




"""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 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							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(_a	,				range(len(_a				)				)				)				)
							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(_a				) + """\n"""				)
							with open(self.merges_file	,				"""w"""	,				encoding="""utf-8"""				) as fp:
										fp.write("""\n""".join(_a				)				)

							lowerCamelCase       =      {
							    """do_resize""": True,
							    """size""": 20,
							    """do_center_crop""": True,
							    """crop_size""": 18,
							    """do_normalize""": True,
							    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
							    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
							}
							lowerCamelCase       =      os.path.join(self.tmpdirname	,				_a				)
							with open(self.image_processor_file	,				"""w"""	,				encoding="""utf-8"""				) as fp:
										json.dump(_a	,				_a				)





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							return CLIPTokenizer.from_pretrained(self.tmpdirname	,				**_a				)





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,				**_a				)





				def      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,				**_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							shutil.rmtree(self.tmpdirname				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      [np.random.randint(255	,				size=(3, 30, 400)	,				dtype=np.uinta				)]

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

							return image_inputs





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_tokenizer()
							lowerCamelCase       =      self.get_rust_tokenizer()
							lowerCamelCase       =      self.get_image_processor()

							lowerCamelCase       =      CLIPSegProcessor(tokenizer=_a	,				image_processor=_a				)
							processor_slow.save_pretrained(self.tmpdirname				)
							lowerCamelCase       =      CLIPSegProcessor.from_pretrained(self.tmpdirname	,				use_fast=_a				)

							lowerCamelCase       =      CLIPSegProcessor(tokenizer=_a	,				image_processor=_a				)
							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	,				_a				)
							self.assertIsInstance(processor_fast.tokenizer	,				_a				)

							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	,				_a				)
							self.assertIsInstance(processor_fast.image_processor	,				_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							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=_a	,				padding_value=1.0				)

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

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

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_image_processor()
							lowerCamelCase       =      self.get_tokenizer()

							lowerCamelCase       =      CLIPSegProcessor(tokenizer=_a	,				image_processor=_a				)

							lowerCamelCase       =      self.prepare_image_inputs()

							lowerCamelCase       =      image_processor(_a	,				return_tensors="""np"""				)
							lowerCamelCase       =      processor(images=_a	,				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      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_image_processor()
							lowerCamelCase       =      self.get_tokenizer()

							lowerCamelCase       =      CLIPSegProcessor(tokenizer=_a	,				image_processor=_a				)

							lowerCamelCase       =      """lower newer"""

							lowerCamelCase       =      processor(text=_a				)

							lowerCamelCase       =      tokenizer(_a				)

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_image_processor()
							lowerCamelCase       =      self.get_tokenizer()

							lowerCamelCase       =      CLIPSegProcessor(tokenizer=_a	,				image_processor=_a				)

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

							lowerCamelCase       =      processor(text=_a	,				images=_a				)

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

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_image_processor()
							lowerCamelCase       =      self.get_tokenizer()

							lowerCamelCase       =      CLIPSegProcessor(tokenizer=_a	,				image_processor=_a				)

							lowerCamelCase       =      self.prepare_image_inputs()
							lowerCamelCase       =      self.prepare_image_inputs()

							lowerCamelCase       =      processor(images=_a	,				visual_prompt=_a				)

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

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





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.get_image_processor()
							lowerCamelCase       =      self.get_tokenizer()

							lowerCamelCase       =      CLIPSegProcessor(tokenizer=_a	,				image_processor=_a				)

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

							lowerCamelCase       =      processor.batch_decode(_a				)
							lowerCamelCase       =      tokenizer.batch_decode(_a				)

							self.assertListEqual(_a	,				_a				)









"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					lowerCAmelCase							:    Dict  							=							argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    """--original_config_file""",
					    default=None,
					    type=str,
					    help="""The YAML config file corresponding to the original architecture.""",
					)
					parser.add_argument(
					    """--num_in_channels""",
					    default=None,
					    type=int,
					    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
					)
					parser.add_argument(
					    """--scheduler_type""",
					    default="""pndm""",
					    type=str,
					    help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
					)
					parser.add_argument(
					    """--pipeline_type""",
					    default=None,
					    type=str,
					    help=(
					        """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
					        """. If `None` pipeline will be automatically inferred."""
					    ),
					)
					parser.add_argument(
					    """--image_size""",
					    default=None,
					    type=int,
					    help=(
					        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
					        """ Base. Use 768 for Stable Diffusion v2."""
					    ),
					)
					parser.add_argument(
					    """--prediction_type""",
					    default=None,
					    type=str,
					    help=(
					        """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
					        """ Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."""
					    ),
					)
					parser.add_argument(
					    """--extract_ema""",
					    action="""store_true""",
					    help=(
					        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
					        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
					        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
					    ),
					)
					parser.add_argument(
					    """--upcast_attention""",
					    action="""store_true""",
					    help=(
					        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
					        """ diffusion 2.1."""
					    ),
					)
					parser.add_argument(
					    """--from_safetensors""",
					    action="""store_true""",
					    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
					)
					parser.add_argument(
					    """--to_safetensors""",
					    action="""store_true""",
					    help="""Whether to store pipeline in safetensors format or not.""",
					)
					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
					parser.add_argument(
					    """--stable_unclip""",
					    type=str,
					    default=None,
					    required=False,
					    help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
					)
					parser.add_argument(
					    """--stable_unclip_prior""",
					    type=str,
					    default=None,
					    required=False,
					    help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
					)
					parser.add_argument(
					    """--clip_stats_path""",
					    type=str,
					    help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
					    required=False,
					)
					parser.add_argument(
					    """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
					)
					parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
					parser.add_argument(
					    """--vae_path""",
					    type=str,
					    default=None,
					    required=False,
					    help="""Set to a path, hub id to an already converted vae to not convert it again.""",
					)
					lowerCAmelCase							:    Dict  							=							parser.parse_args()

					lowerCAmelCase							:    str  							=							download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



