





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
    import tensorflow as tf

    from ..tf_utils import stable_softmax


if is_torch_available():
    import torch


__lowerCamelCase	:					Optional[Any]			  =					logging.get_logger(__name__)



@add_end_docstrings(
    __snake_case	,       r'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '	,       )
class  A__       (	__snake_case      ):





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.framework == "tf":
        UpperCamelCase     : Tuple	     =       tf.where(input_ids == self.tokenizer.mask_token_id       ).numpy()
       elif self.framework == "pt":
        UpperCamelCase     : List[Any]	     =       torch.nonzero(input_ids == self.tokenizer.mask_token_id	,							as_tuple=A_       )
       else:
        raise ValueError("Unsupported framework"       )
       return masked_index





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.get_masked_index(A_       )
       UpperCamelCase     : Optional[Any]	     =       np.prod(masked_index.shape       )
       if numel < 1:
        raise PipelineException(
            "fill-mask"	,							self.model.base_model_prefix	,							F"""No mask_token ({self.tokenizer.mask_token}) found on the input"""	,							)





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if isinstance(A_	,							A_       ):
        for model_input in model_inputs:
         self._ensure_exactly_one_mask_token(model_input["input_ids"][0]       )
       else:
        for input_ids in model_inputs["input_ids"]:
         self._ensure_exactly_one_mask_token(A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_=None	,							**A_       ):
       '''simple docstring'''




       if return_tensors is None:
        UpperCamelCase     : List[str]	     =       self.framework
       UpperCamelCase     : int	     =       self.tokenizer(A_	,							return_tensors=A_       )
       self.ensure_exactly_one_mask_token(A_       )
       return model_inputs





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self.model(**A_       )
       UpperCamelCase     : List[Any]	     =       model_inputs["input_ids"]
       return model_outputs





      def 	__UpperCamelCase(			self	,							A_	,							A_=5	,							A_=None       ):
       '''simple docstring'''




       if target_ids is not None and target_ids.shape[0] < top_k:
        UpperCamelCase     : str	     =       target_ids.shape[0]
       UpperCamelCase     : List[str]	     =       model_outputs["input_ids"][0]
       UpperCamelCase     : Union[str, Any]	     =       model_outputs["logits"]

       if self.framework == "tf":
        UpperCamelCase     : str	     =       tf.where(input_ids == self.tokenizer.mask_token_id       ).numpy()[:, 0]

        UpperCamelCase     : Dict	     =       outputs.numpy()

        UpperCamelCase     : Any	     =       outputs[0, masked_index, :]
        UpperCamelCase     : int	     =       stable_softmax(A_	,							axis=-1       )
        if target_ids is not None:
         UpperCamelCase     : Union[str, Any]	     =       tf.gather_nd(tf.squeeze(A_	,							0       )	,							target_ids.reshape(-1	,							1       )       )
         UpperCamelCase     : Union[str, Any]	     =       tf.expand_dims(A_	,							0       )

        UpperCamelCase     : Optional[int]	     =       tf.math.top_k(A_	,							k=A_       )
        UpperCamelCase     ,   UpperCamelCase     : str	     =       topk.values.numpy(), topk.indices.numpy()
       else:
        UpperCamelCase     : Tuple	     =       torch.nonzero(input_ids == self.tokenizer.mask_token_id	,							as_tuple=A_       ).squeeze(-1       )
        # Fill mask pipeline supports only one ${mask_token} per sample

        UpperCamelCase     : Tuple	     =       outputs[0, masked_index, :]
        UpperCamelCase     : Union[str, Any]	     =       logits.softmax(dim=-1       )
        if target_ids is not None:
         UpperCamelCase     : Any	     =       probs[..., target_ids]

        UpperCamelCase     ,   UpperCamelCase     : List[str]	     =       probs.topk(A_       )

       UpperCamelCase     : List[Any]	     =       []
       UpperCamelCase     : Optional[Any]	     =       values.shape[0] == 1
       for i, (_values, _predictions) in enumerate(zip(values.tolist()	,							predictions.tolist()       )       ):
        UpperCamelCase     : Dict	     =       []
        for v, p in zip(_values	,							_predictions       ):
         # Copy is important since we're going to modify this array in place
         UpperCamelCase     : Tuple	     =       input_ids.numpy().copy()
         if target_ids is not None:
          UpperCamelCase     : str	     =       target_ids[p].tolist()

