



'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
   import torch


if is_vision_available():
   from PIL import Image

   from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  a_				   =ViTImageProcessor if is_vision_available() else None




  @property
  def 		_lowercase	(					self :      Any  )   ->   Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Optional[Any]       			=					(3, 32, 128)
    __lowerCamelCase			:			Tuple       			=					tempfile.mkdtemp()

    # fmt: off
    __lowerCamelCase			:			int       			=					['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
    # fmt: on
    __lowerCamelCase			:			str       			=					dict(zip(_a     ,      range(len(_a  )  )  )  )

    __lowerCamelCase			:			int       			=					os.path.join(self.tmpdirname     ,      VOCAB_FILES_NAMES['vocab_file']  )
    with open(self.vocab_file     ,      'w'     ,      encoding='utf-8'  ) as fp:
      fp.write(json.dumps(_a  ) + '\n'  )

    __lowerCamelCase			:			str       			=					{
        'do_normalize': False,
        'do_resize': True,
        'image_processor_type': 'ViTImageProcessor',
        'resample': 3,
        'size': {'height': 32, 'width': 128},
    }
    __lowerCamelCase			:			Optional[Any]       			=					os.path.join(self.tmpdirname     ,      _a  )
    with open(self.image_processor_file     ,      'w'     ,      encoding='utf-8'  ) as fp:
      json.dump(_a     ,      _a  )




  def 		_lowercase	(					self :      List[str]     ,      **_a :      int  )   ->   int:
    return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,      **_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      **_a :      List[str]  )   ->   Optional[int]:
    return ViTImageProcessor.from_pretrained(self.tmpdirname     ,      **_a  )




  def 		_lowercase	(					self :      Any  )   ->   str:
    shutil.rmtree(self.tmpdirname  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    __lowerCamelCase			:			Tuple       			=					np.random.randint(255     ,      size=(3, 30, 400)     ,      dtype=np.uinta  )

    __lowerCamelCase			:			int       			=					Image.fromarray(np.moveaxis(_a     ,      0     ,      -1  )  )

    return image_input




  def 		_lowercase	(					self :      Any  )   ->   Dict:
    __lowerCamelCase			:			List[Any]       			=					self.get_tokenizer()
    __lowerCamelCase			:			Union[str, Any]       			=					self.get_image_processor()

    __lowerCamelCase			:			str       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )
    processor.save_pretrained(self.tmpdirname  )
    __lowerCamelCase			:			List[str]       			=					MgpstrProcessor.from_pretrained(self.tmpdirname     ,      use_fast=_a  )

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

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




  def 		_lowercase	(					self :      Dict  )   ->   Optional[int]:
    __lowerCamelCase			:			Tuple       			=					self.get_tokenizer()
    __lowerCamelCase			:			Tuple       			=					self.get_image_processor()

    __lowerCamelCase			:			str       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )
    processor.save_pretrained(self.tmpdirname  )

    __lowerCamelCase			:			Any       			=					self.get_tokenizer(bos_token='(BOS)'     ,      eos_token='(EOS)'  )
    __lowerCamelCase			:			Optional[Any]       			=					self.get_image_processor(do_normalize=_a     ,      padding_value=1.0  )

    __lowerCamelCase			:			List[str]       			=					MgpstrProcessor.from_pretrained(
        self.tmpdirname     ,      bos_token='(BOS)'     ,      eos_token='(EOS)'     ,      do_normalize=_a     ,      padding_value=1.0  )

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

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




  def 		_lowercase	(					self :      Tuple  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					self.get_image_processor()
    __lowerCamelCase			:			Optional[Any]       			=					self.get_tokenizer()

    __lowerCamelCase			:			Union[str, Any]       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			List[str]       			=					self.prepare_image_inputs()

    __lowerCamelCase			:			str       			=					image_processor(_a     ,      return_tensors='np'  )
    __lowerCamelCase			:			int       			=					processor(images=_a     ,      return_tensors='np'  )

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




  def 		_lowercase	(					self :      str  )   ->   Optional[Any]:
    __lowerCamelCase			:			Optional[int]       			=					self.get_image_processor()
    __lowerCamelCase			:			Union[str, Any]       			=					self.get_tokenizer()

