





import argparse

import requests
import torch
from PIL import Image

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






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					CLIPSegTextConfig()
		SCREAMING_SNAKE_CASE__								=					CLIPSegVisionConfig(patch_size=16			)

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

		SCREAMING_SNAKE_CASE__								=					CLIPSegConfig.from_text_vision_configs(
		    UpperCamelCase__    ,      UpperCamelCase__    ,      use_complex_transposed_convolution=UpperCamelCase__    ,      reduce_dim=UpperCamelCase__    ,      )
		return config






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

		return name






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							Optional[int]			):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE__								=					orig_state_dict.pop(UpperCamelCase__			)

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

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

		return orig_state_dict






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






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							Dict    ,      UpperCamelCase__:							int			):
		SCREAMING_SNAKE_CASE__								=					get_clipseg_config(UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__								=					CLIPSegForImageSegmentation(UpperCamelCase__			)
		model.eval()

		SCREAMING_SNAKE_CASE__								=					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
		SCREAMING_SNAKE_CASE__								=					convert_state_dict(UpperCamelCase__    ,      UpperCamelCase__			)
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					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}'''			)

		SCREAMING_SNAKE_CASE__								=					ViTImageProcessor(size=352			)
		SCREAMING_SNAKE_CASE__								=					CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""			)
		SCREAMING_SNAKE_CASE__								=					CLIPSegProcessor(image_processor=UpperCamelCase__    ,      tokenizer=UpperCamelCase__			)

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

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

		with torch.no_grad():
				SCREAMING_SNAKE_CASE__								=					model(**UpperCamelCase__			)

		# verify values
		SCREAMING_SNAKE_CASE__								=					torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]			)
		SCREAMING_SNAKE_CASE__								=					torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]			)
		if model_name == "clipseg-rd64-refined":
				SCREAMING_SNAKE_CASE__								=					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":
				SCREAMING_SNAKE_CASE__								=					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":
				SCREAMING_SNAKE_CASE__								=					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]    ,      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__":
	_lowerCamelCase       =	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.'
	)

	_lowerCamelCase       =	parser.parse_args()
	convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
	import torch




class      UpperCamelCase_				(   unittest.TestCase ,     UpperCamelCase__			):


							def    _snake_case     (							self			:Dict						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					load_tool("""speech-to-text"""						)
									self.tool.setup()


							def    _snake_case     (							self			:str						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)


							def    _snake_case     (							self			:Union[str, Any]						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool(audio=torch.ones(3000						)						)
									self.assertEqual(__A							, """ you"""						)