         UpperCamelCase     : List[str]	     =       p
         # Filter padding out:
         UpperCamelCase     : Union[str, Any]	     =       tokens[np.where(tokens != self.tokenizer.pad_token_id       )]
         # Originally we skip special tokens to give readable output.
         # For multi masks though, the other [MASK] would be removed otherwise
         # making the output look odd, so we add them back
         UpperCamelCase     : int	     =       self.tokenizer.decode(A_	,							skip_special_tokens=A_       )
         UpperCamelCase     : int	     =       {"score": v, "token": p, "token_str": self.tokenizer.decode([p]       ), "sequence": sequence}
         row.append(A_       )
        result.append(A_       )
       if single_mask:
        return result[0]
       return result





      def 	__UpperCamelCase(			self	,							A_	,							A_=None       ):
       '''simple docstring'''




       if isinstance(A_	,							A_       ):
        UpperCamelCase     : Dict	     =       [targets]
       try:
        UpperCamelCase     : int	     =       self.tokenizer.get_vocab()
       except Exception:
        UpperCamelCase     : Tuple	     =       {}
       UpperCamelCase     : Union[str, Any]	     =       []
       for target in targets:
        UpperCamelCase     : Optional[int]	     =       vocab.get(A_	,							A_       )
        if id_ is None:
         UpperCamelCase     : Any	     =       self.tokenizer(
             A_	,							add_special_tokens=A_	,							return_attention_mask=A_	,							return_token_type_ids=A_	,							max_length=1	,							truncation=A_	,							)["input_ids"]
         if len(A_       ) == 0:
          logger.warning(
              F"""The specified target token `{target}` does not exist in the model vocabulary. """
              "We cannot replace it with anything meaningful, ignoring it"       )
          continue
         UpperCamelCase     : str	     =       input_ids[0]
         # XXX: If users encounter this pass
         # it becomes pretty slow, so let's make sure
         # The warning enables them to fix the input to
         # get faster performance.
         logger.warning(
             F"""The specified target token `{target}` does not exist in the model vocabulary. """
             F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_       )}`."""       )
        target_ids.append(id_       )
       UpperCamelCase     : Dict	     =       list(set(A_       )       )
       if len(A_       ) == 0:
        raise ValueError("At least one target must be provided when passed."       )
       UpperCamelCase     : Optional[int]	     =       np.array(A_       )
       return target_ids





      def 	__UpperCamelCase(			self	,							A_=None	,							A_=None       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       {}

       if targets is not None:
        UpperCamelCase     : Optional[Any]	     =       self.get_target_ids(A_	,							A_       )
        UpperCamelCase     : int	     =       target_ids

       if top_k is not None:
        UpperCamelCase     : int	     =       top_k

       if self.tokenizer.mask_token_id is None:
        raise PipelineException(
            "fill-mask"	,							self.model.base_model_prefix	,							"The tokenizer does not define a `mask_token`."       )
       return {}, {}, postprocess_params





      def __call__(			self	,							A_	,							*A_	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       super().__call__(A_	,							**A_       )
       if isinstance(A_	,							A_       ) and len(A_       ) == 1:
        return outputs[0]
       return outputs







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


__lowerCamelCase	:					List[str]			  =					logging.get_logger(__name__)  # pylint: disable=invalid-name



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       super().__init__()
       self.register_modules(
           vae=A_	,							text_encoder=A_	,							tokenizer=A_	,							unet=A_	,							scheduler=A_	,							safety_checker=A_	,							feature_extractor=A_	,							)





      def 	__UpperCamelCase(			self	,							A_ = "auto"       ):
       '''simple docstring'''




       if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        UpperCamelCase     : Union[str, Any]	     =       self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.enable_attention_slicing(A_       )





      @torch.no_grad()
      def __call__(			self	,							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_ = None	,							**A_	,							):
       '''simple docstring'''




       if isinstance(A_	,							A_       ):
        UpperCamelCase     : Any	     =       1
       elif isinstance(A_	,							A_       ):
        UpperCamelCase     : Optional[Any]	     =       len(A_       )
       else:
        raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(A_       )}"""       )

       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(A_	,							A_       ) or callback_steps <= 0)
       ):
        raise ValueError(
            F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
            F""" {type(A_       )}."""       )

       # get prompt text embeddings
       UpperCamelCase     : int	     =       self.tokenizer(
           A_	,							padding="max_length"	,							max_length=self.tokenizer.model_max_length	,							return_tensors="pt"	,							)
       UpperCamelCase     : Optional[Any]	     =       text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        UpperCamelCase     : List[str]	     =       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}"""       )
        UpperCamelCase     : Any	     =       text_input_ids[:, : self.tokenizer.model_max_length]

