
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

__A				=logging.get_logger(__name__)


__A				={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__A				={
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

__A				={'''facebook/blenderbot-3B''': 1_2_8}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = BlenderbotTokenizer



	def __init__(    self						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  lowercase="replace"						,  lowercase="<s>"						,  lowercase="</s>"						,  lowercase="</s>"						,  lowercase="<s>"						,  lowercase="<unk>"						,  lowercase="<pad>"						,  lowercase="<mask>"						,  lowercase=False						,  lowercase=True						,  **lowercase						,  )				->       Any:
					super().__init__(
					    lowercase						,  lowercase						,  tokenizer_file=lowercase						,  errors=lowercase						,  bos_token=lowercase						,  eos_token=lowercase						,  sep_token=lowercase						,  cls_token=lowercase						,  unk_token=lowercase						,  pad_token=lowercase						,  mask_token=lowercase						,  add_prefix_space=lowercase						,  trim_offsets=lowercase						,  **lowercase						,  )

					lowerCamelCase_						=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
					if pre_tok_state.get("add_prefix_space"						,  lowercase ) != add_prefix_space:
									lowerCamelCase_						=			getattr(lowercase						,  pre_tok_state.pop("type" ) )
									lowerCamelCase_						=			add_prefix_space
									lowerCamelCase_						=			pre_tok_class(**lowercase )

					lowerCamelCase_						=			add_prefix_space

					lowerCamelCase_						=			"post_processor"
					lowerCamelCase_						=			getattr(self.backend_tokenizer						,  lowercase						,  lowercase )
					if tokenizer_component_instance:
									lowerCamelCase_						=			json.loads(tokenizer_component_instance.__getstate__() )

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
													lowerCamelCase_						=			tuple(state["sep"] )
									if "cls" in state:
													lowerCamelCase_						=			tuple(state["cls"] )

									lowerCamelCase_						=			False

									if state.get("add_prefix_space"						,  lowercase ) != add_prefix_space:
													lowerCamelCase_						=			add_prefix_space
													lowerCamelCase_						=			True

									if state.get("trim_offsets"						,  lowercase ) != trim_offsets:
													lowerCamelCase_						=			trim_offsets
													lowerCamelCase_						=			True

									if changes_to_apply:
													lowerCamelCase_						=			getattr(lowercase						,  state.pop("type" ) )
													lowerCamelCase_						=			component_class(**lowercase )
													setattr(self.backend_tokenizer						,  lowercase						,  lowercase )



	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					if self._mask_token is None:
									if self.verbose:
													logger.error("Using mask_token, but it is not set yet." )
									return None
					return str(self._mask_token )



	@mask_token.setter
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[str]:
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else value
					lowerCamelCase_						=			value



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       BatchEncoding:
					lowerCamelCase_						=			kwargs.get("is_split_into_words"						,  lowercase )
					assert self.add_prefix_space or not is_split_into_words, (
					    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    "to use it with pretokenized inputs."
					)

					return super()._batch_encode_plus(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       BatchEncoding:
					lowerCamelCase_						=			kwargs.get("is_split_into_words"						,  lowercase )

					assert self.add_prefix_space or not is_split_into_words, (
					    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    "to use it with pretokenized inputs."
					)

					return super()._encode_plus(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					lowerCamelCase_						=			self._tokenizer.model.save(lowercase						,  name=lowercase )
					return tuple(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       int:
					return token_ids_a + [self.eos_token_id]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[int]:
					lowerCamelCase_						=			[]
					for is_user, text in conversation.iter_texts():
									if is_user:
													# We need to space prefix as it's being done within blenderbot
													inputs.append(" " + text )
									else:
													# Generated responses should contain them already.
													inputs.append(lowercase )

					lowerCamelCase_						=			"  ".join(lowercase )
					lowerCamelCase_						=			self.encode(lowercase )
					if len(lowercase ) > self.model_max_length:
									lowerCamelCase_						=			input_ids[-self.model_max_length :]
									logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.' )
					return input_ids




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     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[Any]:
					super().__init__()

					if hasattr(scheduler.config						,  "steps_offset" ) and scheduler.config.steps_offset != 1:
									lowerCamelCase_						=			(
									    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 )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			1
									lowerCamelCase_						=			FrozenDict(lowercase )

					if hasattr(scheduler.config						,  "skip_prk_steps" ) and scheduler.config.skip_prk_steps is False:
									lowerCamelCase_						=			(
									    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 )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			True
									lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase = "auto" )				->       Tuple:
					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowerCamelCase_						=			self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					self.enable_attention_slicing(lowercase )



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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					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						,  lowercase						,  lowercase						,  lowercase						,  lowercase = 512						,  lowercase = 512						,  lowercase = 50						,  lowercase = 7.5						,  lowercase = None						,  lowercase = 1						,  lowercase = 0.0						,  lowercase = None						,  lowercase = None						,  lowercase = "pil"						,  lowercase = True						,  lowercase = None						,  lowercase = 1						,  **lowercase						,  )				->       int:
					lowerCamelCase_						=			self.segmentation_processor(
					    text=[text]						,  images=[image]						,  padding="max_length"						,  return_tensors="pt" ).to(self.device )
					lowerCamelCase_						=			self.segmentation_model(**lowercase )
					lowerCamelCase_						=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
					lowerCamelCase_						=			self.numpy_to_pil(lowercase )[0].resize(image.size )

					# Run inpainting pipeline with the generated mask
					lowerCamelCase_						=			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						,  )


