



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class    _UpperCAmelCase      ( __lowercase			):

				"""simple docstring"""




				lowercase__       							= """facebook/bart-large-mnli"""
				lowercase__       							= (
				    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
				    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
				    """It returns the most likely label in the list of provided `labels` for the input text."""
				)
				lowercase__       							= """text_classifier"""
				lowercase__       							= AutoTokenizer
				lowercase__       							= AutoModelForSequenceClassification

				lowercase__       							= ["""text""", ["""text"""]]
				lowercase__       							= ["""text"""]




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								super().setup()
								lowercase__     				=    self.model.config
								lowercase__     				=    -1
								for idx, label in config.idalabel.items():
												if label.lower().startswith('''entail'''						):
																lowercase__     				=    int(lowerCamelCase						)
								if self.entailment_id == -1:
												raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					Dict,						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    labels
								return self.pre_processor(
								    [text] * len(lowerCamelCase						),						[F"""This example is {label}""" for label in labels],						return_tensors='''pt''',						padding='''max_length''',						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    outputs.logits
								lowercase__     				=    torch.argmax(logits[:, 2]						).item()
								return self._labels[label_id]












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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A__			:    Any         =				logging.get_logger(__name__)  # pylint: disable=invalid-name
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					WhisperForConditionalGeneration,						lowerCamelCase		:					WhisperProcessor,						lowerCamelCase		:					AutoencoderKL,						lowerCamelCase		:					CLIPTextModel,						lowerCamelCase		:					CLIPTokenizer,						lowerCamelCase		:					UNetaDConditionModel,						lowerCamelCase		:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],						lowerCamelCase		:					StableDiffusionSafetyChecker,						lowerCamelCase		:					CLIPImageProcessor,						):

								'''simple docstring'''
								super().__init__()

								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(
								    speech_model=lowerCamelCase,						speech_processor=lowerCamelCase,						vae=lowerCamelCase,						text_encoder=lowerCamelCase,						tokenizer=lowerCamelCase,						unet=lowerCamelCase,						scheduler=lowerCamelCase,						feature_extractor=lowerCamelCase,						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[Union[str, int]] = "auto"						):

								'''simple docstring'''
								if slice_size == "auto":
												lowercase__     				=    self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.enable_attention_slicing(lowerCamelCase						)




				@torch.no_grad()
				def __call__(  self		:					Any,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Optional[Any]=16_000,						lowerCamelCase		:					int = 512,						lowerCamelCase		:					int = 512,						lowerCamelCase		:					int = 50,						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[Any],						):

								'''simple docstring'''
								lowercase__     				=    self.speech_processor.feature_extractor(
								    lowerCamelCase,						return_tensors='''pt''',						sampling_rate=lowerCamelCase						).input_features.to(self.device						)
								lowercase__     				=    self.speech_model.generate(lowerCamelCase,						max_length=480_000						)

								lowercase__     				=    self.speech_processor.tokenizer.batch_decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase,						normalize=lowerCamelCase						)[
								    0
								]

								if isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    1
								elif isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    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
								lowercase__     				=    self.tokenizer(
								    lowerCamelCase,						padding='''max_length''',						max_length=self.tokenizer.model_max_length,						return_tensors='''pt''',						)
								lowercase__     				=    text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												lowercase__     				=    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}"""						)
												lowercase__     				=    text_input_ids[:, : self.tokenizer.model_max_length]
								lowercase__     				=    self.text_encoder(text_input_ids.to(self.device						)						)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								lowercase__				,		lowercase__				,		lowercase__     				=    text_embeddings.shape
								lowercase__     				=    text_embeddings.repeat(1,						lowerCamelCase,						1						)
								lowercase__     				=    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.
								lowercase__     				=    guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												lowercase__     				=    42
												if negative_prompt is None:
																lowercase__     				=    [''''''] * batch_size
												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						):
																lowercase__     				=    [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:
																lowercase__     				=    negative_prompt

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

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												lowercase__     				=    uncond_embeddings.shape[1]
												lowercase__     				=    uncond_embeddings.repeat(1,						lowerCamelCase,						1						)
												lowercase__     				=    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
												lowercase__     				=    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`.
								lowercase__     				=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								lowercase__     				=    text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not exist on mps
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device='''cpu''',						dtype=lowerCamelCase						).to(
																    self.device						)
												else:
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device=self.device,						dtype=lowerCamelCase						)
								else:
												if latents.shape != latents_shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
												lowercase__     				=    latents.to(self.device						)

								# 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
								lowercase__     				=    self.scheduler.timesteps.to(self.device						)

								# scale the initial noise by the standard deviation required by the scheduler
								lowercase__     				=    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]
								lowercase__     				=    '''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
								lowercase__     				=    {}
								if accepts_eta:
												lowercase__     				=    eta

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

												# predict the noise residual
												lowercase__     				=    self.unet(lowerCamelCase,						lowerCamelCase,						encoder_hidden_states=lowerCamelCase						).sample

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

												# compute the previous noisy sample x_t -> x_t-1
												lowercase__     				=    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						)

								lowercase__     				=    1 / 0.18215 * latents
								lowercase__     				=    self.vae.decode(lowerCamelCase						).sample

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

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

								if output_type == "pil":
												lowercase__     				=    self.numpy_to_pil(lowerCamelCase						)

								if not return_dict:
												return image

								return StableDiffusionPipelineOutput(images=lowerCamelCase,						nsfw_content_detected=lowerCamelCase						)