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

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : str	     =       text_embeddings.shape
       UpperCamelCase     : int	     =       text_embeddings.repeat(1	,							A_	,							1       )
       UpperCamelCase     : str	     =       text_embeddings.view(bs_embed * num_images_per_prompt	,							A_	,							-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.
       UpperCamelCase     : Optional[int]	     =       guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        UpperCamelCase     : List[str]
        if negative_prompt is None:
         UpperCamelCase     : Tuple	     =       [""]
        elif type(A_       ) is not type(A_       ):
         raise TypeError(
             F"""`negative_prompt` should be the same type to `prompt`, but got {type(A_       )} !="""
             F""" {type(A_       )}."""       )
        elif isinstance(A_	,							A_       ):
         UpperCamelCase     : Optional[int]	     =       [negative_prompt]
        elif batch_size != len(A_       ):
         raise ValueError(
             F"""`negative_prompt`: {negative_prompt} has batch size {len(A_       )}, but `prompt`:"""
             F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
             " the batch size of `prompt`."       )
        else:
         UpperCamelCase     : Union[str, Any]	     =       negative_prompt

        UpperCamelCase     : Tuple	     =       text_input_ids.shape[-1]
        UpperCamelCase     : str	     =       self.tokenizer(
            A_	,							padding="max_length"	,							max_length=A_	,							truncation=A_	,							return_tensors="pt"	,							)
        UpperCamelCase     : str	     =       self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        UpperCamelCase     : Optional[int]	     =       uncond_embeddings.shape[1]
        UpperCamelCase     : Optional[Any]	     =       uncond_embeddings.repeat(A_	,							A_	,							1       )
        UpperCamelCase     : Dict	     =       uncond_embeddings.view(batch_size * num_images_per_prompt	,							A_	,							-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
        UpperCamelCase     : Optional[int]	     =       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`.
       UpperCamelCase     : Union[str, Any]	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCamelCase     : Tuple	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       UpperCamelCase     : Any	     =       text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not exist on mps
         UpperCamelCase     : Optional[Any]	     =       torch.randn(
             A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(self.device       )
         UpperCamelCase     : Dict	     =       torch.randn(A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(
             self.device       )
        else:
         UpperCamelCase     : Tuple	     =       torch.randn(
             A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
         UpperCamelCase     : str	     =       torch.randn(A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
       else:
        if latents_reference.shape != latents_shape:
         raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
        UpperCamelCase     : List[Any]	     =       latents_reference.to(self.device       )
        UpperCamelCase     : Optional[Any]	     =       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
       UpperCamelCase     : List[str]	     =       (latents_shape[3] - latents_shape_reference[3]) // 2
       UpperCamelCase     : str	     =       (latents_shape[2] - latents_shape_reference[2]) // 2
       UpperCamelCase     : List[Any]	     =       latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       UpperCamelCase     : Optional[Any]	     =       latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       UpperCamelCase     : str	     =       0 if dx < 0 else dx
       UpperCamelCase     : Union[str, Any]	     =       0 if dy < 0 else dy
       UpperCamelCase     : Union[str, Any]	     =       max(-dx	,							0       )
       UpperCamelCase     : Tuple	     =       max(-dy	,							0       )
       # import pdb
       # pdb.set_trace()
       UpperCamelCase     : Optional[Any]	     =       latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(A_       )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCamelCase     : Union[str, Any]	     =       self.scheduler.timesteps.to(self.device       )

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCamelCase     : str	     =       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]
       UpperCamelCase     : List[str]	     =       "eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
       UpperCamelCase     : str	     =       {}
       if accepts_eta:
        UpperCamelCase     : int	     =       eta

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

        # predict the noise residual
        UpperCamelCase     : List[str]	     =       self.unet(A_	,							A_	,							encoder_hidden_states=A_       ).sample

        # perform guidance
        if do_classifier_free_guidance:
         UpperCamelCase     ,   UpperCamelCase     : Any	     =       noise_pred.chunk(2       )
         UpperCamelCase     : List[str]	     =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        UpperCamelCase     : Optional[Any]	     =       self.scheduler.step(A_	,							A_	,							A_	,							**A_       ).prev_sample

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

       UpperCamelCase     : int	     =       1 / 0.1_82_15 * latents
       UpperCamelCase     : Tuple	     =       self.vae.decode(A_       ).sample

       UpperCamelCase     : Any	     =       (image / 2 + 0.5).clamp(0	,							1       )

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

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

       if output_type == "pil":
        UpperCamelCase     : Tuple	     =       self.numpy_to_pil(A_       )

       if not return_dict:
        return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=A_	,							nsfw_content_detected=A_       )
