





'''simple docstring'''






from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
	import PIL

# soft dependency
if is_pytesseract_available():
	import pytesseract

__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			return [
			    int(1000 * (box[0] / width) ),
			    int(1000 * (box[1] / height) ),
			    int(1000 * (box[2] / width) ),
			    int(1000 * (box[3] / height) ),
			]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     to_pil_image(_lowerCamelCase )
			__snake_case      ,     __snake_case					:      Union[str, Any]							      =     pil_image.size
			__snake_case					:      Tuple							      =     pytesseract.image_to_data(_lowerCamelCase    ,	lang=_lowerCamelCase    ,	output_type="""dict"""    ,	config=_lowerCamelCase )
			__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      List[str]							      =     data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

			# filter empty words and corresponding coordinates
			__snake_case					:      Dict							      =     [idx for idx, word in enumerate(_lowerCamelCase ) if not word.strip()]
			__snake_case					:      int							      =     [word for idx, word in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Optional[int]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Dict							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Optional[int]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      List[str]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			__snake_case					:      str							      =     []
			for x, y, w, h in zip(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Any							      =     [x, y, x + w, y + h]
						actual_boxes.append(_lowerCamelCase )

			# finally, normalize the bounding boxes
			__snake_case					:      str							      =     []
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) )

			assert len(_lowerCamelCase ) == len(_lowerCamelCase ), "Not as many words as there are bounding boxes"

			return words, normalized_boxes




class  _A (					__lowercase		):
		lowercase__:   Optional[int]				     =   ['''pixel_values''']

		def __init__(   self					:   List[str]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   PILImageResampling = PILImageResampling.BILINEAR  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   float = 1 / 2_55  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[str] = ""  ,      **__magic_name__					:   str  ,      )					->    None:


					"""simple docstring"""

					super().__init__(**__magic_name__			)
					__snake_case					:      int							      =     size if size is not None else {"""height""": 2_24, """width""": 2_24}
					__snake_case					:      Optional[Any]							      =     get_size_dict(__magic_name__			)

					__snake_case					:      Tuple							      =     do_resize
					__snake_case					:      List[Any]							      =     size
					__snake_case					:      Dict							      =     resample
					__snake_case					:      str							      =     do_rescale
					__snake_case					:      List[str]							      =     rescale_value
					__snake_case					:      Optional[int]							      =     do_normalize
					__snake_case					:      List[str]							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__snake_case					:      Optional[Any]							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD
					__snake_case					:      Union[str, Any]							      =     apply_ocr
					__snake_case					:      List[Any]							      =     ocr_lang
					__snake_case					:      Optional[int]							      =     tesseract_config

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Dict[str, int]  ,      __magic_name__					:   PILImageResampling = PILImageResampling.BILINEAR  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   List[Any]  ,      )					->    np.ndarray:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     get_size_dict(__magic_name__			)
					if "height" not in size or "width" not in size:
								raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''			)
					__snake_case					:      Any							      =     (size["""height"""], size["""width"""])
					return resize(__magic_name__  ,      size=__magic_name__  ,      resample=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Union[int, float]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   Dict  ,      )					->    np.ndarray:


					"""simple docstring"""

					return rescale(__magic_name__  ,      scale=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Union[float, Iterable[float]]  ,      __magic_name__					:   Union[float, Iterable[float]]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   Optional[Any]  ,      )					->    np.ndarray:


					"""simple docstring"""

					return normalize(__magic_name__  ,      mean=__magic_name__  ,      std=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   ImageInput  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   List[Any]=None  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   float = None  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[Union[str, TensorType]] = None  ,      __magic_name__					:   ChannelDimension = ChannelDimension.FIRST  ,      **__magic_name__					:   int  ,      )					->    PIL.Image.Image:


					"""simple docstring"""

