
import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
							import textwrap

							from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
							import torch

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							A__			:    Optional[Any]         =				False


A__			:    Optional[int]         =				logging.get_logger(__name__)
A__			:    Tuple         =				'ybelkada/fonts'






def 						a    (		):






				'''simple docstring'''
				if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
								raise ImportError(
								    F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
								    '''Pix2StructImageProcessor. Please upgrade torch.'''       )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				requires_backends(lowerCamelCase_	,     ['''torch''']       )
				_check_torch_version()

				lowercase__     				=    image_tensor.unsqueeze(0       )
				lowercase__     				=    torch.nn.functional.unfold(lowerCamelCase_	,     (patch_height, patch_width)	,     stride=(patch_height, patch_width)       )
				lowercase__     				=    patches.reshape(image_tensor.size(0       )	,     image_tensor.size(1       )	,     lowerCamelCase_	,     lowerCamelCase_	,     -1       )
				lowercase__     				=    patches.permute(0	,     4	,     2	,     3	,     1       ).reshape(
				    image_tensor.size(2       ) // patch_height	,     image_tensor.size(3       ) // patch_width	,     image_tensor.size(1       ) * patch_height * patch_width	,     )
				return patches.unsqueeze(0       )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_ = 36	,     lowerCamelCase_ = "black"	,     lowerCamelCase_ = "white"	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = 5	,     lowerCamelCase_ = None	,     lowerCamelCase_ = None	,     ):






				'''simple docstring'''
				requires_backends(lowerCamelCase_	,     '''vision'''       )
				# Add new lines so that each line is no more than 80 characters.

				lowercase__     				=    textwrap.TextWrapper(width=80       )
				lowercase__     				=    wrapper.wrap(text=lowerCamelCase_       )
				lowercase__     				=    '''\n'''.join(lowerCamelCase_       )

				if font_bytes is not None and font_path is None:
								lowercase__     				=    io.BytesIO(lowerCamelCase_       )
				elif font_path is not None:
								lowercase__     				=    font_path
				else:
								lowercase__     				=    hf_hub_download(lowerCamelCase_	,     '''Arial.TTF'''       )
				lowercase__     				=    ImageFont.truetype(lowerCamelCase_	,     encoding='''UTF-8'''	,     size=lowerCamelCase_       )

				# Use a temporary canvas to determine the width and height in pixels when
				# rendering the text.
				lowercase__     				=    ImageDraw.Draw(Image.new('''RGB'''	,     (1, 1)	,     lowerCamelCase_       )       )
				lowercase__				,		lowercase__				,		lowercase__				,		lowercase__     				=    temp_draw.textbbox((0, 0)	,     lowerCamelCase_	,     lowerCamelCase_       )

				# Create the actual image with a bit of padding around the text.
				lowercase__     				=    text_width + left_padding + right_padding
				lowercase__     				=    text_height + top_padding + bottom_padding
				lowercase__     				=    Image.new('''RGB'''	,     (image_width, image_height)	,     lowerCamelCase_       )
				lowercase__     				=    ImageDraw.Draw(lowerCamelCase_       )
				draw.text(xy=(left_padding, top_padding)	,     text=lowerCamelCase_	,     fill=lowerCamelCase_	,     font=lowerCamelCase_       )
				return image






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				requires_backends(lowerCamelCase_	,     '''vision'''       )

				# Convert to PIL image if necessary
				lowercase__     				=    to_pil_image(lowerCamelCase_       )

				lowercase__     				=    render_text(lowerCamelCase_	,     **lowerCamelCase_       )
				lowercase__     				=    max(header_image.width	,     image.width       )

				lowercase__     				=    int(image.height * (new_width / image.width)       )
				lowercase__     				=    int(header_image.height * (new_width / header_image.width)       )

				lowercase__     				=    Image.new('''RGB'''	,     (new_width, new_height + new_header_height)	,     '''white'''       )
				new_image.paste(header_image.resize((new_width, new_header_height)       )	,     (0, 0)       )
				new_image.paste(image.resize((new_width, new_height)       )	,     (0, new_header_height)       )

