





"""simple docstring"""





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__				:Optional[Any]				   =       transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[int]:





  """simple docstring"""


  if isinstance(UpperCAmelCase_  ,			torch.Tensor  ):
    return image
  elif isinstance(UpperCAmelCase_  ,			PIL.Image.Image  ):
    __UpperCAmelCase    :						Optional[int]       =						[image]

  __UpperCAmelCase    :						List[str]       =						[trans(img.convert('''RGB'''  )  ) for img in image]
  __UpperCAmelCase    :						Optional[Any]       =						torch.stack(UpperCAmelCase_  )
  return image







class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      Any						,				__lowercase       :      int						,				__lowercase       :      List[Any] ):


        '''simple docstring'''




        super().__init__()

        # make sure scheduler can always be converted to DDIM
        __UpperCAmelCase    :						Optional[int]       =						DDIMScheduler.from_config(scheduler.config )

        self.register_modules(unet=__lowercase						,				scheduler=__lowercase )

      def       A_					(							self       :      Dict						,				__lowercase       :      List[Any] ):


        '''simple docstring'''




        if strength < 0 or strength > 1:
          raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}''' )

      def       A_					(							self       :      Dict						,				__lowercase       :      Any						,				__lowercase       :      Optional[int]						,				__lowercase       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						min(int(num_inference_steps * strength )						,				__lowercase )

        __UpperCAmelCase    :						Any       =						max(num_inference_steps - init_timestep						,				0 )
        __UpperCAmelCase    :						Tuple       =						self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start

      def       A_					(							self       :      List[str]						,				__lowercase       :      int						,				__lowercase       :      List[Any]						,				__lowercase       :      Tuple						,				__lowercase       :      str						,				__lowercase       :      Optional[int]						,				__lowercase       :      List[str]=None ):


        '''simple docstring'''




        if not isinstance(__lowercase						,				(torch.Tensor, PIL.Image.Image, list) ):
          raise ValueError(
              f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowercase )}''' )

        __UpperCAmelCase    :						List[str]       =						image.to(device=__lowercase						,				dtype=__lowercase )

        if isinstance(__lowercase						,				__lowercase ) and len(__lowercase ) != batch_size:
          raise ValueError(
              f'''You have passed a list of generators of length {len(__lowercase )}, but requested an effective batch'''
              f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )

        __UpperCAmelCase    :						List[Any]       =						init_latents.shape
        __UpperCAmelCase    :						Union[str, Any]       =						randn_tensor(__lowercase						,				generator=__lowercase						,				device=__lowercase						,				dtype=__lowercase )

        # get latents
        print('''add noise to latents at timestep'''						,				__lowercase )
        __UpperCAmelCase    :						Any       =						self.scheduler.add_noise(__lowercase						,				__lowercase						,				__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						init_latents

        return latents







      @torch.no_grad()
      def __call__(							self       :      Any						,				__lowercase       :      Union[torch.FloatTensor, PIL.Image.Image] = None						,				__lowercase       :      float = 0.8						,				__lowercase       :      int = 1						,				__lowercase       :      Optional[Union[torch.Generator, List[torch.Generator]]] = None						,				__lowercase       :      float = 0.0						,				__lowercase       :      int = 50						,				__lowercase       :      Optional[bool] = None						,				__lowercase       :      Optional[str] = "pil"						,				__lowercase       :      bool = True						,				):


        '''simple docstring'''




        self.check_inputs(__lowercase )

        # 2. Preprocess image
        __UpperCAmelCase    :						List[str]       =						preprocess(__lowercase )

        # 3. set timesteps
        self.scheduler.set_timesteps(__lowercase						,				device=self.device )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						self.get_timesteps(__lowercase						,				__lowercase						,				self.device )
        __UpperCAmelCase    :						Tuple       =						timesteps[:1].repeat(__lowercase )

        # 4. Prepare latent variables
        __UpperCAmelCase    :						Union[str, Any]       =						self.prepare_latents(__lowercase						,				__lowercase						,				__lowercase						,				self.unet.dtype						,				self.device						,				__lowercase )
        __UpperCAmelCase    :						str       =						latents