					__snake_case					:      Dict							      =     do_resize if do_resize is not None else self.do_resize
					__snake_case					:      List[str]							      =     size if size is not None else self.size
					__snake_case					:      Dict							      =     get_size_dict(__magic_name__			)
					__snake_case					:      List[Any]							      =     resample if resample is not None else self.resample
					__snake_case					:      Union[str, Any]							      =     do_rescale if do_rescale is not None else self.do_rescale
					__snake_case					:      int							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case					:      Optional[int]							      =     do_normalize if do_normalize is not None else self.do_normalize
					__snake_case					:      int							      =     image_mean if image_mean is not None else self.image_mean
					__snake_case					:      Dict							      =     image_std if image_std is not None else self.image_std
					__snake_case					:      Dict							      =     apply_ocr if apply_ocr is not None else self.apply_ocr
					__snake_case					:      Optional[Any]							      =     ocr_lang if ocr_lang is not None else self.ocr_lang
					__snake_case					:      List[str]							      =     tesseract_config if tesseract_config is not None else self.tesseract_config

					__snake_case					:      str							      =     make_list_of_images(__magic_name__			)

					if not valid_images(__magic_name__			):
								raise ValueError(
								    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
								    """torch.Tensor, tf.Tensor or jax.ndarray."""			)

					if do_resize and size is None:
								raise ValueError("""Size must be specified if do_resize is True."""			)

					if do_rescale and rescale_factor is None:
								raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

					if do_normalize and (image_mean is None or image_std is None):
								raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""			)

					# All transformations expect numpy arrays.
					__snake_case					:      Optional[Any]							      =     [to_numpy_array(__magic_name__			) for image in images]

					# Tesseract OCR to get words + normalized bounding boxes
					if apply_ocr:
								requires_backends(self  ,      """pytesseract"""			)
								__snake_case					:      str							      =     []
								__snake_case					:      Union[str, Any]							      =     []
								for image in images:
											__snake_case      ,     __snake_case					:      List[Any]							      =     apply_tesseract(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
											words_batch.append(__magic_name__			)
											boxes_batch.append(__magic_name__			)

					if do_resize:
								__snake_case					:      Union[str, Any]							      =     [self.resize(image=__magic_name__  ,      size=__magic_name__  ,      resample=__magic_name__			) for image in images]

					if do_rescale:
								__snake_case					:      str							      =     [self.rescale(image=__magic_name__  ,      scale=__magic_name__			) for image in images]

					if do_normalize:
								__snake_case					:      Optional[int]							      =     [self.normalize(image=__magic_name__  ,      mean=__magic_name__  ,      std=__magic_name__			) for image in images]

					__snake_case					:      int							      =     [to_channel_dimension_format(__magic_name__  ,      __magic_name__			) for image in images]

					__snake_case					:      Union[str, Any]							      =     BatchFeature(data={"""pixel_values""": images}  ,      tensor_type=__magic_name__			)

					if apply_ocr:
								__snake_case					:      str							      =     words_batch
								__snake_case					:      List[Any]							      =     boxes_batch
					return data









'''simple docstring'''






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


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




class  _A (					__lowercase		):

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


					"""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=__magic_name__  ,      speech_processor=__magic_name__  ,      vae=__magic_name__  ,      text_encoder=__magic_name__  ,      tokenizer=__magic_name__  ,      unet=__magic_name__  ,      scheduler=__magic_name__  ,      feature_extractor=__magic_name__  ,      )

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


					"""simple docstring"""

					if slice_size == "auto":
								__snake_case					:      str							      =     self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					self.enable_attention_slicing(__magic_name__			)

		@torch.no_grad()
		def __call__(   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   Dict=1_60_00  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 50  ,      __magic_name__					:   float = 7.5  ,      __magic_name__					:   Optional[Union[str, List[str]]] = None  ,      __magic_name__					:   Optional[int] = 1  ,      __magic_name__					:   float = 0.0  ,      __magic_name__					:   Optional[torch.Generator] = None  ,      __magic_name__					:   Optional[torch.FloatTensor] = None  ,      __magic_name__					:   Optional[str] = "pil"  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,      __magic_name__					:   int = 1  ,      **__magic_name__					:   List[str]  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.speech_processor.feature_extractor(
					    __magic_name__  ,      return_tensors="""pt"""  ,      sampling_rate=__magic_name__			).input_features.to(self.device			)
					__snake_case					:      List[str]							      =     self.speech_model.generate(__magic_name__  ,      max_length=48_00_00			)

