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 __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      tempfile.mkdtemp()

     # fmt: off
     SCREAMING_SNAKE_CASE__										=      ["""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
     SCREAMING_SNAKE_CASE__										=      dict(zip(_lowercase       ,	range(len(_lowercase      )      )      )      )
     SCREAMING_SNAKE_CASE__										=      ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
     SCREAMING_SNAKE_CASE__										=      {"""unk_token""": """<unk>"""}

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

     SCREAMING_SNAKE_CASE__										=      {
         """do_resize""": True,
         """size""": 20,
         """do_center_crop""": True,
         """crop_size""": 18,
         """do_normalize""": True,
         """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
         """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
     }
     SCREAMING_SNAKE_CASE__										=      os.path.join(self.tmpdirname       ,	_lowercase      )
     with open(self.image_processor_file       ,	"""w"""       ,	encoding="""utf-8"""      ) as fp:
       json.dump(_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				int       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return CLIPTokenizer.from_pretrained(self.tmpdirname       ,	**_lowercase      )




   def 	__a						(    self			:				List[str]       ,	**_lowercase			:				int      ):



     """simple docstring"""






     return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,	**_lowercase      )




   def 	__a						(    self			:				Optional[Any]       ,	**_lowercase			:				List[Any]      ):



     """simple docstring"""






     return ViTImageProcessor.from_pretrained(self.tmpdirname       ,	**_lowercase      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname      )




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [np.random.randint(2_55       ,	size=(3, 30, 4_00)       ,	dtype=np.uinta      )]

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

     return image_inputs




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()
     SCREAMING_SNAKE_CASE__										=      self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()

     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=_lowercase       ,	image_processor=_lowercase      )
     processor_slow.save_pretrained(self.tmpdirname      )
     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor.from_pretrained(self.tmpdirname       ,	use_fast=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=_lowercase       ,	image_processor=_lowercase      )
     processor_fast.save_pretrained(self.tmpdirname      )
     SCREAMING_SNAKE_CASE__										=      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       ,	_lowercase      )
     self.assertIsInstance(processor_fast.tokenizer       ,	_lowercase      )

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




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,	image_processor=self.get_image_processor()      )
     processor.save_pretrained(self.tmpdirname      )

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

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

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

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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()
     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()

     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=_lowercase       ,	image_processor=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__										=      image_processor(_lowercase       ,	return_tensors="""np"""      )
     SCREAMING_SNAKE_CASE__										=      processor(images=_lowercase       ,	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 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()
     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()

     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=_lowercase       ,	image_processor=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      """lower newer"""

     SCREAMING_SNAKE_CASE__										=      processor(text=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      tokenizer(_lowercase      )

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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()
     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()

     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=_lowercase       ,	image_processor=_lowercase      )

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

     SCREAMING_SNAKE_CASE__										=      processor(text=_lowercase       ,	images=_lowercase      )

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

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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()
     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()

     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=_lowercase       ,	image_processor=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      self.prepare_image_inputs()
     SCREAMING_SNAKE_CASE__										=      self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__										=      processor(images=_lowercase       ,	visual_prompt=_lowercase      )

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

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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_image_processor()
     SCREAMING_SNAKE_CASE__										=      self.get_tokenizer()

     SCREAMING_SNAKE_CASE__										=      CLIPSegProcessor(tokenizer=_lowercase       ,	image_processor=_lowercase      )

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

     SCREAMING_SNAKE_CASE__										=      processor.batch_decode(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      tokenizer.batch_decode(_lowercase      )

     self.assertListEqual(_lowercase       ,	_lowercase      )

from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__lowerCamelCase			: str							   =		logging.get_logger(__name__)  # pylint: disable=invalid-name







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Dict       ,	_lowercase			:				CLIPSegForImageSegmentation       ,	_lowercase			:				CLIPSegProcessor       ,	_lowercase			:				AutoencoderKL       ,	_lowercase			:				CLIPTextModel       ,	_lowercase			:				CLIPTokenizer       ,	_lowercase			:				UNetaDConditionModel       ,	_lowercase			:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,	_lowercase			:				StableDiffusionSafetyChecker       ,	_lowercase			:				CLIPImageProcessor       ,	):