    __lowerCamelCase			:			Union[str, Any]       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			Tuple       			=					'test'

    __lowerCamelCase			:			Dict       			=					processor(text=_a  )

    __lowerCamelCase			:			List[Any]       			=					tokenizer(_a  )
    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key]     ,      encoded_processor[key]  )




  def 		_lowercase	(					self :      List[Any]  )   ->   Tuple:
    __lowerCamelCase			:			str       			=					self.get_image_processor()
    __lowerCamelCase			:			Tuple       			=					self.get_tokenizer()

    __lowerCamelCase			:			List[str]       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			List[str]       			=					'test'
    __lowerCamelCase			:			Optional[Any]       			=					self.prepare_image_inputs()

    __lowerCamelCase			:			Optional[Any]       			=					processor(text=_a     ,      images=_a  )

    self.assertListEqual(list(inputs.keys()  )     ,      ['pixel_values', 'labels']  )

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




  def 		_lowercase	(					self :      Optional[int]  )   ->   List[Any]:
    __lowerCamelCase			:			Optional[Any]       			=					self.get_image_processor()
    __lowerCamelCase			:			Tuple       			=					self.get_tokenizer()

    __lowerCamelCase			:			List[Any]       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			List[str]       			=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    __lowerCamelCase			:			Tuple       			=					processor.char_decode(_a  )
    __lowerCamelCase			:			Union[str, Any]       			=					tokenizer.batch_decode(_a  )
    __lowerCamelCase			:			int       			=					[seq.replace(' '     ,      ''  ) for seq in decoded_tok]

    self.assertListEqual(_a     ,      _a  )




  def 		_lowercase	(					self :      Tuple  )   ->   Tuple:
    __lowerCamelCase			:			List[Any]       			=					self.get_image_processor()
    __lowerCamelCase			:			int       			=					self.get_tokenizer()

    __lowerCamelCase			:			Tuple       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			int       			=					None
    __lowerCamelCase			:			List[str]       			=					self.prepare_image_inputs()

    __lowerCamelCase			:			Tuple       			=					processor(text=_a     ,      images=_a  )

    self.assertListEqual(list(inputs.keys()  )     ,      processor.model_input_names  )




  def 		_lowercase	(					self :      List[str]  )   ->   List[str]:
    __lowerCamelCase			:			Optional[Any]       			=					self.get_image_processor()
    __lowerCamelCase			:			Optional[Any]       			=					self.get_tokenizer()

    __lowerCamelCase			:			Union[str, Any]       			=					MgpstrProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			List[str]       			=					torch.randn(1     ,      27     ,      38  )
    __lowerCamelCase			:			int       			=					torch.randn(1     ,      27     ,      5_0257  )
    __lowerCamelCase			:			Tuple       			=					torch.randn(1     ,      27     ,      3_0522  )

    __lowerCamelCase			:			str       			=					processor.batch_decode([char_input, bpe_input, wp_input]  )

    self.assertListEqual(list(results.keys()  )     ,      ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds']  )











'''simple docstring'''





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


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






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          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'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

    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=_a     ,      segmentation_processor=_a     ,      vae=_a     ,      text_encoder=_a     ,      tokenizer=_a     ,      unet=_a     ,      scheduler=_a     ,      safety_checker=_a     ,      feature_extractor=_a     ,      )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Union[str, int]] = "auto"  )   ->   List[str]:
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      __lowerCamelCase			:			List[Any]       			=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('Please install accelerate via `pip install accelerate`'  )

    __lowerCamelCase			:			Dict       			=					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(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_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 :      List[str]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

    # Run inpainting pipeline with the generated mask
    __lowerCamelCase			:			Tuple       			=					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=_a     ,      image=_a     ,      mask_image=_a     ,      height=_a     ,      width=_a     ,      num_inference_steps=_a     ,      guidance_scale=_a     ,      negative_prompt=_a     ,      num_images_per_prompt=_a     ,      eta=_a     ,      generator=_a     ,      latents=_a     ,      output_type=_a     ,      return_dict=_a     ,      callback=_a     ,      callback_steps=_a     ,      )






