




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ViltImageProcessor





class 					lowerCamelCase_							(	unittest.TestCase							):




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase=7,  __UpperCamelCase=3,  __UpperCamelCase=18,  __UpperCamelCase=30,  __UpperCamelCase=400,  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=2,  __UpperCamelCase=True,  __UpperCamelCase=[0.5, 0.5, 0.5],  __UpperCamelCase=[0.5, 0.5, 0.5],  ):
							_snake_case     	=	size if size is not None else {"shortest_edge": 30}
							_snake_case     	=	parent
							_snake_case     	=	batch_size
							_snake_case     	=	num_channels
							_snake_case     	=	image_size
							_snake_case     	=	min_resolution
							_snake_case     	=	max_resolution
							_snake_case     	=	do_resize
							_snake_case     	=	size
							_snake_case     	=	size_divisor
							_snake_case     	=	do_normalize
							_snake_case     	=	image_mean
							_snake_case     	=	image_std






					def     __SCREAMING_SNAKE_CASE     (   self			):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase=False			):
							if not batched:
									_snake_case     	=	self.size["shortest_edge"]
									_snake_case     	=	image_inputs[0]
									if isinstance(__UpperCamelCase,  Image.Image			):
											_snake_case	,   _snake_case     	=	image.size
									else:
											_snake_case	,   _snake_case     	=	image.shape[1], image.shape[2]
									_snake_case     	=	size / min(__UpperCamelCase,  __UpperCamelCase			)
									if h < w:
											_snake_case	,   _snake_case     	=	size, scale * w
									else:
											_snake_case	,   _snake_case     	=	scale * h, size

									_snake_case     	=	int((1_333 / 800) * size			)
									if max(__UpperCamelCase,  __UpperCamelCase			) > max_size:
											_snake_case     	=	max_size / max(__UpperCamelCase,  __UpperCamelCase			)
											_snake_case     	=	newh * scale
											_snake_case     	=	neww * scale

									_snake_case	,   _snake_case     	=	int(newh + 0.5			), int(neww + 0.5			)
									_snake_case	,   _snake_case     	=	(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

							else:
									_snake_case     	=	[]
									for image in image_inputs:
											_snake_case	,   _snake_case     	=	self.get_expected_values([image]			)
											expected_values.append((expected_height, expected_width)			)
									_snake_case     	=	max(__UpperCamelCase,  key=lambda __UpperCamelCase			: item[0]			)[0]
									_snake_case     	=	max(__UpperCamelCase,  key=lambda __UpperCamelCase			: item[1]			)[1]

							return expected_height, expected_width





@require_torch
@require_vision
class 					lowerCamelCase_							(	UpperCAmelCase_,	unittest.TestCase							):




					'''simple docstring'''






					lowercase__   =			ViltImageProcessor if is_vision_available() else None






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	ViltImageProcessingTester(self			)






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							return self.image_processor_tester.prepare_image_processor_dict()






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(__UpperCamelCase,  "image_mean"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "image_std"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_normalize"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "do_resize"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "size"			)			)
							self.assertTrue(hasattr(__UpperCamelCase,  "size_divisor"			)			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.image_processing_class.from_dict(self.image_processor_dict			)
							self.assertEqual(image_processor.size,  {"shortest_edge": 30}			)

							_snake_case     	=	self.image_processing_class.from_dict(self.image_processor_dict,  size=42			)
							self.assertEqual(image_processor.size,  {"shortest_edge": 42}			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							pass






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# Initialize image_processing
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_snake_case     	=	prepare_image_inputs(self.image_processor_tester,  equal_resolution=__UpperCamelCase			)
							for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase,  Image.Image			)

							# Test not batched input
							_snake_case     	=	image_processing(image_inputs[0],  return_tensors="pt"			).pixel_values