				# Convert back to the original framework if necessary
				lowercase__     				=    to_numpy_array(lowerCamelCase_       )

				if infer_channel_dimension_format(lowerCamelCase_       ) == ChannelDimension.LAST:
								lowercase__     				=    to_channel_dimension_format(lowerCamelCase_	,     ChannelDimension.LAST       )

				return new_image




class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""flattened_patches"""]




				def __init__(  self		:					str,						lowerCamelCase		:					bool = True,						lowerCamelCase		:					bool = True,						lowerCamelCase		:					Dict[str, int] = None,						lowerCamelCase		:					int = 2_048,						lowerCamelCase		:					bool = False,						**lowerCamelCase		:					Optional[Any],						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
								lowercase__     				=    do_normalize
								lowercase__     				=    do_convert_rgb
								lowercase__     				=    max_patches
								lowercase__     				=    is_vqa




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.ndarray,						lowerCamelCase		:					int,						lowerCamelCase		:					dict,						**lowerCamelCase		:					Dict						):

								'''simple docstring'''
								requires_backends(self.extract_flattened_patches,						'''torch'''						)
								_check_torch_version()

								# convert to torch
								lowercase__     				=    to_channel_dimension_format(lowerCamelCase,						ChannelDimension.FIRST						)
								lowercase__     				=    torch.from_numpy(lowerCamelCase						)

								lowercase__				,		lowercase__     				=    patch_size['''height'''], patch_size['''width''']
								lowercase__				,		lowercase__     				=    get_image_size(lowerCamelCase						)

								# maximize scale s.t.
								lowercase__     				=    math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)						)
								lowercase__     				=    max(min(math.floor(scale * image_height / patch_height						),						lowerCamelCase						),						1						)
								lowercase__     				=    max(min(math.floor(scale * image_width / patch_width						),						lowerCamelCase						),						1						)
								lowercase__     				=    max(num_feasible_rows * patch_height,						1						)
								lowercase__     				=    max(num_feasible_cols * patch_width,						1						)

								lowercase__     				=    torch.nn.functional.interpolate(
								    image.unsqueeze(0						),						size=(resized_height, resized_width),						mode='''bilinear''',						align_corners=lowerCamelCase,						antialias=lowerCamelCase,						).squeeze(0						)

								# [1, rows, columns, patch_height * patch_width * image_channels]
								lowercase__     				=    torch_extract_patches(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    patches.shape
								lowercase__     				=    patches_shape[1]
								lowercase__     				=    patches_shape[2]
								lowercase__     				=    patches_shape[3]

								# [rows * columns, patch_height * patch_width * image_channels]
								lowercase__     				=    patches.reshape([rows * columns, depth]						)

								# [rows * columns, 1]
								lowercase__     				=    torch.arange(lowerCamelCase						).reshape([rows, 1]						).repeat(1,						lowerCamelCase						).reshape([rows * columns, 1]						)
								lowercase__     				=    torch.arange(lowerCamelCase						).reshape([1, columns]						).repeat(lowerCamelCase,						1						).reshape([rows * columns, 1]						)

								# Offset by 1 so the ids do not contain zeros, which represent padding.
								row_ids += 1
								col_ids += 1

								# Prepare additional patch features.
								# [rows * columns, 1]
								lowercase__     				=    row_ids.to(torch.floataa						)
								lowercase__     				=    col_ids.to(torch.floataa						)

								# [rows * columns, 2 + patch_height * patch_width * image_channels]
								lowercase__     				=    torch.cat([row_ids, col_ids, patches],						-1						)

								# [max_patches, 2 + patch_height * patch_width * image_channels]
								lowercase__     				=    torch.nn.functional.pad(lowerCamelCase,						[0, 0, 0, max_patches - (rows * columns)]						).float()

								lowercase__     				=    to_numpy_array(lowerCamelCase						)

								return result




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.ndarray,						lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None,						**lowerCamelCase		:					int						):

								'''simple docstring'''
								if image.dtype == np.uinta:
												lowercase__     				=    image.astype(np.floataa						)

								# take mean across the whole `image`
								lowercase__     				=    np.mean(lowerCamelCase						)
								lowercase__     				=    np.std(lowerCamelCase						)
								lowercase__     				=    max(lowerCamelCase,						1.0 / math.sqrt(np.prod(image.shape						)						)						)

								return normalize(lowerCamelCase,						mean=lowerCamelCase,						std=lowerCamelCase,						**lowerCamelCase						)




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					ImageInput,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					bool = None,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Dict[str, int]] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						lowerCamelCase		:					ChannelDimension = ChannelDimension.FIRST,						**lowerCamelCase		:					Union[str, Any],						):

