






import argparse

import requests
import torch
from PIL import Image

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






def    UpperCAmelCase__	(						lowerCamelCase  : 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(
       lowerCamelCase ,lowerCamelCase ,use_complex_transposed_convolution=lowerCamelCase ,reduce_dim=lowerCamelCase ,)
   return config






def    UpperCAmelCase__	(						lowerCamelCase  : str						):
   # 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    UpperCAmelCase__	(						lowerCamelCase  : int ,lowerCamelCase  : Optional[Any]						):
   for key in orig_state_dict.copy().keys():
      lowerCamelCase											=orig_state_dict.pop(lowerCamelCase						)

      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(lowerCamelCase						)
         if "visual_projection" in new_name or "text_projection" in new_name:
            lowerCamelCase											=val.T
         lowerCamelCase											=val

   return orig_state_dict






def    UpperCAmelCase__	(						):
   lowerCamelCase											="""http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowerCamelCase											=Image.open(requests.get(lowerCamelCase ,stream=lowerCamelCase						).raw						)
   return image






def    UpperCAmelCase__	(						lowerCamelCase  : Dict ,lowerCamelCase  : List[str] ,lowerCamelCase  : Optional[int] ,lowerCamelCase  : Any						):
   lowerCamelCase											=get_clipseg_config(lowerCamelCase						)
   lowerCamelCase											=CLIPSegForImageSegmentation(lowerCamelCase						)
   model.eval()

   lowerCamelCase											=torch.load(lowerCamelCase ,map_location="""cpu"""						)

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

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

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

   lowerCamelCase											=ViTImageProcessor(size=3_52						)
   lowerCamelCase											=CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""						)
   lowerCamelCase											=CLIPSegProcessor(image_processor=lowerCamelCase ,tokenizer=lowerCamelCase						)

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

   lowerCamelCase											=processor(text=lowerCamelCase ,images=[image] * len(lowerCamelCase						) ,padding="""max_length""" ,return_tensors="""pt"""						)

   with torch.no_grad():
      lowerCamelCase											=model(**lowerCamelCase						)

   # verify values
   lowerCamelCase											=torch.tensor([0.1110, -0.1882, 0.1645]						)
   lowerCamelCase											=torch.tensor([0.2692, -0.7197, -0.1328]						)
   if model_name == "clipseg-rd64-refined":
      lowerCamelCase											=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":
      lowerCamelCase											=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":
      lowerCamelCase											=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] ,lowerCamelCase ,atol=1E-3						)
   assert torch.allclose(outputs.conditional_embeddings[0, :3] ,lowerCamelCase ,atol=1E-3						)
   assert torch.allclose(outputs.pooled_output[0, :3] ,lowerCamelCase ,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(lowerCamelCase						)
      processor.save_pretrained(lowerCamelCase						)

   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__":
   _UpperCAmelCase				:	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.'
   )

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







import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

_UpperCAmelCase				:	Optional[int]    						=    logging.get_logger(__name__)






def    UpperCAmelCase__	(						lowerCamelCase  : bytes ,lowerCamelCase  : int						):
   lowerCamelCase											=f'''{sampling_rate}'''
   lowerCamelCase											="""1"""
   lowerCamelCase											="""f32le"""
   lowerCamelCase											=[
       """ffmpeg""",
       """-i""",
       """pipe:0""",
       """-ac""",
       ac,
       """-ar""",
       ar,
       """-f""",
       format_for_conversion,
       """-hide_banner""",
       """-loglevel""",
       """quiet""",
       """pipe:1""",
   ]

   try:
      lowerCamelCase											=subprocess.Popen(lowerCamelCase ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE						)
   except FileNotFoundError:
      raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""						)
   lowerCamelCase											=ffmpeg_process.communicate(lowerCamelCase						)
   lowerCamelCase											=output_stream[0]

   lowerCamelCase											=np.frombuffer(lowerCamelCase ,np.floataa						)
   if audio.shape[0] == 0:
      raise ValueError("""Malformed soundfile"""						)
   return audio







