






import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

							if is_vision_available():
														from transformers import OneFormerImageProcessor
														from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
														from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
							from PIL import Image






def 						a    (		lowerCamelCase_	,     lowerCamelCase_="shi-labs/oneformer_demo"       ):






				'''simple docstring'''
				with open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='''dataset'''       )	,     '''r'''       ) as f:
								lowercase__     				=    json.load(lowerCamelCase_       )
				lowercase__     				=    {}
				lowercase__     				=    []
				lowercase__     				=    []
				for key, info in class_info.items():
								lowercase__     				=    info['''name''']
								class_names.append(info['''name''']       )
								if info["isthing"]:
												thing_ids.append(int(lowerCamelCase_       )       )
				lowercase__     				=    thing_ids
				lowercase__     				=    class_names
				return metadata
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					List[str],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					str=7,						lowerCamelCase		:					int=3,						lowerCamelCase		:					List[str]=30,						lowerCamelCase		:					Union[str, Any]=400,						lowerCamelCase		:					int=None,						lowerCamelCase		:					Union[str, Any]=True,						lowerCamelCase		:					Dict=True,						lowerCamelCase		:					int=[0.5, 0.5, 0.5],						lowerCamelCase		:					Dict=[0.5, 0.5, 0.5],						lowerCamelCase		:					List[str]=10,						lowerCamelCase		:					Tuple=False,						lowerCamelCase		:					Dict=255,						lowerCamelCase		:					Any="shi-labs/oneformer_demo",						lowerCamelCase		:					Tuple="ade20k_panoptic.json",						lowerCamelCase		:					List[Any]=10,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    {'''shortest_edge''': 32, '''longest_edge''': 1_333} if size is None else size
								lowercase__     				=    do_normalize
								lowercase__     				=    image_mean
								lowercase__     				=    image_std
								lowercase__     				=    class_info_file
								lowercase__     				=    prepare_metadata(lowerCamelCase,						lowerCamelCase						)
								lowercase__     				=    num_text
								lowercase__     				=    repo_path

								# for the post_process_functions
								lowercase__     				=    2
								lowercase__     				=    10
								lowercase__     				=    10
								lowercase__     				=    3
								lowercase__     				=    4
								lowercase__     				=    num_labels
								lowercase__     				=    do_reduce_labels
								lowercase__     				=    ignore_index




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "num_labels": self.num_labels,
								    "do_reduce_labels": self.do_reduce_labels,
								    "ignore_index": self.ignore_index,
								    "class_info_file": self.class_info_file,
								    "metadata": self.metadata,
								    "num_text": self.num_text,
								}




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

								'''simple docstring'''
								if not batched:
												lowercase__     				=    image_inputs[0]
												if isinstance(lowerCamelCase,						Image.Image						):
																lowercase__				,		lowercase__     				=    image.size
												else:
																lowercase__				,		lowercase__     				=    image.shape[1], image.shape[2]
												if w < h:
																lowercase__     				=    int(self.size['''shortest_edge'''] * h / w						)
																lowercase__     				=    self.size['''shortest_edge''']
												elif w > h:
																lowercase__     				=    self.size['''shortest_edge''']
																lowercase__     				=    int(self.size['''shortest_edge'''] * w / h						)
												else:
																lowercase__     				=    self.size['''shortest_edge''']
																lowercase__     				=    self.size['''shortest_edge''']

								else:
												lowercase__     				=    []
												for image in image_inputs:
																lowercase__				,		lowercase__     				=    self.get_expected_values([image]						)
																expected_values.append((expected_height, expected_width)						)
												lowercase__     				=    max(lowerCamelCase,						key=lambda lowerCamelCase						: item[0]						)[0]
												lowercase__     				=    max(lowerCamelCase,						key=lambda lowerCamelCase						: item[1]						)[1]

								return expected_height, expected_width




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								return OneFormerForUniversalSegmentationOutput(
								    # +1 for null class
								    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)						),						masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)						),						)



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
				# only for test_image_processing_common.test_image_proc_to_json_string
				lowercase__       							= image_processing_class