								'''simple docstring'''
								lowercase__     				=    do_normalize if do_normalize is not None else self.do_normalize
								lowercase__     				=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
								lowercase__     				=    patch_size if patch_size is not None else self.patch_size
								lowercase__     				=    max_patches if max_patches is not None else self.max_patches
								lowercase__     				=    self.is_vqa

								if kwargs.get('''data_format''',						lowerCamelCase						) is not None:
												raise ValueError('''data_format is not an accepted input as the outputs are '''						)

								lowercase__     				=    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.'''						)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowercase__     				=    [convert_to_rgb(lowerCamelCase						) for image in images]

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

								if is_vqa:
												if header_text is None:
																raise ValueError('''A header text must be provided for VQA models.'''						)
												lowercase__     				=    kwargs.pop('''font_bytes''',						lowerCamelCase						)
												lowercase__     				=    kwargs.pop('''font_path''',						lowerCamelCase						)

												if isinstance(lowerCamelCase,						lowerCamelCase						):
																lowercase__     				=    [header_text] * len(lowerCamelCase						)

												lowercase__     				=    [
												    render_header(lowerCamelCase,						header_text[i],						font_bytes=lowerCamelCase,						font_path=lowerCamelCase						)
												    for i, image in enumerate(lowerCamelCase						)
												]

								if do_normalize:
												lowercase__     				=    [self.normalize(image=lowerCamelCase						) for image in images]

								# convert to torch tensor and permute
								lowercase__     				=    [
								    self.extract_flattened_patches(image=lowerCamelCase,						max_patches=lowerCamelCase,						patch_size=lowerCamelCase						)
								    for image in images
								]

								# create attention mask in numpy
								lowercase__     				=    [(image.sum(axis=-1						) != 0).astype(np.floataa						) for image in images]

								lowercase__     				=    BatchFeature(
								    data={'''flattened_patches''': images, '''attention_mask''': attention_masks},						tensor_type=lowerCamelCase						)

								return encoded_outputs












import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A__			:    Tuple         =				logging.get_logger(__name__)
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""input_features""", """is_longer"""]




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					int=64,						lowerCamelCase		:					Union[str, Any]=48_000,						lowerCamelCase		:					str=480,						lowerCamelCase		:					Tuple=10,						lowerCamelCase		:					List[Any]=1_024,						lowerCamelCase		:					Optional[int]=0.0,						lowerCamelCase		:					Optional[Any]=False,						lowerCamelCase		:					float = 0,						lowerCamelCase		:					float = 14_000,						lowerCamelCase		:					int = None,						lowerCamelCase		:					str = "fusion",						lowerCamelCase		:					str = "repeatpad",						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__(
								    feature_size=lowerCamelCase,						sampling_rate=lowerCamelCase,						padding_value=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    top_db
								lowercase__     				=    truncation
								lowercase__     				=    padding
								lowercase__     				=    fft_window_size
								lowercase__     				=    (fft_window_size >> 1) + 1
								lowercase__     				=    hop_length
								lowercase__     				=    max_length_s
								lowercase__     				=    max_length_s * sampling_rate
								lowercase__     				=    sampling_rate
								lowercase__     				=    frequency_min
								lowercase__     				=    frequency_max
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm=lowerCamelCase,						mel_scale='''htk''',						)
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm='''slaney''',						mel_scale='''slaney''',						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    copy.deepcopy(self.__dict__						)
								lowercase__     				=    self.__class__.__name__
								if "mel_filters" in output:
												del output["mel_filters"]
								if "mel_filters_slaney" in output:
												del output["mel_filters_slaney"]
								return output




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.array,						lowerCamelCase		:					Optional[np.array] = None						):

								'''simple docstring'''
								lowercase__     				=    spectrogram(
								    lowerCamelCase,						window_function(self.fft_window_size,						'''hann'''						),						frame_length=self.fft_window_size,						hop_length=self.hop_length,						power=2.0,						mel_filters=lowerCamelCase,						log_mel='''dB''',						)
								return log_mel_spectrogram.T




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

								'''simple docstring'''
								lowercase__     				=    np.array_split(list(range(0,						total_frames - chunk_frames + 1						)						),						3						)
								if len(ranges[1]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								if len(ranges[2]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								# randomly choose index for each part
								lowercase__     				=    np.random.choice(ranges[0]						)
								lowercase__     				=    np.random.choice(ranges[1]						)
								lowercase__     				=    np.random.choice(ranges[2]						)

								lowercase__     				=    mel[idx_front : idx_front + chunk_frames, :]
								lowercase__     				=    mel[idx_middle : idx_middle + chunk_frames, :]
								lowercase__     				=    mel[idx_back : idx_back + chunk_frames, :]

								lowercase__     				=    torch.tensor(mel[None, None, :]						)
								lowercase__     				=    torch.nn.functional.interpolate(
								    lowerCamelCase,						size=[chunk_frames, 64],						mode='''bilinear''',						align_corners=lowerCamelCase						)
								lowercase__     				=    mel_shrink[0][0].numpy()
								lowercase__     				=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],						axis=0						)
								return mel_fusion