							_snake_case	,   _snake_case     	=	self.image_processor_tester.get_expected_values(__UpperCamelCase			)
							self.assertEqual(
							    encoded_images.shape,  (1, self.image_processor_tester.num_channels, expected_height, expected_width),  )

							# Test batched
							_snake_case     	=	image_processing(__UpperCamelCase,  return_tensors="pt"			).pixel_values

							_snake_case	,   _snake_case     	=	self.image_processor_tester.get_expected_values(__UpperCamelCase,  batched=__UpperCamelCase			)
							self.assertEqual(
							    encoded_images.shape,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),  )






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# Initialize image_processing
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							_snake_case     	=	prepare_image_inputs(self.image_processor_tester,  equal_resolution=__UpperCamelCase,  numpify=__UpperCamelCase			)
							for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase,  np.ndarray			)

							# Test not batched input
							_snake_case     	=	image_processing(image_inputs[0],  return_tensors="pt"			).pixel_values

							_snake_case	,   _snake_case     	=	self.image_processor_tester.get_expected_values(__UpperCamelCase			)
							self.assertEqual(
							    encoded_images.shape,  (1, self.image_processor_tester.num_channels, expected_height, expected_width),  )

							# Test batched
							_snake_case     	=	image_processing(__UpperCamelCase,  return_tensors="pt"			).pixel_values

							_snake_case	,   _snake_case     	=	self.image_processor_tester.get_expected_values(__UpperCamelCase,  batched=__UpperCamelCase			)
							self.assertEqual(
							    encoded_images.shape,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),  )






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# Initialize image_processing
							_snake_case     	=	self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							_snake_case     	=	prepare_image_inputs(self.image_processor_tester,  equal_resolution=__UpperCamelCase,  torchify=__UpperCamelCase			)
							for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase,  torch.Tensor			)

							# Test not batched input
							_snake_case     	=	image_processing(image_inputs[0],  return_tensors="pt"			).pixel_values

							_snake_case	,   _snake_case     	=	self.image_processor_tester.get_expected_values(__UpperCamelCase			)
							self.assertEqual(
							    encoded_images.shape,  (1, self.image_processor_tester.num_channels, expected_height, expected_width),  )

							# Test batched
							_snake_case     	=	image_processing(__UpperCamelCase,  return_tensors="pt"			).pixel_values

							_snake_case	,   _snake_case     	=	self.image_processor_tester.get_expected_values(__UpperCamelCase,  batched=__UpperCamelCase			)
							self.assertEqual(
							    encoded_images.shape,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ),  )











'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

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






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	CLIPSegTextConfig()
		_snake_case     	=	CLIPSegVisionConfig(patch_size=16  )

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

		_snake_case     	=	CLIPSegConfig.from_text_vision_configs(
		    lowercase_						,							lowercase_						,							use_complex_transposed_convolution=lowercase_						,							reduce_dim=lowercase_						,							)
		return config






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







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

		return name






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				_snake_case     	=	orig_state_dict.pop(lowercase_  )

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

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

		return orig_state_dict






def 		_lowercase		(     ):



		'''simple docstring'''







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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	get_clipseg_config(lowercase_  )
		_snake_case     	=	CLIPSegForImageSegmentation(lowercase_  )
		model.eval()

		_snake_case     	=	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
		_snake_case     	=	convert_state_dict(lowercase_						,							lowercase_  )
		_snake_case	,   _snake_case     	=	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}'''  )

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

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

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

		with torch.no_grad():
				_snake_case     	=	model(**lowercase_  )

		# verify values
		_snake_case     	=	torch.tensor([0.1110, -0.1882, 0.1645]  )
		_snake_case     	=	torch.tensor([0.2692, -0.7197, -0.1328]  )
		if model_name == "clipseg-rd64-refined":
				_snake_case     	=	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":
				_snake_case     	=	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":
				_snake_case     	=	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]						,							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__":
						_lowerCamelCase							:						str							     =  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							:						Optional[int]							     =  parser.parse_args()
						convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





