

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



class       __snake_case  (    lowerCAmelCase__       ):

		def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A						,		):
						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										    f"""The configuration file of this scheduler: {scheduler} is outdated.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'						,		__UpperCamelCase						,		standard_warn=__UpperCamelCase)
										SCREAMING_SNAKE_CASE_									=						dict(scheduler.config)
										SCREAMING_SNAKE_CASE_									=						1
										SCREAMING_SNAKE_CASE_									=						FrozenDict(__UpperCamelCase)

						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'						,		__UpperCamelCase						,		standard_warn=__UpperCamelCase)
										SCREAMING_SNAKE_CASE_									=						dict(scheduler.config)
										SCREAMING_SNAKE_CASE_									=						True
										SCREAMING_SNAKE_CASE_									=						FrozenDict(__UpperCamelCase)

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

		def 						lowerCAmelCase__     (					self						,		_A = "auto"):
						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(__UpperCamelCase)

		def 						lowerCAmelCase__     (					self):
						self.enable_attention_slicing(__UpperCamelCase)

		def 						lowerCAmelCase__     (					self):
						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(__UpperCamelCase						,		__UpperCamelCase)

		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 						lowerCAmelCase__     (					self):
						if self.device != torch.device('meta') or not hasattr(self.unet						,		'_hf_hook'):
										return self.device
						for module in self.unet.modules():
										if (
										    hasattr(__UpperCamelCase						,		'_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						,		_A						,		_A						,		_A						,		_A = 512						,		_A = 512						,		_A = 50						,		_A = 7.5						,		_A = None						,		_A = 1						,		_A = 0.0						,		_A = None						,		_A = None						,		_A = "pil"						,		_A = True						,		_A = None						,		_A = 1						,		**_A						,		):
						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(**__UpperCamelCase)
						SCREAMING_SNAKE_CASE_									=						torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
						SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(__UpperCamelCase)[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=__UpperCamelCase						,		image=__UpperCamelCase						,		mask_image=__UpperCamelCase						,		height=__UpperCamelCase						,		width=__UpperCamelCase						,		num_inference_steps=__UpperCamelCase						,		guidance_scale=__UpperCamelCase						,		negative_prompt=__UpperCamelCase						,		num_images_per_prompt=__UpperCamelCase						,		eta=__UpperCamelCase						,		generator=__UpperCamelCase						,		latents=__UpperCamelCase						,		output_type=__UpperCamelCase						,		return_dict=__UpperCamelCase						,		callback=__UpperCamelCase						,		callback_steps=__UpperCamelCase						,		)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       UpperCamelCase__  :      str			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