					__snake_case					:      List[Any]							      =     self.speech_processor.tokenizer.batch_decode(__magic_name__  ,      skip_special_tokens=__magic_name__  ,      normalize=__magic_name__			)[
					    0
					]

					if isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Tuple							      =     1
					elif isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Optional[int]							      =     len(__magic_name__			)
					else:
								raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__magic_name__			)}'''			)

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

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

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
								__snake_case					:      Tuple							      =     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					:      Any							      =     text_input_ids[:, : self.tokenizer.model_max_length]
					__snake_case					:      int							      =     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					:      Any							      =     text_embeddings.shape
					__snake_case					:      List[Any]							      =     text_embeddings.repeat(1  ,      __magic_name__  ,      1			)
					__snake_case					:      Dict							      =     text_embeddings.view(bs_embed * num_images_per_prompt  ,      __magic_name__  ,      -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					:      Optional[int]							      =     guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
								__snake_case					:      List[str]
								if negative_prompt is None:
											__snake_case					:      Optional[Any]							      =     [""""""] * batch_size
								elif type(__magic_name__			) is not type(__magic_name__			):
											raise TypeError(
											    f'''`negative_prompt` should be the same type to `prompt`, but got {type(__magic_name__			)} !='''
											    f''' {type(__magic_name__			)}.'''			)
								elif isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      Dict							      =     [negative_prompt]
								elif batch_size != len(__magic_name__			):
											raise ValueError(
											    f'''`negative_prompt`: {negative_prompt} has batch size {len(__magic_name__			)}, 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					:      int							      =     negative_prompt

								__snake_case					:      List[str]							      =     text_input_ids.shape[-1]
								__snake_case					:      Any							      =     self.tokenizer(
								    __magic_name__  ,      padding="""max_length"""  ,      max_length=__magic_name__  ,      truncation=__magic_name__  ,      return_tensors="""pt"""  ,      )
								__snake_case					:      Dict							      =     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					:      Optional[int]							      =     uncond_embeddings.shape[1]
								__snake_case					:      Union[str, Any]							      =     uncond_embeddings.repeat(1  ,      __magic_name__  ,      1			)
								__snake_case					:      Tuple							      =     uncond_embeddings.view(batch_size * num_images_per_prompt  ,      __magic_name__  ,      -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					:      Dict							      =     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					:      List[Any]							      =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					__snake_case					:      List[Any]							      =     text_embeddings.dtype
					if latents is None:
								if self.device.type == "mps":
											# randn does not exist on mps
											__snake_case					:      Optional[int]							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device="""cpu"""  ,      dtype=__magic_name__			).to(
											    self.device			)
								else:
											__snake_case					:      int							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device=self.device  ,      dtype=__magic_name__			)
					else:
								if latents.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''			)
								__snake_case					:      List[str]							      =     latents.to(self.device			)

					# set timesteps
					self.scheduler.set_timesteps(__magic_name__			)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					__snake_case					:      Optional[int]							      =     self.scheduler.timesteps.to(self.device			)

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

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

								# predict the noise residual
								__snake_case					:      Tuple							      =     self.unet(__magic_name__  ,      __magic_name__  ,      encoder_hidden_states=__magic_name__			).sample

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

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

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

					__snake_case					:      int							      =     1 / 0.18215 * latents
					__snake_case					:      Optional[Any]							      =     self.vae.decode(__magic_name__			).sample

					__snake_case					:      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
					__snake_case					:      Any							      =     image.cpu().permute(0  ,      2  ,      3  ,      1			).float().numpy()

					if output_type == "pil":
								__snake_case					:      Tuple							      =     self.numpy_to_pil(__magic_name__			)

					if not return_dict:
								return image

					return StableDiffusionPipelineOutput(images=__magic_name__  ,      nsfw_content_detected=__magic_name__			)