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    OneFormerImageProcessorTester(self						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self.image_processing_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''image_mean'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''image_std'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_normalize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''ignore_index'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''class_info_file'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''num_text'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''repo_path'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''metadata'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_reduce_labels'''						)						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								# Initialize image_processor
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_processing_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    image_processor(image_inputs[0],						['''semantic'''],						return_tensors='''pt'''						).pixel_values

								lowercase__				,		lowercase__     				=    self.image_processing_tester.get_expected_values(lowerCamelCase						)

								self.assertEqual(
								    encoded_images.shape,						(1, self.image_processing_tester.num_channels, expected_height, expected_width),						)

								# Test batched
								lowercase__				,		lowercase__     				=    self.image_processing_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase						)

								lowercase__     				=    image_processor(
								    lowerCamelCase,						['''semantic'''] * len(lowerCamelCase						),						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processing_tester.batch_size,
								        self.image_processing_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								# Initialize image_processor
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    prepare_image_inputs(self.image_processing_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						np.ndarray						)

								# Test not batched input
								lowercase__     				=    image_processor(image_inputs[0],						['''semantic'''],						return_tensors='''pt'''						).pixel_values

								lowercase__				,		lowercase__     				=    self.image_processing_tester.get_expected_values(lowerCamelCase						)

								self.assertEqual(
								    encoded_images.shape,						(1, self.image_processing_tester.num_channels, expected_height, expected_width),						)

								# Test batched
								lowercase__				,		lowercase__     				=    self.image_processing_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase						)

								lowercase__     				=    image_processor(
								    lowerCamelCase,						['''semantic'''] * len(lowerCamelCase						),						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processing_tester.batch_size,
								        self.image_processing_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								# Initialize image_processor
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    prepare_image_inputs(self.image_processing_tester,						equal_resolution=lowerCamelCase,						torchify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						torch.Tensor						)

								# Test not batched input
								lowercase__     				=    image_processor(image_inputs[0],						['''semantic'''],						return_tensors='''pt'''						).pixel_values

								lowercase__				,		lowercase__     				=    self.image_processing_tester.get_expected_values(lowerCamelCase						)

								self.assertEqual(
								    encoded_images.shape,						(1, self.image_processing_tester.num_channels, expected_height, expected_width),						)

								# Test batched
								lowercase__				,		lowercase__     				=    self.image_processing_tester.get_expected_values(lowerCamelCase,						batched=lowerCamelCase						)

								lowercase__     				=    image_processor(
								    lowerCamelCase,						['''semantic'''] * len(lowerCamelCase						),						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processing_tester.batch_size,
								        self.image_processing_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					List[str]=False,						lowerCamelCase		:					Any=False,						lowerCamelCase		:					int="np"						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# prepare image and target
								lowercase__     				=    self.image_processing_tester.num_labels
								lowercase__     				=    None
								lowercase__     				=    None
								lowercase__     				=    prepare_image_inputs(self.image_processing_tester,						equal_resolution=lowerCamelCase						)
								if with_segmentation_maps:
												lowercase__     				=    num_labels
												if is_instance_map:
																lowercase__     				=    list(range(lowerCamelCase						)						) * 2
																lowercase__     				=    dict(enumerate(lowerCamelCase						)						)
												lowercase__     				=    [
												    np.random.randint(0,						high * 2,						(img.size[1], img.size[0])						).astype(np.uinta						) for img in image_inputs
												]
												if segmentation_type == "pil":
																lowercase__     				=    [Image.fromarray(lowerCamelCase						) for annotation in annotations]

								lowercase__     				=    image_processor(
								    lowerCamelCase,						['''semantic'''] * len(lowerCamelCase						),						lowerCamelCase,						return_tensors='''pt''',						instance_id_to_semantic_id=lowerCamelCase,						pad_and_return_pixel_mask=lowerCamelCase,						)

								return inputs




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								def common(lowerCamelCase		:					Optional[Any]=False,						lowerCamelCase		:					Tuple=None						):
												lowercase__     				=    self.comm_get_image_processor_inputs(
												    with_segmentation_maps=lowerCamelCase,						is_instance_map=lowerCamelCase,						segmentation_type=lowerCamelCase						)

												lowercase__     				=    inputs['''mask_labels''']
												lowercase__     				=    inputs['''class_labels''']
												lowercase__     				=    inputs['''pixel_values''']
												lowercase__     				=    inputs['''text_inputs''']

