





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


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

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

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




           '''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        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					Optional[Union[str, int]] = "auto" )  ->	List[str]:




           '''simple docstring'''


           if slice_size == "auto":
                 SCREAMING_SNAKE_CASE	=		self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	str:




           '''simple docstring'''


           self.enable_attention_slicing(lowerCamelCase__ )






     @torch.no_grad()
     def __call__(							self		:					str		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					int=16000		,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__		:					Tuple		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.speech_processor.feature_extractor(
               lowerCamelCase__		,return_tensors="""pt"""		,sampling_rate=lowerCamelCase__ ).input_features.to(self.device )
           SCREAMING_SNAKE_CASE	=		self.speech_model.generate(lowerCamelCase__		,max_length=480000 )

           SCREAMING_SNAKE_CASE	=		self.speech_processor.tokenizer.batch_decode(lowerCamelCase__		,skip_special_tokens=lowerCamelCase__		,normalize=lowerCamelCase__ )[
               0
           ]

           if isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		1
           elif isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 SCREAMING_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
           SCREAMING_SNAKE_CASE	=		self.tokenizer(
               lowerCamelCase__		,padding="""max_length"""		,max_length=self.tokenizer.model_max_length		,return_tensors="""pt"""		,)
           SCREAMING_SNAKE_CASE	=		text_inputs.input_ids

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

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

                 SCREAMING_SNAKE_CASE	=		text_input_ids.shape[-1]
                 SCREAMING_SNAKE_CASE	=		self.tokenizer(
                     lowerCamelCase__		,padding="""max_length"""		,max_length=lowerCamelCase__		,truncation=lowerCamelCase__		,return_tensors="""pt"""		,)
                 SCREAMING_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
                 SCREAMING_SNAKE_CASE	=		uncond_embeddings.shape[1]
                 SCREAMING_SNAKE_CASE	=		uncond_embeddings.repeat(1		,lowerCamelCase__		,1 )
                 SCREAMING_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
                 SCREAMING_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`.
           SCREAMING_SNAKE_CASE	=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           SCREAMING_SNAKE_CASE	=		text_embeddings.dtype
           if latents is None:
                 if self.device.type == "mps":
                       # randn does not exist on mps
                       SCREAMING_SNAKE_CASE	=		torch.randn(lowerCamelCase__		,generator=lowerCamelCase__		,device="""cpu"""		,dtype=lowerCamelCase__ ).to(
                           self.device )
                 else:
                       SCREAMING_SNAKE_CASE	=		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}""" )
                 SCREAMING_SNAKE_CASE	=		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
           SCREAMING_SNAKE_CASE	=		self.scheduler.timesteps.to(self.device )

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

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

                 # predict the noise residual
                 SCREAMING_SNAKE_CASE	=		self.unet(lowerCamelCase__		,lowerCamelCase__		,encoder_hidden_states=lowerCamelCase__ ).sample

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

                 # compute the previous noisy sample x_t -> x_t-1
                 SCREAMING_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__ )

           SCREAMING_SNAKE_CASE	=		1 / 0.18215 * latents
           SCREAMING_SNAKE_CASE	=		self.vae.decode(lowerCamelCase__ ).sample

           SCREAMING_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
           SCREAMING_SNAKE_CASE	=		image.cpu().permute(0		,2		,3		,1 ).float().numpy()

           if output_type == "pil":
                 SCREAMING_SNAKE_CASE	=		self.numpy_to_pil(lowerCamelCase__ )

           if not return_dict:
                 return image

           return StableDiffusionPipelineOutput(images=lowerCamelCase__		,nsfw_content_detected=lowerCamelCase__ )









import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[]
      for line in lines:
            SCREAMING_SNAKE_CASE	=		re.sub(r"""#.*"""				,		""""""				,		_SCREAMING_SNAKE_CASE			)  # remove comments
            if line:
                  filtered_lines.append(_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		"""\n""".join(_SCREAMING_SNAKE_CASE			)

      # Make a hash from all this code
      SCREAMING_SNAKE_CASE	=		full_str.encode("""utf-8"""			)
      return shaaaa(_SCREAMING_SNAKE_CASE			).hexdigest()


# get importable module names and hash for caching
SCREAMING_SNAKE_CASE_		     = {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
SCREAMING_SNAKE_CASE_		     = {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
SCREAMING_SNAKE_CASE_		     = {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
SCREAMING_SNAKE_CASE_		     = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
      _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


