREAMING_SNAKE_CASE   ,							help='Name of the dataset to download'  )
      run_beam_parser.add_argument('--name'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=_SCREAMING_SNAKE_CASE   ,							help='Dataset config name'  )
      run_beam_parser.add_argument(
          '--cache_dir'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=_SCREAMING_SNAKE_CASE   ,							help='Cache directory where the datasets are stored'   ,							)
      run_beam_parser.add_argument(
          '--beam_pipeline_options'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=''   ,							help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'   ,							)
      run_beam_parser.add_argument(
          '--data_dir'   ,							type=_SCREAMING_SNAKE_CASE   ,							default=_SCREAMING_SNAKE_CASE   ,							help='Can be used to specify a manual directory to get the files from'   ,							)
      run_beam_parser.add_argument('--all_configs'   ,							action='store_true'   ,							help='Test all dataset configurations'  )
      run_beam_parser.add_argument('--save_info'   ,							action='store_true'   ,							help='Save the dataset infos file'  )
      run_beam_parser.add_argument(
          '--ignore_verifications'   ,							action='store_true'   ,							help='Run the test without checksums and splits checks'  )
      run_beam_parser.add_argument('--force_redownload'   ,							action='store_true'   ,							help='Force dataset redownload'  )
      # aliases
      run_beam_parser.add_argument('--save_infos'   ,							action='store_true'   ,							help='alias for save_info'  )
      run_beam_parser.set_defaults(func=_SCREAMING_SNAKE_CASE  )







   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				dataset
      __lowerCamelCase								=				name
      __lowerCamelCase								=				cache_dir
      __lowerCamelCase								=				beam_pipeline_options
      __lowerCamelCase								=				data_dir
      __lowerCamelCase								=				all_configs
      __lowerCamelCase								=				save_infos
      __lowerCamelCase								=				ignore_verifications
      __lowerCamelCase								=				force_redownload
      __lowerCamelCase								=				config_kwargs







   def       _A							(  self  ):


      '''simple docstring'''

      import apache_beam as beam

      if self._name is not None and self._all_configs:
         print('Both parameters `name` and `all_configs` can\'t be used at once.'  )
         exit(1  )
      __lowerCamelCase		,__lowerCamelCase								=				self._dataset, self._name
      __lowerCamelCase								=				dataset_module_factory(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				import_main_class(dataset_module.module_path  )
      __lowerCamelCase								=				[]
      if self._beam_pipeline_options:
         __lowerCamelCase								=				beam.options.pipeline_options.PipelineOptions(
             flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','  ) if opt]  )
      else:
         __lowerCamelCase								=				None
      if self._all_configs and len(builder_cls.BUILDER_CONFIGS  ) > 0:
         for builder_config in builder_cls.BUILDER_CONFIGS:
            builders.append(
                builder_cls(
                    config_name=builder_config.name   ,							data_dir=self._data_dir   ,							hash=dataset_module.hash   ,							beam_options=_SCREAMING_SNAKE_CASE   ,							cache_dir=self._cache_dir   ,							base_path=dataset_module.builder_kwargs.get('base_path'  )   ,							)  )
      else:
         builders.append(
             builder_cls(
                 config_name=_SCREAMING_SNAKE_CASE   ,							data_dir=self._data_dir   ,							beam_options=_SCREAMING_SNAKE_CASE   ,							cache_dir=self._cache_dir   ,							base_path=dataset_module.builder_kwargs.get('base_path'  )   ,							**self._config_kwargs   ,							)  )

      for builder in builders:
         builder.download_and_prepare(
             download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
             if not self._force_redownload
             else DownloadMode.FORCE_REDOWNLOAD   ,							download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH  )   ,							verification_mode=VerificationMode.NO_CHECKS
             if self._ignore_verifications
             else VerificationMode.ALL_CHECKS   ,							try_from_hf_gcs=_SCREAMING_SNAKE_CASE   ,							)
         if self._save_infos:
            builder._save_infos()

      print('Apache beam run successful.'  )

      # If save_infos=True, the dataset infos file is created next to the loaded module file.
      # Let's move it to the original directory of the dataset script, to allow the user to
      # upload them on S3 at the same time afterwards.
      if self._save_infos:
         __lowerCamelCase								=				os.path.join(builder_cls.get_imported_module_dir()   ,							config.DATASETDICT_INFOS_FILENAME  )

         __lowerCamelCase								=				Path(_SCREAMING_SNAKE_CASE  ).name + '.py'

         __lowerCamelCase								=				os.path.join(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )
         if os.path.isfile(_SCREAMING_SNAKE_CASE  ):
            __lowerCamelCase								=				os.path.dirname(_SCREAMING_SNAKE_CASE  )
         elif os.path.isfile(_SCREAMING_SNAKE_CASE  ):
            __lowerCamelCase								=				path
         else:  # in case of a remote dataset
            print(F'''Dataset Infos file saved at {dataset_infos_path}'''  )
            exit(1  )

         # Move datasetinfo back to the user
         __lowerCamelCase								=				os.path.join(_SCREAMING_SNAKE_CASE   ,							config.DATASETDICT_INFOS_FILENAME  )
         copyfile(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )
         print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''  )