        # 5. Denoising loop
        for t in self.progress_bar(__lowercase ):
          # 1. predict noise model_output
          __UpperCAmelCase    :						List[Any]       =						self.unet(__lowercase						,				__lowercase ).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          __UpperCAmelCase    :						List[Any]       =						self.scheduler.step(
              __lowercase						,				__lowercase						,				__lowercase						,				eta=__lowercase						,				use_clipped_model_output=__lowercase						,				generator=__lowercase						,				).prev_sample

        __UpperCAmelCase    :						str       =						(image / 2 + 0.5).clamp(0						,				1 )
        __UpperCAmelCase    :						List[Any]       =						image.cpu().permute(0						,				2						,				3						,				1 ).numpy()
        if output_type == "pil":
          __UpperCAmelCase    :						Optional[int]       =						self.numpy_to_pil(__lowercase )

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=__lowercase )






"""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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						tempfile.mkdtemp()

        # fmt: off
        __UpperCAmelCase    :						Optional[int]       =						['''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
        __UpperCAmelCase    :						List[str]       =						dict(zip(__lowercase						,				range(len(__lowercase ) ) ) )
        __UpperCAmelCase    :						Any       =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        __UpperCAmelCase    :						Dict       =						{'''unk_token''': '''<unk>'''}

        __UpperCAmelCase    :						Optional[int]       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''vocab_file'''] )
        __UpperCAmelCase    :						Any       =						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 ) )

        __UpperCAmelCase    :						List[str]       =						{
            '''do_resize''': True,
            '''size''': 20,
            '''do_center_crop''': True,
            '''crop_size''': 18,
            '''do_normalize''': True,
            '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
        }
        __UpperCAmelCase    :						Any       =						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       :      Optional[Any]						,				**__lowercase       :      Tuple ):


        '''simple docstring'''




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

      def       A_					(							self       :      Any						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




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

      def       A_					(							self       :      Dict						,				**__lowercase       :      str ):


        '''simple docstring'''




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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        shutil.rmtree(self.tmpdirname )

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


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						[np.random.randint(255						,				size=(3, 30, 400)						,				dtype=np.uinta )]

        __UpperCAmelCase    :						int       =						[Image.fromarray(np.moveaxis(__lowercase						,				0						,				-1 ) ) for x in image_inputs]

        return image_inputs

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_tokenizer()
        __UpperCAmelCase    :						Dict       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Optional[Any]       =						self.get_image_processor()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_slow.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor.from_pretrained(self.tmpdirname						,				use_fast=__lowercase )

        __UpperCAmelCase    :						Tuple       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_fast.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						str       =						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       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

        __UpperCAmelCase    :						Any       =						self.get_tokenizer(bos_token='''(BOS)'''						,				eos_token='''(EOS)''' )
        __UpperCAmelCase    :						Tuple       =						self.get_image_processor(do_normalize=__lowercase						,				padding_value=1.0 )

        __UpperCAmelCase    :						List[str]       =						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       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.get_image_processor()
        __UpperCAmelCase    :						List[str]       =						self.get_tokenizer()

        __UpperCAmelCase    :						Optional[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						List[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Dict       =						image_processor(__lowercase						,				return_tensors='''np''' )
        __UpperCAmelCase    :						Optional[int]       =						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'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						int       =						'''lower newer'''

        __UpperCAmelCase    :						int       =						processor(text=__lowercase )

        __UpperCAmelCase    :						Any       =						tokenizer(__lowercase )

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Union[str, Any]       =						self.get_tokenizer()

        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						str       =						'''lower newer'''
        __UpperCAmelCase    :						int       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						str       =						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'''




        __UpperCAmelCase    :						Dict       =						self.get_image_processor()
        __UpperCAmelCase    :						str       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						self.prepare_image_inputs()
        __UpperCAmelCase    :						Optional[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Tuple       =						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       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Dict       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase    :						int       =						processor.batch_decode(__lowercase )
        __UpperCAmelCase    :						int       =						tokenizer.batch_decode(__lowercase )

        self.assertListEqual(__lowercase						,				__lowercase )