												# check the batch_size
												for mask_label, class_label, text_input in zip(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						):
																self.assertEqual(mask_label.shape[0],						class_label.shape[0]						)
																# this ensure padding has happened
																self.assertEqual(mask_label.shape[1:],						pixel_values.shape[2:]						)
																self.assertEqual(len(lowerCamelCase						),						self.image_processing_tester.num_text						)

								common()
								common(is_instance_map=lowerCamelCase						)
								common(is_instance_map=lowerCamelCase,						segmentation_type='''pil'''						)
								common(is_instance_map=lowerCamelCase,						segmentation_type='''pil'''						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    np.zeros((20, 50)						)
								lowercase__     				=    1
								lowercase__     				=    1
								lowercase__     				=    1

								lowercase__     				=    binary_mask_to_rle(lowerCamelCase						)
								self.assertEqual(len(lowerCamelCase						),						4						)
								self.assertEqual(rle[0],						21						)
								self.assertEqual(rle[1],						45						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(
								    num_labels=self.image_processing_tester.num_classes,						max_seq_length=77,						task_seq_length=77,						class_info_file='''ade20k_panoptic.json''',						num_text=self.image_processing_tester.num_text,						repo_path='''shi-labs/oneformer_demo''',						)
								lowercase__     				=    self.image_processing_tester.get_fake_oneformer_outputs()

								lowercase__     				=    fature_extractor.post_process_semantic_segmentation(lowerCamelCase						)

								self.assertEqual(len(lowerCamelCase						),						self.image_processing_tester.batch_size						)
								self.assertEqual(
								    segmentation[0].shape,						(
								        self.image_processing_tester.height,
								        self.image_processing_tester.width,
								    ),						)

								lowercase__     				=    [(1, 4) for i in range(self.image_processing_tester.batch_size						)]
								lowercase__     				=    fature_extractor.post_process_semantic_segmentation(lowerCamelCase,						target_sizes=lowerCamelCase						)

								self.assertEqual(segmentation[0].shape,						target_sizes[0]						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(
								    num_labels=self.image_processing_tester.num_classes,						max_seq_length=77,						task_seq_length=77,						class_info_file='''ade20k_panoptic.json''',						num_text=self.image_processing_tester.num_text,						repo_path='''shi-labs/oneformer_demo''',						)
								lowercase__     				=    self.image_processing_tester.get_fake_oneformer_outputs()
								lowercase__     				=    image_processor.post_process_instance_segmentation(lowerCamelCase,						threshold=0						)

								self.assertTrue(len(lowerCamelCase						) == self.image_processing_tester.batch_size						)
								for el in segmentation:
												self.assertTrue('''segmentation''' in el						)
												self.assertTrue('''segments_info''' in el						)
												self.assertEqual(type(el['''segments_info''']						),						lowerCamelCase						)
												self.assertEqual(
												    el['''segmentation'''].shape,						(self.image_processing_tester.height, self.image_processing_tester.width)						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(
								    num_labels=self.image_processing_tester.num_classes,						max_seq_length=77,						task_seq_length=77,						class_info_file='''ade20k_panoptic.json''',						num_text=self.image_processing_tester.num_text,						repo_path='''shi-labs/oneformer_demo''',						)
								lowercase__     				=    self.image_processing_tester.get_fake_oneformer_outputs()
								lowercase__     				=    image_processor.post_process_panoptic_segmentation(lowerCamelCase,						threshold=0						)

								self.assertTrue(len(lowerCamelCase						) == self.image_processing_tester.batch_size						)
								for el in segmentation:
												self.assertTrue('''segmentation''' in el						)
												self.assertTrue('''segments_info''' in el						)
												self.assertEqual(type(el['''segments_info''']						),						lowerCamelCase						)
												self.assertEqual(
												    el['''segmentation'''].shape,						(self.image_processing_tester.height, self.image_processing_tester.width)						)












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)




