
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:
							lowercase__		:List[str]			  =   False


lowercase__		:Optional[Any]			  =   logging.get_logger(__name__)
lowercase__		:List[str]			  =   "ybelkada/fonts"



def 	UpperCamelCase   (							):
					'''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 	UpperCamelCase   (							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 	UpperCamelCase   (							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 	UpperCamelCase   (							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     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =['''flattened_patches''']




							def __init__(    self       ,A__ = True       ,A__ = True       ,A__ = None       ,A__ = 2_0_4_8       ,A__ = False       ,**A__       ,):
												super().__init__(**A__)
												lowercase						     =						patch_size if patch_size is not None else {'''height''': 1_6, '''width''': 1_6}
												lowercase						     =						do_normalize
												lowercase						     =						do_convert_rgb
												lowercase						     =						max_patches
												lowercase						     =						is_vqa




							def 			A__				(    self       ,A__       ,A__       ,A__       ,**A__):
												requires_backends(self.extract_flattened_patches       ,'''torch''')
												_check_torch_version()

												# convert to torch
												lowercase						     =						to_channel_dimension_format(A__       ,ChannelDimension.FIRST)
												lowercase						     =						torch.from_numpy(A__)

												lowercase     ,						lowercase						     =						patch_size['''height'''], patch_size['''width''']
												lowercase     ,						lowercase						     =						get_image_size(A__)

												# 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)       ,A__)       ,1)
												lowercase						     =						max(min(math.floor(scale * image_width / patch_width)       ,A__)       ,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=A__       ,antialias=A__       ,).squeeze(0)

												# [1, rows, columns, patch_height * patch_width * image_channels]
												lowercase						     =						torch_extract_patches(A__       ,A__       ,A__)

												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(A__).reshape([rows, 1]).repeat(1       ,A__).reshape([rows * columns, 1])
												lowercase						     =						torch.arange(A__).reshape([1, columns]).repeat(A__       ,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(A__       ,[0, 0, 0, max_patches - (rows * columns)]).float()

												lowercase						     =						to_numpy_array(A__)

												return result




							def 			A__				(    self       ,A__       ,A__ = None       ,**A__):
												if image.dtype == np.uinta:
																	lowercase						     =						image.astype(np.floataa)

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

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





							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
												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'''       ,A__) is not None:
																	raise ValueError('''data_format is not an accepted input as the outputs are ''')

												lowercase						     =						make_list_of_images(A__)

												if not valid_images(A__):
																	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(A__) for image in images]

												# All transformations expect numpy arrays.
												lowercase						     =						[to_numpy_array(A__) 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'''       ,A__)
																	lowercase						     =						kwargs.pop('''font_path'''       ,A__)

																	if isinstance(A__       ,A__):
																						lowercase						     =						[header_text] * len(A__)

																	lowercase						     =						[
																	    render_header(A__       ,header_text[i]       ,font_bytes=A__       ,font_path=A__)
																	    for i, image in enumerate(A__)
																	]

												if do_normalize:
																	lowercase						     =						[self.normalize(image=A__) for image in images]

												# convert to torch tensor and permute
												lowercase						     =						[
												    self.extract_flattened_patches(image=A__       ,max_patches=A__       ,patch_size=A__)
												    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=A__)

												return encoded_outputs






from pathlib import Path

import fire
from tqdm import tqdm



def 	UpperCamelCase   (							lowerCAmelCase__="ro"			,      lowerCAmelCase__="en"			,      lowerCAmelCase__="wmt16"			,      lowerCAmelCase__=None		):
					'''simple docstring'''
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''		)
					lowercase						     =						f'{src_lang}-{tgt_lang}'
					print(f'Converting {dataset}-{pair}'		)
					lowercase						     =						datasets.load_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)
					if save_dir is None:
										lowercase						     =						f'{dataset}-{pair}'
					lowercase						     =						Path(lowerCAmelCase__		)
					save_dir.mkdir(exist_ok=lowerCAmelCase__		)

					for split in ds.keys():
										print(f'Splitting {split} with {ds[split].num_rows} records'		)

										# to save to val.source, val.target like summary datasets
										lowercase						     =						'''val''' if split == '''validation''' else split
										lowercase						     =						save_dir.joinpath(f'{fn}.source'		)
										lowercase						     =						save_dir.joinpath(f'{fn}.target'		)
										lowercase						     =						src_path.open('''w+'''		)
										lowercase						     =						tgt_path.open('''w+'''		)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]		):
															lowercase						     =						x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''		)
															tgt_fp.write(ex[tgt_lang] + '''\n'''		)

					print(f'Saved {dataset} dataset to {save_dir}'		)


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)




