


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Dict     ,					_lowerCAmelCase							:							AutoencoderKL     ,					_lowerCAmelCase							:							CLIPTextModel     ,					_lowerCAmelCase							:							CLIPTokenizer     ,					_lowerCAmelCase							:							UNetaDConditionModel     ,					_lowerCAmelCase							:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,					_lowerCAmelCase							:							StableDiffusionSafetyChecker     ,					_lowerCAmelCase							:							CLIPImageProcessor     ,					) ->			Optional[Any]:




     """simple docstring"""
     super().__init__()
     self.register_modules(
         vae=_lowerCAmelCase     ,					text_encoder=_lowerCAmelCase     ,					tokenizer=_lowerCAmelCase     ,					unet=_lowerCAmelCase     ,					scheduler=_lowerCAmelCase     ,					safety_checker=_lowerCAmelCase     ,					feature_extractor=_lowerCAmelCase     ,					)



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Optional[Union[str, int]] = "auto"	) ->			int:




     """simple docstring"""
     if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        snake_case_            =       self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			Dict:




     """simple docstring"""
     self.enable_attention_slicing(_lowerCAmelCase	)



  @torch.no_grad()
  def __call__(  self							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, List[str]]     ,					_lowerCAmelCase							:							int = 5_1_2     ,					_lowerCAmelCase							:							int = 5_1_2     ,					_lowerCAmelCase							:							int = 5_0     ,					_lowerCAmelCase							:							float = 7.5     ,					_lowerCAmelCase							:							Optional[Union[str, List[str]]] = None     ,					_lowerCAmelCase							:							Optional[int] = 1     ,					_lowerCAmelCase							:							float = 0.0     ,					_lowerCAmelCase							:							Optional[torch.Generator] = None     ,					_lowerCAmelCase							:							Optional[torch.FloatTensor] = None     ,					_lowerCAmelCase							:							Optional[str] = "pil"     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,					_lowerCAmelCase							:							int = 1     ,					_lowerCAmelCase							:							Optional[torch.FloatTensor] = None     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			List[Any]:




     """simple docstring"""

     if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       1
     elif isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       len(_lowerCAmelCase	)
     else:
        raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_lowerCAmelCase	)}'''	)

     if height % 8 != 0 or width % 8 != 0:
        raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''	)

     if (callback_steps is None) or (
         callback_steps is not None and (not isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) or callback_steps <= 0)
     ):
        raise ValueError(
            F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
            F''' {type(_lowerCAmelCase	)}.'''	)

     # get prompt text embeddings
     snake_case_            =       self.tokenizer(
         _lowerCAmelCase     ,					padding="max_length"     ,					max_length=self.tokenizer.model_max_length     ,					return_tensors="pt"     ,					)
     snake_case_            =       text_inputs.input_ids

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        snake_case_            =       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
        logger.warning(
            "The following part of your input was truncated because CLIP can only handle sequences up to"
            F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''	)
        snake_case_            =       text_input_ids[:, : self.tokenizer.model_max_length]

     if text_embeddings is None:
        snake_case_            =       self.text_encoder(text_input_ids.to(self.device	)	)[0]

     # duplicate text embeddings for each generation per prompt, using mps friendly method
     snake_case_     ,     snake_case_     ,     snake_case_            =       text_embeddings.shape
     snake_case_            =       text_embeddings.repeat(1     ,					_lowerCAmelCase     ,					1	)
     snake_case_            =       text_embeddings.view(bs_embed * num_images_per_prompt     ,					_lowerCAmelCase     ,					-1	)

     # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
     # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
     # corresponds to doing no classifier free guidance.
     snake_case_            =       guidance_scale > 1.0
     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance:
        snake_case_            =       42
        if negative_prompt is None:
           snake_case_            =       [""]
        elif type(_lowerCAmelCase	) is not type(_lowerCAmelCase	):
           raise TypeError(
               F'''`negative_prompt` should be the same type to `prompt`, but got {type(_lowerCAmelCase	)} !='''
               F''' {type(_lowerCAmelCase	)}.'''	)
        elif isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
           snake_case_            =       [negative_prompt]
        elif batch_size != len(_lowerCAmelCase	):
           raise ValueError(
               F'''`negative_prompt`: {negative_prompt} has batch size {len(_lowerCAmelCase	)}, but `prompt`:'''
               F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
               " the batch size of `prompt`."	)
        else:
           snake_case_            =       negative_prompt