     """simple docstring"""






     super().__init__()

     if hasattr(scheduler.config       ,	"""steps_offset"""      ) and scheduler.config.steps_offset != 1:
       SCREAMING_SNAKE_CASE__										=      (
           f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
           f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
           """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
           """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
           """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
           """ file"""
       )
       deprecate("""steps_offset!=1"""       ,	"""1.0.0"""       ,	_lowercase       ,	standard_warn=_lowercase      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      1
       SCREAMING_SNAKE_CASE__										=      FrozenDict(_lowercase      )

     if hasattr(scheduler.config       ,	"""skip_prk_steps"""      ) and scheduler.config.skip_prk_steps is False:
       SCREAMING_SNAKE_CASE__										=      (
           f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
           """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
           """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
           """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
           """ Hub, it would be very nice if you could open a Pull request for the"""
           """ `scheduler/scheduler_config.json` file"""
       )
       deprecate("""skip_prk_steps not set"""       ,	"""1.0.0"""       ,	_lowercase       ,	standard_warn=_lowercase      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      True
       SCREAMING_SNAKE_CASE__										=      FrozenDict(_lowercase      )

     if safety_checker is None:
       logger.warning(
           f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
           """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
           """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
           """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
           """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
           """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""      )

     self.register_modules(
         segmentation_model=_lowercase       ,	segmentation_processor=_lowercase       ,	vae=_lowercase       ,	text_encoder=_lowercase       ,	tokenizer=_lowercase       ,	unet=_lowercase       ,	scheduler=_lowercase       ,	safety_checker=_lowercase       ,	feature_extractor=_lowercase       ,	)




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Union[str, int]] = "auto"      ):



     """simple docstring"""






     if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       SCREAMING_SNAKE_CASE__										=      self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(_lowercase      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     self.enable_attention_slicing(_lowercase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     if is_accelerate_available():
       from accelerate import cpu_offload
     else:
       raise ImportError("""Please install accelerate via `pip install accelerate`"""      )

     SCREAMING_SNAKE_CASE__										=      torch.device("""cuda"""      )

     for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
       if cpu_offloaded_model is not None:
         cpu_offload(_lowercase       ,	_lowercase      )




   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     if self.device != torch.device("""meta"""      ) or not hasattr(self.unet       ,	"""_hf_hook"""      ):
       return self.device
     for module in self.unet.modules():
       if (
           hasattr(_lowercase       ,	"""_hf_hook"""      )
           and hasattr(module._hf_hook       ,	"""execution_device"""      )
           and module._hf_hook.execution_device is not None
       ):
         return torch.device(module._hf_hook.execution_device      )
     return self.device




   @torch.no_grad()
   def __call__(    self			:				Optional[Any]       ,	_lowercase			:				Union[str, List[str]]       ,	_lowercase			:				Union[torch.FloatTensor, PIL.Image.Image]       ,	_lowercase			:				str       ,	_lowercase			:				int = 5_12       ,	_lowercase			:				int = 5_12       ,	_lowercase			:				int = 50       ,	_lowercase			:				float = 7.5       ,	_lowercase			:				Optional[Union[str, List[str]]] = None       ,	_lowercase			:				Optional[int] = 1       ,	_lowercase			:				float = 0.0       ,	_lowercase			:				Optional[torch.Generator] = None       ,	_lowercase			:				Optional[torch.FloatTensor] = None       ,	_lowercase			:				Optional[str] = "pil"       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,	_lowercase			:				int = 1       ,	**_lowercase			:				str       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.segmentation_processor(
         text=[text]       ,	images=[image]       ,	padding="""max_length"""       ,	return_tensors="""pt"""      ).to(self.device      )
     SCREAMING_SNAKE_CASE__										=      self.segmentation_model(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
     SCREAMING_SNAKE_CASE__										=      self.numpy_to_pil(_lowercase      )[0].resize(image.size      )

     # Run inpainting pipeline with the generated mask
     SCREAMING_SNAKE_CASE__										=      StableDiffusionInpaintPipeline(
         vae=self.vae       ,	text_encoder=self.text_encoder       ,	tokenizer=self.tokenizer       ,	unet=self.unet       ,	scheduler=self.scheduler       ,	safety_checker=self.safety_checker       ,	feature_extractor=self.feature_extractor       ,	)
     return inpainting_pipeline(
         prompt=_lowercase       ,	image=_lowercase       ,	mask_image=_lowercase       ,	height=_lowercase       ,	width=_lowercase       ,	num_inference_steps=_lowercase       ,	guidance_scale=_lowercase       ,	negative_prompt=_lowercase       ,	num_images_per_prompt=_lowercase       ,	eta=_lowercase       ,	generator=_lowercase       ,	latents=_lowercase       ,	output_type=_lowercase       ,	return_dict=_lowercase       ,	callback=_lowercase       ,	callback_steps=_lowercase       ,	)
