

"""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)








"""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





