code: 
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""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(
  					    lowercase__    ,							lowercase__    ,							use_complex_transposed_convolution=lowercase__    ,							reduce_dim=lowercase__    ,							)
  					return config
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					# update prefixes
  					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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					for key in orig_state_dict.copy().keys():
  										A    =      orig_state_dict.pop(lowercase__   )
  
  										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(lowercase__   )
  															if "visual_projection" in new_name or "text_projection" in new_name:
  																				A    =      val.T
  															A    =      val
  
  					return orig_state_dict
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      "http://images.cocodataset.org/val2017/000000039769.jpg"
  					A    =      Image.open(requests.get(lowercase__    ,							stream=lowercase__   ).raw   )
  					return image
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      get_clipseg_config(lowercase__   )
  					A    =      CLIPSegForImageSegmentation(lowercase__   )
  					model.eval()
  
  					A    =      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
  					A    =      convert_state_dict(lowercase__    ,							lowercase__   )
  					A    ,					A    =      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}"""   )
  
  					A    =      ViTImageProcessor(size=352   )
  					A    =      CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"   )
  					A    =      CLIPSegProcessor(image_processor=lowercase__    ,							tokenizer=lowercase__   )
  
  					A    =      prepare_img()
  					A    =      ["a glass", "something to fill", "wood", "a jar"]
  
  					A    =      processor(text=lowercase__    ,							images=[image] * len(lowercase__   )    ,							padding="max_length"    ,							return_tensors="pt"   )
  
  					with torch.no_grad():
  										A    =      model(**lowercase__   )
  
  					# verify values
  					A    =      torch.tensor([0.11_10, -0.18_82, 0.16_45]   )
  					A    =      torch.tensor([0.26_92, -0.71_97, -0.13_28]   )
  					if model_name == "clipseg-rd64-refined":
  										A    =      torch.tensor(
  										    [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]   )
  					elif model_name == "clipseg-rd64":
  										A    =      torch.tensor(
  										    [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]   )
  					elif model_name == "clipseg-rd16":
  										A    =      torch.tensor(
  										    [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]   )
  					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__":
  	__A  :					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.'
  	)
  
  	__A  :					Optional[Any]												=	parser.parse_args()
  	convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  __A  :					List[str]												=	logging.get_logger(__name__)
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					"mt5"
  		SCREAMING_SNAKE_CASE              =					["past_key_values"]
  
  
  		def __init__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=2_5_0_1_1_2  ,  __SCREAMING_SNAKE_CASE				:	int=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6_4  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_0_2_4  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=8  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6  ,  __SCREAMING_SNAKE_CASE				:	int=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_2_8  ,  __SCREAMING_SNAKE_CASE				:	Tuple=0.1  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1E-6  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]="gated-gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=True  ,  __SCREAMING_SNAKE_CASE				:	str=True  ,  __SCREAMING_SNAKE_CASE				:	int="T5Tokenizer"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=False  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=0  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  **__SCREAMING_SNAKE_CASE				:	str  ,  ):
  							super().__init__(
  							    is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  tokenizer_class=__SCREAMING_SNAKE_CASE  ,  tie_word_embeddings=__SCREAMING_SNAKE_CASE  ,  pad_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE  ,  )
  							A    =      vocab_size
  							A    =      d_model
  							A    =      d_kv
  							A    =      d_ff
  							A    =      num_layers
  							A    =      (
  							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
  							)  # default = symmetry
  							A    =      num_heads
  							A    =      relative_attention_num_buckets
  							A    =      relative_attention_max_distance
  							A    =      dropout_rate
  							A    =      layer_norm_epsilon
  							A    =      initializer_factor
  							A    =      feed_forward_proj
  							A    =      use_cache
  
  							A    =      self.feed_forward_proj.split("-")
  							A    =      act_info[-1]
  							A    =      act_info[0] == "gated"
  
  							if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
  												raise ValueError(
  												    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
  												    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
  												    "'gated-gelu' or 'relu'")
  
  							# for backwards compatibility
  							if feed_forward_proj == "gated-gelu":
  												A    =      "gelu_new"
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							return self.d_model
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							return self.num_heads
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							return self.num_layers
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		@property
  		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							A    =      {
  							    "input_ids": {0: "batch", 1: "encoder_sequence"},
  							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
  							}
  							if self.use_past:
  												A    =      "past_encoder_sequence + sequence"
  												A    =      {0: "batch"}
  												A    =      {0: "batch", 1: "past_decoder_sequence + sequence"}
  							else:
  												A    =      {0: "batch", 1: "decoder_sequence"}
  												A    =      {0: "batch", 1: "decoder_sequence"}
  
  							if self.use_past:
  												self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,  direction="inputs")
  
  							return common_inputs
  
  
  		@property
  		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							return 1_3
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	int):
  							return 5E-4
  
  
  
  
  
  