


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


snake_case__				:	str					       =					logging.get_logger(__name__)  # pylint: disable=invalid-name





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       def __init__(						self				:				int		,				__a				:				WhisperForConditionalGeneration		,				__a				:				WhisperProcessor		,				__a				:				AutoencoderKL		,				__a				:				CLIPTextModel		,				__a				:				CLIPTokenizer		,				__a				:				UNetaDConditionModel		,				__a				:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,				__a				:				StableDiffusionSafetyChecker		,				__a				:				CLIPImageProcessor		,				)					->Optional[Any]:
          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=__a		,				speech_processor=__a		,				vae=__a		,				text_encoder=__a		,				tokenizer=__a		,				unet=__a		,				scheduler=__a		,				feature_extractor=__a		,				)



       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				Optional[Union[str, int]] = "auto"							)					->List[Any]:
          if slice_size == "auto":
             lowerCamelCase_						:   Optional[Any]		= self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(__a							)



       def 				_lowerCAmelCase							(						self				:				str							)					->List[Any]:
          self.enable_attention_slicing(__a							)




       @torch.no_grad()
       def __call__(						self				:				Optional[Any]		,				__a				:				int		,				__a				:				str=16_000		,				__a				:				int = 512		,				__a				:				int = 512		,				__a				:				int = 50		,				__a				:				float = 7.5		,				__a				:				Optional[Union[str, List[str]]] = None		,				__a				:				Optional[int] = 1		,				__a				:				float = 0.0		,				__a				:				Optional[torch.Generator] = None		,				__a				:				Optional[torch.FloatTensor] = None		,				__a				:				Optional[str] = "pil"		,				__a				:				bool = True		,				__a				:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,				__a				:				int = 1		,				**__a				:				int		,				)					->Optional[int]:
          lowerCamelCase_						:   Any		= self.speech_processor.feature_extractor(
              __a		,				return_tensors="""pt"""		,				sampling_rate=__a							).input_features.to(self.device							)
          lowerCamelCase_						:   Optional[Any]		= self.speech_model.generate(__a		,				max_length=480_000							)

          lowerCamelCase_						:   Optional[int]		= self.speech_processor.tokenizer.batch_decode(__a		,				skip_special_tokens=__a		,				normalize=__a							)[
              0
          ]

          if isinstance(__a		,				__a							):
             lowerCamelCase_						:   Union[str, Any]		= 1
          elif isinstance(__a		,				__a							):
             lowerCamelCase_						:   int		= 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
          lowerCamelCase_						:   List[str]		= self.tokenizer(
              __a		,				padding="""max_length"""		,				max_length=self.tokenizer.model_max_length		,				return_tensors="""pt"""		,				)
          lowerCamelCase_						:   Dict		= text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowerCamelCase_						:   int		= 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}'''							)
             lowerCamelCase_						:   str		= text_input_ids[:, : self.tokenizer.model_max_length]
          lowerCamelCase_						:   Union[str, Any]		= self.text_encoder(text_input_ids.to(self.device							)							)[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   int		= text_embeddings.shape
          lowerCamelCase_						:   str		= text_embeddings.repeat(1		,				__a		,				1							)
          lowerCamelCase_						:   Optional[Any]		= 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.
          lowerCamelCase_						:   Tuple		= guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
             lowerCamelCase_						:   List[str]
             if negative_prompt is None:
                lowerCamelCase_						:   int		= [""""""] * batch_size
             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							):
                lowerCamelCase_						:   List[str]		= [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:
                lowerCamelCase_						:   Dict		= negative_prompt

             lowerCamelCase_						:   Any		= text_input_ids.shape[-1]
             lowerCamelCase_						:   str		= self.tokenizer(
                 __a		,				padding="""max_length"""		,				max_length=__a		,				truncation=__a		,				return_tensors="""pt"""		,				)
             lowerCamelCase_						:   Optional[Any]		= self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

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

          # 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
          lowerCamelCase_						:   str		= self.scheduler.timesteps.to(self.device							)

