






'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  tempfile.mkdtemp()

								# fmt: off
								lowerCamelCase__         =  ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
								# fmt: on
								lowerCamelCase__         =  dict(zip(_lowerCAmelCase   ,range(len(_lowerCAmelCase       )       )       )       )
								lowerCamelCase__         =  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
								lowerCamelCase__         =  {"""unk_token""": """<unk>"""}

								lowerCamelCase__         =  os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["""vocab_file"""]       )
								lowerCamelCase__         =  os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["""merges_file"""]       )
								with open(self.vocab_file   ,"""w"""   ,encoding="""utf-8"""       ) as fp:
														fp.write(json.dumps(_lowerCAmelCase       ) + """\n"""       )
								with open(self.merges_file   ,"""w"""   ,encoding="""utf-8"""       ) as fp:
														fp.write("""\n""".join(_lowerCAmelCase       )       )

								lowerCamelCase__         =  {
								    """do_resize""": True,
								    """size""": 20,
								    """do_center_crop""": True,
								    """crop_size""": 18,
								    """do_normalize""": True,
								    """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
								    """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
								}
								lowerCamelCase__         =  os.path.join(self.tmpdirname   ,_lowerCAmelCase       )
								with open(self.image_processor_file   ,"""w"""   ,encoding="""utf-8"""       ) as fp:
														json.dump(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return CLIPTokenizer.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return ViTImageProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								shutil.rmtree(self.tmpdirname       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [np.random.randint(2_55   ,size=(3, 30, 4_00)   ,dtype=np.uinta       )]

								lowerCamelCase__         =  [Image.fromarray(np.moveaxis(_lowerCAmelCase   ,0   ,-1       )       ) for x in image_inputs]

								return image_inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_tokenizer()
								lowerCamelCase__         =  self.get_rust_tokenizer()
								lowerCamelCase__         =  self.get_image_processor()

								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )
								processor_slow.save_pretrained(self.tmpdirname       )
								lowerCamelCase__         =  CLIPSegProcessor.from_pretrained(self.tmpdirname   ,use_fast=_lowerCAmelCase       )

								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )
								processor_fast.save_pretrained(self.tmpdirname       )
								lowerCamelCase__         =  CLIPSegProcessor.from_pretrained(self.tmpdirname       )

								self.assertEqual(processor_slow.tokenizer.get_vocab()   ,tokenizer_slow.get_vocab()       )
								self.assertEqual(processor_fast.tokenizer.get_vocab()   ,tokenizer_fast.get_vocab()       )
								self.assertEqual(tokenizer_slow.get_vocab()   ,tokenizer_fast.get_vocab()       )
								self.assertIsInstance(processor_slow.tokenizer   ,_lowerCAmelCase       )
								self.assertIsInstance(processor_fast.tokenizer   ,_lowerCAmelCase       )

								self.assertEqual(processor_slow.image_processor.to_json_string()   ,image_processor.to_json_string()       )
								self.assertEqual(processor_fast.image_processor.to_json_string()   ,image_processor.to_json_string()       )
								self.assertIsInstance(processor_slow.image_processor   ,_lowerCAmelCase       )
								self.assertIsInstance(processor_fast.image_processor   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								lowerCamelCase__         =  self.get_tokenizer(bos_token="""(BOS)"""   ,eos_token="""(EOS)"""       )
								lowerCamelCase__         =  self.get_image_processor(do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								lowerCamelCase__         =  CLIPSegProcessor.from_pretrained(
								    self.tmpdirname   ,bos_token="""(BOS)"""   ,eos_token="""(EOS)"""   ,do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()       )
								self.assertIsInstance(processor.tokenizer   ,_lowerCAmelCase       )

								self.assertEqual(processor.image_processor.to_json_string()   ,image_processor_add_kwargs.to_json_string()       )
								self.assertIsInstance(processor.image_processor   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  image_processor(_lowerCAmelCase   ,return_tensors="""np"""       )
								lowerCamelCase__         =  processor(images=_lowerCAmelCase   ,return_tensors="""np"""       )

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1E-2       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""

								lowerCamelCase__         =  processor(text=_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer(_lowerCAmelCase       )

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								self.assertListEqual(list(inputs.keys()       )   ,["""input_ids""", """attention_mask""", """pixel_values"""]       )

								# test if it raises when no input is passed
								with pytest.raises(_lowerCAmelCase       ):
														processor()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  self.prepare_image_inputs()
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(images=_lowerCAmelCase   ,visual_prompt=_lowerCAmelCase       )

								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """conditional_pixel_values"""]       )

								# test if it raises when no input is passed
								with pytest.raises(_lowerCAmelCase       ):
														processor()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  CLIPSegProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__         =  processor.batch_decode(_lowerCAmelCase       )
								lowerCamelCase__         =  tokenizer.batch_decode(_lowerCAmelCase       )

								self.assertListEqual(_lowerCAmelCase   ,_lowerCAmelCase       )









'''simple docstring'''







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  OmegaConf.load(__lowerCAmelCase						)
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)["""model"""]
						lowerCamelCase__         =  list(state_dict.keys()						)

						# extract state_dict for VQVAE
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """first_stage_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

    # extract state_dict for UNetLDM
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  """model.diffusion_model."""
						for key in keys:
												if key.startswith(__lowerCAmelCase						):
																		lowerCamelCase__         =  state_dict[key]

						lowerCamelCase__         =  config.model.params.first_stage_config.params
						lowerCamelCase__         =  config.model.params.unet_config.params

						lowerCamelCase__         =  VQModel(**__lowerCAmelCase						).eval()
						vqvae.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  UNetLDMModel(**__lowerCAmelCase						).eval()
						unet.load_state_dict(__lowerCAmelCase						)

						lowerCamelCase__         =  DDIMScheduler(
						    timesteps=config.model.params.timesteps   ,    beta_schedule="""scaled_linear"""   ,    beta_start=config.model.params.linear_start   ,    beta_end=config.model.params.linear_end   ,    clip_sample=__lowerCAmelCase   ,    )

						lowerCamelCase__         =  LDMPipeline(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)
						pipeline.save_pretrained(__lowerCAmelCase						)


if __name__ == "__main__":
							UpperCamelCase    :      Optional[int]  			=		argparse.ArgumentParser()
							parser.add_argument('--checkpoint_path', type=str, required=True)
							parser.add_argument('--config_path', type=str, required=True)
							parser.add_argument('--output_path', type=str, required=True)
							UpperCamelCase    :      List[Any]  			=		parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