@add_end_docstrings(UpperCamelCase   )
class 		lowerCAmelCase_							(     UpperCamelCase   ):





     def __init__(  self       ,   *_UpperCAmelCase       ,   **_UpperCAmelCase     )		->							Dict:
        # Default, might be overriden by the model.config.
        lowerCamelCase											=5
        super().__init__(*_UpperCAmelCase       ,   **_UpperCAmelCase     )

        if self.framework != "pt":
           raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''     )

        self.check_model_type(_UpperCAmelCase     )





     def __call__(  self       ,   _UpperCAmelCase       ,   **_UpperCAmelCase       ,   )		->							Union[str, Any]:
        return super().__call__(_UpperCAmelCase       ,   **_UpperCAmelCase     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase=None       ,   **_UpperCAmelCase     )		->							Optional[int]:
        # No parameters on this pipeline right now
        lowerCamelCase											={}
        if top_k is not None:
           if top_k > self.model.config.num_labels:
              lowerCamelCase											=self.model.config.num_labels
           lowerCamelCase											=top_k
        return {}, {}, postprocess_params





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							Dict:
        if isinstance(_UpperCAmelCase       ,   _UpperCAmelCase     ):
           if inputs.startswith("""http://"""     ) or inputs.startswith("""https://"""     ):
              # We need to actually check for a real protocol, otherwise it's impossible to use a local file
              # like http_huggingface_co.png
              lowerCamelCase											=requests.get(_UpperCAmelCase     ).content
           else:
              with open(_UpperCAmelCase       ,   """rb"""     ) as f:
                 lowerCamelCase											=f.read()

        if isinstance(_UpperCAmelCase       ,   _UpperCAmelCase     ):
           lowerCamelCase											=ffmpeg_read(_UpperCAmelCase       ,   self.feature_extractor.sampling_rate     )

        if isinstance(_UpperCAmelCase       ,   _UpperCAmelCase     ):
           # Accepting `"array"` which is the key defined in `datasets` for
           # better integration
           if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
              raise ValueError(
                  """When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a """
                  """\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, """
                  """containing the sampling_rate associated with that array"""     )

           lowerCamelCase											=inputs.pop("""raw"""       ,   _UpperCAmelCase     )
           if _inputs is None:
              # Remove path which will not be used from `datasets`.
              inputs.pop("""path"""       ,   _UpperCAmelCase     )
              lowerCamelCase											=inputs.pop("""array"""       ,   _UpperCAmelCase     )
           lowerCamelCase											=inputs.pop("""sampling_rate"""     )
           lowerCamelCase											=_inputs
           if in_sampling_rate != self.feature_extractor.sampling_rate:
              import torch

              if is_torchaudio_available():
                 from torchaudio import functional as F
              else:
                 raise ImportError(
                     """torchaudio is required to resample audio samples in AudioClassificationPipeline. """
                     """The torchaudio package can be installed through: `pip install torchaudio`."""     )

              lowerCamelCase											=F.resample(
                  torch.from_numpy(_UpperCAmelCase     )       ,   _UpperCAmelCase       ,   self.feature_extractor.sampling_rate     ).numpy()

        if not isinstance(_UpperCAmelCase       ,   np.ndarray     ):
           raise ValueError("""We expect a numpy ndarray as input"""     )
        if len(inputs.shape     ) != 1:
           raise ValueError("""We expect a single channel audio input for AudioClassificationPipeline"""     )

        lowerCamelCase											=self.feature_extractor(
            _UpperCAmelCase       ,   sampling_rate=self.feature_extractor.sampling_rate       ,   return_tensors="""pt"""     )
        return processed





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							int:
        lowerCamelCase											=self.model(**_UpperCAmelCase     )
        return model_outputs





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase=5     )		->							Tuple:
        lowerCamelCase											=model_outputs.logits[0].softmax(-1     )
        lowerCamelCase							,					lowerCamelCase											=probs.topk(_UpperCAmelCase     )

        lowerCamelCase											=scores.tolist()
        lowerCamelCase											=ids.tolist()

        lowerCamelCase											=[{"""score""": score, """label""": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase       ,   _UpperCAmelCase     )]

        return labels