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

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

             # predict the noise residual
             lowerCamelCase_						:   str		= self.unet(__a		,				__a		,				encoder_hidden_states=__a							).sample

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

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase_						:   Optional[int]		= 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							)

          lowerCamelCase_						:   Tuple		= 1 / 0.18_215 * latents
          lowerCamelCase_						:   str		= self.vae.decode(__a							).sample

          lowerCamelCase_						:   Union[str, 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
          lowerCamelCase_						:   Tuple		= image.cpu().permute(0		,				2		,				3		,				1							).float().numpy()

          if output_type == "pil":
             lowerCamelCase_						:   Any		= self.numpy_to_pil(__a							)

          if not return_dict:
             return image

          return StableDiffusionPipelineOutput(images=__a		,				nsfw_content_detected=__a							)









import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__				:	str					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()						)}						)
       _a   							=							field(
           metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
       _a   							=							field(
           default=128						,       metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Overwrite the cached training and evaluation sets"}						)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          lowerCamelCase_						:   Optional[Any]		= self.task_name.lower()







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"train"
       _a   							=							"dev"
       _a   							=							"test"







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							42
       _a   							=							42
       _a   							=							42



       def __init__(						self				:				Any		,				__a				:				GlueDataTrainingArguments		,				__a				:				PreTrainedTokenizerBase		,				__a				:				Optional[int] = None		,				__a				:				Union[str, Split] = Split.train		,				__a				:				Optional[str] = None		,				)					->Optional[int]:
          warnings.warn(
              """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
              """library. You can have a look at this example script for pointers: """
              """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""		,				__a		,				)
          lowerCamelCase_						:   Optional[int]		= args
          lowerCamelCase_						:   Tuple		= glue_processors[args.task_name]()
          lowerCamelCase_						:   Optional[Any]		= glue_output_modes[args.task_name]
          if isinstance(__a		,				__a							):
             try:
                lowerCamelCase_						:   List[Any]		= Split[mode]
             except KeyError:
                raise KeyError("""mode is not a valid split name"""							)
        # Load data features from cache or dataset file
          lowerCamelCase_						:   List[Any]		= os.path.join(
              cache_dir if cache_dir is not None else args.data_dir		,				F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''		,				)
          lowerCamelCase_						:   Any		= self.processor.get_labels()
          if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
              "RobertaTokenizer",
              "RobertaTokenizerFast",
              "XLMRobertaTokenizer",
              "BartTokenizer",
              "BartTokenizerFast",
          ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase_,	lowerCamelCase_						:   int		= label_list[2], label_list[1]
          lowerCamelCase_						:   Any		= label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowerCamelCase_						:   List[Any]		= cached_features_file + """.lock"""
          with FileLock(__a							):
             if os.path.exists(__a							) and not args.overwrite_cache:
                lowerCamelCase_						:   str		= time.time()
                lowerCamelCase_						:   int		= torch.load(__a							)
                logger.info(
                    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''		,				time.time() - start							)
             else:
                logger.info(F'''Creating features from dataset file at {args.data_dir}'''							)

                if mode == Split.dev:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_dev_examples(args.data_dir							)
                elif mode == Split.test:
                   lowerCamelCase_						:   Tuple		= self.processor.get_test_examples(args.data_dir							)
                else:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_train_examples(args.data_dir							)
                if limit_length is not None:
                   lowerCamelCase_						:   Dict		= examples[:limit_length]
                lowerCamelCase_						:   Union[str, Any]		= glue_convert_examples_to_features(
                    __a		,				__a		,				max_length=args.max_seq_length		,				label_list=__a		,				output_mode=self.output_mode		,				)
                lowerCamelCase_						:   Optional[Any]		= time.time()
                torch.save(self.features		,				__a							)
                # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                logger.info(
                    F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''							)



       def __len__(						self				:				Any							)					->Any:
          return len(self.features							)



       def __getitem__(						self				:				List[Any]		,				__a				:				Optional[int]							)					->InputFeatures:
          return self.features[i]




       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[int]:
          return self.label_list





