






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__			:    Union[str, Any]         =				False


A__			:    int         =				logging.get_logger(__name__)
A__			:    Optional[int]         =				'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(_UpperCamelCase	,     ['''torch''']       )
				_check_torch_version()

				lowercase__     				=    image_tensor.unsqueeze(0       )
				lowercase__     				=    torch.nn.functional.unfold(_UpperCamelCase	,     (patch_height, patch_width)	,     stride=(patch_height, patch_width)       )
				lowercase__     				=    patches.reshape(image_tensor.size(0       )	,     image_tensor.size(1       )	,     _UpperCamelCase	,     _UpperCamelCase	,     -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(_UpperCamelCase	,     '''vision'''       )
				# Add new lines so that each line is no more than 80 characters.

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

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

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

				# 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)	,     _UpperCamelCase       )
				lowercase__     				=    ImageDraw.Draw(_UpperCamelCase       )
				draw.text(xy=(left_padding, top_padding)	,     text=_UpperCamelCase	,     fill=_UpperCamelCase	,     font=_UpperCamelCase       )
				return image






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






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

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

				lowercase__     				=    render_text(_UpperCamelCase	,     **_UpperCamelCase       )
				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(_UpperCamelCase       )

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

				return new_image




class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""flattened_patches"""]




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

								'''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		:					Tuple,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Optional[Any] = None,						**lowerCamelCase		:					Dict						):

								'''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		:					Any,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					List[Any] = None,						lowerCamelCase		:					Union[str, Any] = None,						lowerCamelCase		:					Dict = None,						lowerCamelCase		:					Optional[Any] = None,						lowerCamelCase		:					Optional[Any] = None,						lowerCamelCase		:					List[str] = None,						lowerCamelCase		:					Dict = ChannelDimension.FIRST,						**lowerCamelCase		:					Tuple,						):

								'''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 argparse
import os
import re


A__			:    Optional[int]         =				'src/transformers'

# Pattern that looks at the indentation in a line.
A__			:    Union[str, Any]         =				re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__			:    List[str]         =				re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A__			:    List[Any]         =				re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
A__			:    int         =				re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A__			:    Tuple         =				re.compile(r'\[([^\]]+)\]')






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    _re_indent.search(lowerCamelCase_       )
				return "" if search is None else search.groups()[0]






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=""	,     lowerCamelCase_=None	,     lowerCamelCase_=None       ):






				'''simple docstring'''
				lowercase__     				=    0
				lowercase__     				=    code.split('''\n'''       )
				if start_prompt is not None:
								while not lines[index].startswith(lowerCamelCase_       ):
												index += 1
								lowercase__     				=    ['''\n'''.join(lines[:index]       )]
				else:
								lowercase__     				=    []

				# We split into blocks until we get to the `end_prompt` (or the end of the block).
				lowercase__     				=    [lines[index]]
				index += 1
				while index < len(lowerCamelCase_       ) and (end_prompt is None or not lines[index].startswith(lowerCamelCase_       )):
								if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
												if len(lowerCamelCase_       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ''' '''       ):
																current_block.append(lines[index]       )
																blocks.append('''\n'''.join(lowerCamelCase_       )       )
																if index < len(lowerCamelCase_       ) - 1:
																				lowercase__     				=    [lines[index + 1]]
																				index += 1
																else:
																				lowercase__     				=    []
												else:
																blocks.append('''\n'''.join(lowerCamelCase_       )       )
																lowercase__     				=    [lines[index]]
								else:
												current_block.append(lines[index]       )
								index += 1

				# Adds current block if it's nonempty.
				if len(lowerCamelCase_       ) > 0:
								blocks.append('''\n'''.join(lowerCamelCase_       )       )

				# Add final block after end_prompt if provided.
				if end_prompt is not None and index < len(lowerCamelCase_       ):
								blocks.append('''\n'''.join(lines[index:]       )       )

				return blocks






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				def _inner(lowerCamelCase_       ):
								return key(lowerCamelCase_       ).lower().replace('''_'''	,     ''''''       )

				return _inner






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None       ):






				'''simple docstring'''

				# If no key is provided, we use a noop.
				def noop(lowerCamelCase_       ):
								return x

				if key is None:
								lowercase__     				=    noop
				# Constants are all uppercase, they go first.
				lowercase__     				=    [obj for obj in objects if key(lowerCamelCase_       ).isupper()]
				# Classes are not all uppercase but start with a capital, they go second.
				lowercase__     				=    [obj for obj in objects if key(lowerCamelCase_       )[0].isupper() and not key(lowerCamelCase_       ).isupper()]
				# Functions begin with a lowercase, they go last.
				lowercase__     				=    [obj for obj in objects if not key(lowerCamelCase_       )[0].isupper()]

				lowercase__     				=    ignore_underscore(lowerCamelCase_       )
				return sorted(lowerCamelCase_	,     key=lowerCamelCase_       ) + sorted(lowerCamelCase_	,     key=lowerCamelCase_       ) + sorted(lowerCamelCase_	,     key=lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				# This inner function sort imports between [ ].
				def _replace(lowerCamelCase_       ):
								lowercase__     				=    match.groups()[0]
								if "," not in imports:
												return F"""[{imports}]"""
								lowercase__     				=    [part.strip().replace('''"'''	,     ''''''       ) for part in imports.split(''','''       )]
								# We will have a final empty element if the line finished with a comma.
								if len(keys[-1]       ) == 0:
												lowercase__     				=    keys[:-1]
								return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(lowerCamelCase_       )]       ) + "]"

				lowercase__     				=    import_statement.split('''\n'''       )
				if len(lowerCamelCase_       ) > 3:
								# Here we have to sort internal imports that are on several lines (one per name):
								# key: [
								#     "object1",
								#     "object2",
								#     ...
								# ]

