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


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





class 		_lowercase						(					snake_case_       ):

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

  """simple docstring"""





  super().__init__()

  if hasattr(scheduler.config   ,      'steps_offset'			) and scheduler.config.steps_offset != 1:
   UpperCamelCase_					:						List[str] =					(
       f"The configuration file of this scheduler: {scheduler} is outdated.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'   ,      snake_case   ,      standard_warn=snake_case			)
   UpperCamelCase_					:						Union[str, Any] =					dict(scheduler.config			)
   UpperCamelCase_					:						int =					1
   UpperCamelCase_					:						int =					FrozenDict(snake_case			)

  if hasattr(scheduler.config   ,      'skip_prk_steps'			) and scheduler.config.skip_prk_steps is False:
   UpperCamelCase_					:						Dict =					(
       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'   ,      snake_case   ,      standard_warn=snake_case			)
   UpperCamelCase_					:						Dict =					dict(scheduler.config			)
   UpperCamelCase_					:						List[str] =					True
   UpperCamelCase_					:						List[Any] =					FrozenDict(snake_case			)

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							Optional[Union[str, int]] = "auto"			)   ->						List[Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						int:

  """simple docstring"""





  self.enable_attention_slicing(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





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

  UpperCamelCase_					:						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(snake_case   ,      snake_case			)

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """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(snake_case   ,      '_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	:							Tuple   ,      snake_case	:							Union[str, List[str]]   ,      snake_case	:							Union[torch.FloatTensor, PIL.Image.Image]   ,      snake_case	:							str   ,      snake_case	:							int = 5_1_2   ,      snake_case	:							int = 5_1_2   ,      snake_case	:							int = 5_0   ,      snake_case	:							float = 7.5   ,      snake_case	:							Optional[Union[str, List[str]]] = None   ,      snake_case	:							Optional[int] = 1   ,      snake_case	:							float = 0.0   ,      snake_case	:							Optional[torch.Generator] = None   ,      snake_case	:							Optional[torch.FloatTensor] = None   ,      snake_case	:							Optional[str] = "pil"   ,      snake_case	:							bool = True   ,      snake_case	:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,      snake_case	:							int = 1   ,      **snake_case	:							Any   ,      )   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					self.segmentation_processor(
      text=[text]   ,      images=[image]   ,      padding='max_length'   ,      return_tensors='pt'			).to(self.device			)
  UpperCamelCase_					:						Dict =					self.segmentation_model(**snake_case			)
  UpperCamelCase_					:						Dict =					torch.sigmoid(outputs.logits			).cpu().detach().unsqueeze(-1			).numpy()
  UpperCamelCase_					:						Optional[Any] =					self.numpy_to_pil(snake_case			)[0].resize(image.size			)

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




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 		_lowercase						(					snake_case_						,      unittest.TestCase       ):
 lowercase				     =   XLMTokenizer
 lowercase				     =   False

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  UpperCamelCase_					:						List[Any] =					[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  UpperCamelCase_					:						str =					dict(zip(snake_case   ,      range(len(snake_case			)			)			)			)
  UpperCamelCase_					:						Tuple =					['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  UpperCamelCase_					:						Tuple =					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'			) as fp:
   fp.write(json.dumps(snake_case			)			)
  with open(self.merges_file   ,      'w'			) as fp:
   fp.write('\n'.join(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'lower newer'
  UpperCamelCase_					:						Optional[int] =					'lower newer'
  return input_text, output_text

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer(self.vocab_file   ,      self.merges_file			)

  UpperCamelCase_					:						List[str] =					'lower'
  UpperCamelCase_					:						Optional[int] =					['low', 'er</w>']
  UpperCamelCase_					:						Optional[Any] =					tokenizer.tokenize(snake_case			)
  self.assertListEqual(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					tokens + ['<unk>']
  UpperCamelCase_					:						int =					[1_4, 1_5, 2_0]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case			)   ,      snake_case			)


 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						int =					XLMTokenizer.from_pretrained('xlm-mlm-en-2048'			)

  UpperCamelCase_					:						int =					tokenizer.encode('sequence builders'   ,      add_special_tokens=snake_case			)
  UpperCamelCase_					:						Optional[int] =					tokenizer.encode('multi-sequence build'   ,      add_special_tokens=snake_case			)

  UpperCamelCase_					:						Tuple =					tokenizer.build_inputs_with_special_tokens(snake_case			)
  UpperCamelCase_					:						Any =					tokenizer.build_inputs_with_special_tokens(snake_case   ,      snake_case			)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]