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.array,						lowerCamelCase		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								if waveform.shape[0] > max_length:
												if truncation == "rand_trunc":
																lowercase__     				=    True
																# random crop to max_length (for compatibility) -> this should be handled by self.pad
																lowercase__     				=    len(lowerCamelCase						) - max_length
																lowercase__     				=    np.random.randint(0,						overflow + 1						)
																lowercase__     				=    waveform[idx : idx + max_length]
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]
												elif truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																lowercase__     				=    mel.shape[0]
																if chunk_frames == total_frames:
																				# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																				# In this case, we just use the whole audio.
																				lowercase__     				=    np.stack([mel, mel, mel, mel],						axis=0						)
																				lowercase__     				=    False
																else:
																				lowercase__     				=    self._random_mel_fusion(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)
																				lowercase__     				=    True
												else:
																raise NotImplementedError(F"""data_truncating {truncation} not implemented"""						)

								else:
												lowercase__     				=    False
												# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
												if waveform.shape[0] < max_length:
																if padding == "repeat":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						n_repeat + 1						)						)[:max_length]
																if padding == "repeatpad":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						lowerCamelCase						)						)
																lowercase__     				=    np.pad(lowerCamelCase,						(0, max_length - waveform.shape[0]),						mode='''constant''',						constant_values=0						)

												if truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    np.stack([input_mel, input_mel, input_mel, input_mel],						axis=0						)
												else:
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]

								return input_mel, longer




				def __call__(  self		:					Union[str, Any],						lowerCamelCase		:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],						lowerCamelCase		:					str = None,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								lowercase__     				=    truncation if truncation is not None else self.truncation
								lowercase__     				=    padding if padding else self.padding

								if sampling_rate is not None:
												if sampling_rate != self.sampling_rate:
																raise ValueError(
																    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																    F""" was sampled with {self.sampling_rate} and not {sampling_rate}."""						)
								else:
												logger.warning(
												    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
												    '''Failing to do so can result in silent errors that might be hard to debug.'''						)

								lowercase__     				=    isinstance(lowerCamelCase,						np.ndarray						) and len(raw_speech.shape						) > 1
								if is_batched_numpy and len(raw_speech.shape						) > 2:
												raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""						)
								lowercase__     				=    is_batched_numpy or (
								    isinstance(lowerCamelCase,						(list, tuple)						) and (isinstance(raw_speech[0],						(np.ndarray, tuple, list)						))
								)

								if is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for speech in raw_speech]
								elif not is_batched and not isinstance(lowerCamelCase,						np.ndarray						):
												lowercase__     				=    np.asarray(lowerCamelCase,						dtype=np.floataa						)
								elif isinstance(lowerCamelCase,						np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
												lowercase__     				=    raw_speech.astype(np.floataa						)

								# always return batch
								if not is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase						)]

								# convert to mel spectrogram, truncate and pad if needed.
								lowercase__     				=    [
								    self._get_input_mel(lowerCamelCase,						max_length if max_length else self.nb_max_samples,						lowerCamelCase,						lowerCamelCase						)
								    for waveform in raw_speech
								]

								lowercase__     				=    []
								lowercase__     				=    []
								for mel, longer in padded_inputs:
												input_mel.append(lowerCamelCase						)
												is_longer.append(lowerCamelCase						)

								if truncation == "fusion" and sum(lowerCamelCase						) == 0:
												# if no audio is longer than 10s, then randomly select one audio to be longer
												lowercase__     				=    np.random.randint(0,						len(lowerCamelCase						)						)
												lowercase__     				=    True

								if isinstance(input_mel[0],						lowerCamelCase						):
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for feature in input_mel]

								# is_longer is a list of bool
								lowercase__     				=    [[longer] for longer in is_longer]

								lowercase__     				=    {'''input_features''': input_mel, '''is_longer''': is_longer}
								lowercase__     				=    BatchFeature(lowerCamelCase						)

								if return_tensors is not None:
												lowercase__     				=    input_features.convert_to_tensors(lowerCamelCase						)

								return input_features