        snake_case_            =       text_input_ids.shape[-1]
        snake_case_            =       self.tokenizer(
            _lowerCAmelCase     ,					padding="max_length"     ,					max_length=_lowerCAmelCase     ,					truncation=_lowerCAmelCase     ,					return_tensors="pt"     ,					)
        snake_case_            =       self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        snake_case_            =       uncond_embeddings.shape[1]
        snake_case_            =       uncond_embeddings.repeat(_lowerCAmelCase     ,					_lowerCAmelCase     ,					1	)
        snake_case_            =       uncond_embeddings.view(batch_size * num_images_per_prompt     ,					_lowerCAmelCase     ,					-1	)

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        snake_case_            =       torch.cat([uncond_embeddings, text_embeddings]	)

     # get the initial random noise unless the user supplied it

     # Unlike in other pipelines, latents need to be generated in the target device
     # for 1-to-1 results reproducibility with the CompVis implementation.
     # However this currently doesn't work in `mps`.
     snake_case_            =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
     snake_case_            =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
     snake_case_            =       text_embeddings.dtype
     if latents is None:
        if self.device.type == "mps":
           # randn does not exist on mps
           snake_case_            =       torch.randn(
               _lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					device="cpu"     ,					dtype=_lowerCAmelCase	).to(self.device	)
           snake_case_            =       torch.randn(_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					device="cpu"     ,					dtype=_lowerCAmelCase	).to(
               self.device	)
        else:
           snake_case_            =       torch.randn(
               _lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					device=self.device     ,					dtype=_lowerCAmelCase	)
           snake_case_            =       torch.randn(_lowerCAmelCase     ,					generator=_lowerCAmelCase     ,					device=self.device     ,					dtype=_lowerCAmelCase	)
     else:
        if latents_reference.shape != latents_shape:
           raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
        snake_case_            =       latents_reference.to(self.device	)
        snake_case_            =       latents.to(self.device	)

     # This is the key part of the pipeline where we
     # try to ensure that the generated images w/ the same seed
     # but different sizes actually result in similar images
     snake_case_            =       (latents_shape[3] - latents_shape_reference[3]) // 2
     snake_case_            =       (latents_shape[2] - latents_shape_reference[2]) // 2
     snake_case_            =       latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
     snake_case_            =       latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
     snake_case_            =       0 if dx < 0 else dx
     snake_case_            =       0 if dy < 0 else dy
     snake_case_            =       max(-dx     ,					0	)
     snake_case_            =       max(-dy     ,					0	)
     # import pdb
     # pdb.set_trace()
     snake_case_            =       latents_reference[:, :, dy : dy + h, dx : dx + w]

     # set timesteps
     self.scheduler.set_timesteps(_lowerCAmelCase	)

     # Some schedulers like PNDM have timesteps as arrays
     # It's more optimized to move all timesteps to correct device beforehand
     snake_case_            =       self.scheduler.timesteps.to(self.device	)

     # scale the initial noise by the standard deviation required by the scheduler
     snake_case_            =       latents * self.scheduler.init_noise_sigma

     # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
     # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
     # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
     # and should be between [0, 1]
     snake_case_            =       "eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
     snake_case_            =       {}
     if accepts_eta:
        snake_case_            =       eta

     for i, t in enumerate(self.progress_bar(_lowerCAmelCase	)	):
        # expand the latents if we are doing classifier free guidance
        snake_case_            =       torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
        snake_case_            =       self.scheduler.scale_model_input(_lowerCAmelCase     ,					_lowerCAmelCase	)

        # predict the noise residual
        snake_case_            =       self.unet(_lowerCAmelCase     ,					_lowerCAmelCase     ,					encoder_hidden_states=_lowerCAmelCase	).sample

        # perform guidance
        if do_classifier_free_guidance:
           snake_case_     ,     snake_case_            =       noise_pred.chunk(2	)
           snake_case_            =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        snake_case_            =       self.scheduler.step(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	).prev_sample