								# We may have to ignore one or two lines on each side.
								lowercase__     				=    2 if lines[1].strip() == '''[''' else 1
								lowercase__     				=    [(i, _re_strip_line.search(lowerCamelCase_       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
								lowercase__     				=    sort_objects(lowerCamelCase_	,     key=lambda lowerCamelCase_       : x[1]       )
								lowercase__     				=    [lines[x[0] + idx] for x in sorted_indices]
								return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
				elif len(lowerCamelCase_       ) == 3:
								# Here we have to sort internal imports that are on one separate line:
								# key: [
								#     "object1", "object2", ...
								# ]
								if _re_bracket_content.search(lines[1]       ) is not None:
												lowercase__     				=    _re_bracket_content.sub(_replace	,     lines[1]       )
								else:
												lowercase__     				=    [part.strip().replace('''"'''	,     ''''''       ) for part in lines[1].split(''','''       )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]       ) == 0:
																lowercase__     				=    keys[:-1]
												lowercase__     				=    get_indent(lines[1]       ) + ''', '''.join([F"""\"{k}\"""" for k in sort_objects(lowerCamelCase_       )]       )
								return "\n".join(lowerCamelCase_       )
				else:
								# Finally we have to deal with imports fitting on one line
								lowercase__     				=    _re_bracket_content.sub(_replace	,     lowerCamelCase_       )
								return import_statement






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=True       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     encoding='''utf-8'''       ) as f:
								lowercase__     				=    f.read()

				if "_import_structure" not in code:
								return

				# Blocks of indent level 0
				lowercase__     				=    split_code_in_indented_blocks(
				    lowerCamelCase_	,     start_prompt='''_import_structure = {'''	,     end_prompt='''if TYPE_CHECKING:'''       )

				# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
				for block_idx in range(1	,     len(lowerCamelCase_       ) - 1       ):
								# Check if the block contains some `_import_structure`s thingy to sort.
								lowercase__     				=    main_blocks[block_idx]
								lowercase__     				=    block.split('''\n'''       )

								# Get to the start of the imports.
								lowercase__     				=    0
								while line_idx < len(lowerCamelCase_       ) and "_import_structure" not in block_lines[line_idx]:
												# Skip dummy import blocks
												if "import dummy" in block_lines[line_idx]:
																lowercase__     				=    len(lowerCamelCase_       )
												else:
																line_idx += 1
								if line_idx >= len(lowerCamelCase_       ):
												continue

								# Ignore beginning and last line: they don't contain anything.
								lowercase__     				=    '''\n'''.join(block_lines[line_idx:-1]       )
								lowercase__     				=    get_indent(block_lines[1]       )
								# Slit the internal block into blocks of indent level 1.
								lowercase__     				=    split_code_in_indented_blocks(lowerCamelCase_	,     indent_level=lowerCamelCase_       )
								# We have two categories of import key: list or _import_structure[key].append/extend
								lowercase__     				=    _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key
								# Grab the keys, but there is a trap: some lines are empty or just comments.
								lowercase__     				=    [(pattern.search(lowerCamelCase_       ).groups()[0] if pattern.search(lowerCamelCase_       ) is not None else None) for b in internal_blocks]
								# We only sort the lines with a key.
								lowercase__     				=    [(i, key) for i, key in enumerate(lowerCamelCase_       ) if key is not None]
								lowercase__     				=    [x[0] for x in sorted(lowerCamelCase_	,     key=lambda lowerCamelCase_       : x[1]       )]

								# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
								lowercase__     				=    0
								lowercase__     				=    []
								for i in range(len(lowerCamelCase_       )       ):
												if keys[i] is None:
																reorderded_blocks.append(internal_blocks[i]       )
												else:
																lowercase__     				=    sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
																reorderded_blocks.append(lowerCamelCase_       )
																count += 1

        # And we put our main block back together with its first and last line.
								lowercase__     				=    '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]       )

				if code != "\n".join(lowerCamelCase_       ):
								if check_only:
												return True
								else:
												print(F"""Overwriting {file}."""       )
												with open(lowerCamelCase_	,     '''w'''	,     encoding='''utf-8'''       ) as f:
																f.write('''\n'''.join(lowerCamelCase_       )       )






def 						a    (		lowerCamelCase_=True       ):






				'''simple docstring'''
				lowercase__     				=    []
				for root, _, files in os.walk(lowerCamelCase_       ):
								if "__init__.py" in files:
												lowercase__     				=    sort_imports(os.path.join(lowerCamelCase_	,     '''__init__.py'''       )	,     check_only=lowerCamelCase_       )
												if result:
																lowercase__     				=    [os.path.join(lowerCamelCase_	,     '''__init__.py'''       )]
				if len(lowerCamelCase_       ) > 0:
								raise ValueError(F"""Would overwrite {len(lowerCamelCase_       )} files, run `make style`."""       )


if __name__ == "__main__":
							A__			:    str         =				argparse.ArgumentParser()
							parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
							A__			:    int         =				parser.parse_args()

							sort_imports_in_all_inits(check_only=args.check_only)




