




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











'''simple docstring'''







import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	lowerCamelCase_				(			UpperCAmelCase_,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					PreTrainedTokenizerFast
					lowercase__       =					False
					lowercase__       =					True
					lowercase__       =					"""tokenizer_file"""






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			False  # because we don't have pretrained_vocab_files_map
							super().setUp()
							_snake_case      			=			True

							_snake_case      			=			["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
							_snake_case      			=			"SaulLu/dummy-tokenizer-bytelevel-bpe"

							# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
							_snake_case      			=			[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained(model_paths[0]					)
							tokenizer.save_pretrained(self.tmpdirname					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer_with_special_tokens_change()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name					)

							_snake_case      			=			("a" for _ in range(1_000					))
							_snake_case      			=			tokenizer.train_new_from_iterator(text_iterator=__UpperCamelCase,    length=1_000,    vocab_size=50					)

							_snake_case      			=			new_tokenizer.encode("a🤗"					)
							self.assertEqual(__UpperCamelCase,    [64, 172, 253, 97, 245]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			AutoTokenizer.from_pretrained("bert-base-cased"					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							_snake_case      			=			len(__UpperCamelCase					)

							with tempfile.TemporaryDirectory() as tmp_dir:
									# Hack to save this in the tokenizer_config.json
									_snake_case      			=			["tokenizer.4.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									json.dump(__UpperCamelCase,    open(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    "w"					)					)

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					) + 1					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

									# Will need to be adjusted if we reach v42 and this test is still here.
									# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
									shutil.move(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    os.path.join(__UpperCamelCase,    "tokenizer.42.0.0.json"					)					)
									_snake_case      			=			["tokenizer.42.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					)					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
							_snake_case      			=			"hf-internal-testing/test-two-tokenizers"

							# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
							_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_997					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

							# Testing an older version by monkey-patching the version in the module it's used.
							import transformers as old_transformers

							_snake_case      			=			"3.0.0"
							_snake_case      			=			old_transformers.models.auto.AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_996					)
							_snake_case      			=			json.loads(old_tokenizer._tokenizer.to_str()					)
							self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# See https://github.com/huggingface/transformers/pull/12550
							# and https://github.com/huggingface/tokenizers/issues/537
							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"					)
							_snake_case      			=			"The Matrix is a 1999 science fiction action film."

							with concurrent.futures.ThreadPoolExecutor() as executor:
									_snake_case      			=			[executor.submit(self.fetch,    __UpperCamelCase,    __UpperCamelCase					) for i in range(10					)]
									_snake_case      			=			[future.result() for future in futures]
									self.assertEqual(__UpperCamelCase,    [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10					)]					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							return tokenizer.encode(__UpperCamelCase,    truncation="longest_first",    padding="longest"					)