        # call the callback, if provided
        if callback is not None and i % callback_steps == 0:
           callback(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)

     snake_case_            =       1 / 0.18_215 * latents
     snake_case_            =       self.vae.decode(_lowerCAmelCase	).sample

     snake_case_            =       (image / 2 + 0.5).clamp(0     ,					1	)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     snake_case_            =       image.cpu().permute(0     ,					2     ,					3     ,					1	).float().numpy()

     if self.safety_checker is not None:
        snake_case_            =       self.feature_extractor(self.numpy_to_pil(_lowerCAmelCase	)     ,					return_tensors="pt"	).to(
            self.device	)
        snake_case_     ,     snake_case_            =       self.safety_checker(
            images=_lowerCAmelCase     ,					clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
     else:
        snake_case_            =       None

     if output_type == "pil":
        snake_case_            =       self.numpy_to_pil(_lowerCAmelCase	)

     if not return_dict:
        return (image, has_nsfw_concept)

     return StableDiffusionPipelineOutput(images=_lowerCAmelCase     ,					nsfw_content_detected=_lowerCAmelCase	)








import json
from typing import 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_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE						:str			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE						:str			=  {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']
  _SCREAMING_SNAKE_CASE		     =							RobertaTokenizer



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							Union[str, Any]=None     ,					_lowerCAmelCase							:							Tuple=None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[Any]="replace"     ,					_lowerCAmelCase							:							str="<s>"     ,					_lowerCAmelCase							:							Tuple="</s>"     ,					_lowerCAmelCase							:							Any="</s>"     ,					_lowerCAmelCase							:							Tuple="<s>"     ,					_lowerCAmelCase							:							Any="<unk>"     ,					_lowerCAmelCase							:							Tuple="<pad>"     ,					_lowerCAmelCase							:							List[str]="<mask>"     ,					_lowerCAmelCase							:							str=False     ,					_lowerCAmelCase							:							Union[str, Any]=True     ,					**_lowerCAmelCase							:							int     ,					) ->			str:




     """simple docstring"""
     super().__init__(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					tokenizer_file=_lowerCAmelCase     ,					errors=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					sep_token=_lowerCAmelCase     ,					cls_token=_lowerCAmelCase     ,					unk_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					mask_token=_lowerCAmelCase     ,					add_prefix_space=_lowerCAmelCase     ,					trim_offsets=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

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

     snake_case_            =       add_prefix_space

     snake_case_            =       "post_processor"
     snake_case_            =       getattr(self.backend_tokenizer     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     if tokenizer_component_instance:
        snake_case_            =       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:
           snake_case_            =       tuple(state["sep"]	)
        if "cls" in state:
           snake_case_            =       tuple(state["cls"]	)

        snake_case_            =       False

        if state.get("add_prefix_space"     ,					_lowerCAmelCase	) != add_prefix_space:
           snake_case_            =       add_prefix_space
           snake_case_            =       True

        if state.get("trim_offsets"     ,					_lowerCAmelCase	) != trim_offsets:
           snake_case_            =       trim_offsets
           snake_case_            =       True

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



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			str:




     """simple docstring"""
     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        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Tuple	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       AddedToken(_lowerCAmelCase     ,					lstrip=_lowerCAmelCase     ,					rstrip=_lowerCAmelCase	) if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	) else value
     snake_case_            =       value



  def        lowerCAmelCase__	(  self							:							Dict     ,					*_lowerCAmelCase							:							Optional[int]     ,					**_lowerCAmelCase							:							Optional[int]	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)
     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(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					*_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			BatchEncoding:




     """simple docstring"""
     snake_case_            =       kwargs.get("is_split_into_words"     ,					_lowerCAmelCase	)

     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(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[str] = None	) ->			Tuple[str]:




     """simple docstring"""
     snake_case_            =       self._tokenizer.model.save(_lowerCAmelCase     ,					name=_lowerCAmelCase	)
     return tuple(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]=None	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[int]     ,					_lowerCAmelCase							:							Optional[List[int]] = None	) ->			List[int]:




     """simple docstring"""
     snake_case_            =       [self.sep_token_id]
     snake_case_            =       [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]




