





from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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__  :     Dict   			=logging.get_logger(__name__)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					str:
				return [
				    int(10_00 * (box[0] / width)			),
				    int(10_00 * (box[1] / height)			),
				    int(10_00 * (box[2] / width)			),
				    int(10_00 * (box[3] / height)			),
				]





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = None			)  ->					Optional[int]:
				lowerCamelCase			=tesseract_config if tesseract_config is not None else """"""

				# apply OCR
				lowerCamelCase			=to_pil_image(__lowerCamelCase			)
				lowerCamelCase	,					lowerCamelCase			=pil_image.size
				lowerCamelCase			=pytesseract.image_to_data(__lowerCamelCase     , lang=__lowerCamelCase     , output_type="""dict"""     , config=__lowerCamelCase			)
				lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

				# filter empty words and corresponding coordinates
				lowerCamelCase			=[idx for idx, word in enumerate(__lowerCamelCase			) if not word.strip()]
				lowerCamelCase			=[word for idx, word in enumerate(__lowerCamelCase			) if idx not in irrelevant_indices]
				lowerCamelCase			=[coord for idx, coord in enumerate(__lowerCamelCase			) if idx not in irrelevant_indices]
				lowerCamelCase			=[coord for idx, coord in enumerate(__lowerCamelCase			) if idx not in irrelevant_indices]
				lowerCamelCase			=[coord for idx, coord in enumerate(__lowerCamelCase			) if idx not in irrelevant_indices]
				lowerCamelCase			=[coord for idx, coord in enumerate(__lowerCamelCase			) if idx not in irrelevant_indices]

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

				# finally, normalize the bounding boxes
				lowerCamelCase			=[]
				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       (  A_      ):
				__A								=			["""pixel_values"""]


				def __init__(						self				,	UpperCAmelCase_ = True				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = PILImageResampling.BILINEAR				,	UpperCAmelCase_ = True				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = ""				,	**UpperCAmelCase_				,	):
								super().__init__(**_lowerCamelCase							)
								lowerCamelCase			=size if size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase			=get_size_dict(_lowerCamelCase							)

								lowerCamelCase			=do_resize
								lowerCamelCase			=size
								lowerCamelCase			=resample
								lowerCamelCase			=apply_ocr
								lowerCamelCase			=ocr_lang
								lowerCamelCase			=tesseract_config


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_ = PILImageResampling.BILINEAR				,	UpperCAmelCase_ = None				,	**UpperCAmelCase_				,	):
								lowerCamelCase			=get_size_dict(_lowerCamelCase							)
								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()}"""							)
								lowerCamelCase			=(size["""height"""], size["""width"""])
								return resize(_lowerCamelCase				,	size=_lowerCamelCase				,	resample=_lowerCamelCase				,	data_format=_lowerCamelCase				,	**_lowerCamelCase							)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = ChannelDimension.FIRST				,	**UpperCAmelCase_				,	):
								lowerCamelCase			=do_resize if do_resize is not None else self.do_resize
								lowerCamelCase			=size if size is not None else self.size
								lowerCamelCase			=get_size_dict(_lowerCamelCase							)
								lowerCamelCase			=resample if resample is not None else self.resample
								lowerCamelCase			=apply_ocr if apply_ocr is not None else self.apply_ocr
								lowerCamelCase			=ocr_lang if ocr_lang is not None else self.ocr_lang
								lowerCamelCase			=tesseract_config if tesseract_config is not None else self.tesseract_config

								lowerCamelCase			=make_list_of_images(_lowerCamelCase							)

								if not valid_images(_lowerCamelCase							):
												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."""							)

								# All transformations expect numpy arrays.
								lowerCamelCase			=[to_numpy_array(_lowerCamelCase							) for image in images]

								if apply_ocr:
												requires_backends(self				,	"""pytesseract"""							)
												lowerCamelCase			=[]
												lowerCamelCase			=[]
												for image in images:
																lowerCamelCase	,					lowerCamelCase			=apply_tesseract(_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase							)
																words_batch.append(_lowerCamelCase							)
																boxes_batch.append(_lowerCamelCase							)

								if do_resize:
												lowerCamelCase			=[self.resize(image=_lowerCamelCase				,	size=_lowerCamelCase				,	resample=_lowerCamelCase							) for image in images]

								# flip color channels from RGB to BGR (as Detectron2 requires this)
								lowerCamelCase			=[flip_channel_order(_lowerCamelCase							) for image in images]
								lowerCamelCase			=[to_channel_dimension_format(_lowerCamelCase				,	_lowerCamelCase							) for image in images]

								lowerCamelCase			=BatchFeature(data={"""pixel_values""": images}				,	tensor_type=_lowerCamelCase							)

								if apply_ocr:
												lowerCamelCase			=words_batch
												lowerCamelCase			=boxes_batch
								return data












from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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

class  __A       (  a      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								super().__init__()
								self.register_modules(unet=UpperCAmelCase_				,	scheduler=UpperCAmelCase_							)


				@torch.no_grad()
				def __call__(						self				,	UpperCAmelCase_ = 1				,	UpperCAmelCase_ = 100				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = True				,	):

								if audio_length_in_s is None:
												lowerCamelCase			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase			=2 ** len(self.unet.up_blocks							)
								if sample_size < 3 * down_scale_factor:
												raise ValueError(
												    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
												    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""							)

								lowerCamelCase			=int(UpperCAmelCase_							)
								if sample_size % down_scale_factor != 0:
												lowerCamelCase			=(
												    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
												) * down_scale_factor
												logger.info(
												    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
												    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
												    """ process."""							)
								lowerCamelCase			=int(UpperCAmelCase_							)

								lowerCamelCase			=next(iter(self.unet.parameters()							)							).dtype
								lowerCamelCase			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(UpperCAmelCase_				,	UpperCAmelCase_							) and len(UpperCAmelCase_							) != batch_size:
												raise ValueError(
												    f"""You have passed a list of generators of length {len(UpperCAmelCase_							)}, but requested an effective batch"""
												    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

								lowerCamelCase			=randn_tensor(UpperCAmelCase_				,	generator=UpperCAmelCase_				,	device=self.device				,	dtype=UpperCAmelCase_							)

								# set step values
								self.scheduler.set_timesteps(UpperCAmelCase_				,	device=audio.device							)
								lowerCamelCase			=self.scheduler.timesteps.to(UpperCAmelCase_							)

								for t in self.progress_bar(self.scheduler.timesteps							):
												# 1. predict noise model_output
												lowerCamelCase			=self.unet(UpperCAmelCase_				,	UpperCAmelCase_							).sample

												# 2. compute previous image: x_t -> t_t-1
												lowerCamelCase			=self.scheduler.step(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							).prev_sample

								lowerCamelCase			=audio.clamp(-1				,	1							).float().cpu().numpy()

								lowerCamelCase			=audio[:, :, :original_sample_size]

								if not return_dict:
												return (audio,)

								return AudioPipelineOutput(audios=UpperCAmelCase_							)





