



import argparse
import os
import re


SCREAMING_SNAKE_CASE__       =      '''src/diffusers'''

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


def A						(       __UpperCamelCase					)						->      str:
			A__       =    _re_indent.search(__UpperCamelCase					)
			return "" if search is None else search.groups()[0]


def A						(       __UpperCamelCase ,	__UpperCamelCase="" ,	__UpperCamelCase=None ,	__UpperCamelCase=None					)						->      List[str]:
			A__       =    0
			A__       =    code.split('\n'					)
			if start_prompt is not None:
						while not lines[index].startswith(__UpperCamelCase					):
									index += 1
						A__       =    ['\n'.join(lines[:index]					)]
			else:
						A__       =    []

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

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

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

			return blocks


def A						(       __UpperCamelCase					)						->      str:

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

			return _inner


def A						(       __UpperCamelCase ,	__UpperCamelCase=None					)						->      Tuple:

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

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

			A__       =    ignore_underscore(__UpperCamelCase					)
			return sorted(__UpperCamelCase ,	key=__UpperCamelCase					) + sorted(__UpperCamelCase ,	key=__UpperCamelCase					) + sorted(__UpperCamelCase ,	key=__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      str:

			# This inner function sort imports between [ ].
			def _replace(__UpperCamelCase					):
						A__       =    match.groups()[0]
						if "," not in imports:
									return f'''[{imports}]'''
						A__       =    [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:
									A__       =    keys[:-1]
						return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(__UpperCamelCase					)]					) + "]"

			A__       =    import_statement.split('\n'					)
			if len(__UpperCamelCase					) > 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.
						A__       =    2 if lines[1].strip() == '[' else 1
						A__       =    [(i, _re_strip_line.search(__UpperCamelCase					).groups()[0]) for i, line in enumerate(lines[idx:-idx]					)]
						A__       =    sort_objects(__UpperCamelCase ,	key=lambda __UpperCamelCase					: x[1]					)
						A__       =    [lines[x[0] + idx] for x in sorted_indices]
						return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]					)
			elif len(__UpperCamelCase					) == 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:
									A__       =    _re_bracket_content.sub(_replace ,	lines[1]					)
						else:
									A__       =    [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:
												A__       =    keys[:-1]
									A__       =    get_indent(lines[1]					) + ', '.join([f'''"{k}"''' for k in sort_objects(__UpperCamelCase					)]					)
						return "\n".join(__UpperCamelCase					)
			else:
						# Finally we have to deal with imports fitting on one line
						A__       =    _re_bracket_content.sub(_replace ,	__UpperCamelCase					)
						return import_statement


def A						(       __UpperCamelCase ,	__UpperCamelCase=True					)						->      List[str]:
			with open(__UpperCamelCase ,	'r'					) as f:
						A__       =    f.read()

			if "_import_structure" not in code:
						return

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

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

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

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

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

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

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


def A						(       __UpperCamelCase=True					)						->      Union[str, Any]:
			A__       =    []
			for root, _, files in os.walk(__UpperCamelCase					):
						if "__init__.py" in files:
									A__       =    sort_imports(os.path.join(__UpperCamelCase ,	'__init__.py'					) ,	check_only=__UpperCamelCase					)
									if result:
												A__       =    [os.path.join(__UpperCamelCase ,	'__init__.py'					)]
			if len(__UpperCamelCase					) > 0:
						raise ValueError(f'''Would overwrite {len(__UpperCamelCase					)} files, run `make style`.'''					)


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

							sort_imports_in_all_inits(check_only=args.check_only)









import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
							from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       Any				,					_snake_case					:       Optional[int]=13				,					_snake_case					:       Optional[Any]=64				,					_snake_case					:       List[str]=2				,					_snake_case					:       Any=3				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       Dict=True				,					_snake_case					:       int=32				,					_snake_case					:       int=5				,					_snake_case					:       Union[str, Any]=4				,					_snake_case					:       int=37				,					_snake_case					:       Tuple="gelu"				,					_snake_case					:       Optional[int]=0.1				,					_snake_case					:       Dict=0.1				,					_snake_case					:       List[str]=10				,					_snake_case					:       Union[str, Any]=0.02				,					_snake_case					:       Dict=[1, 16, 4, 4]				,					_snake_case					:       Dict=None				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    image_size
								A__       =    patch_size
								A__       =    num_channels
								A__       =    is_training
								A__       =    use_labels
								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    type_sequence_label_size
								A__       =    initializer_range
								A__       =    scope
								A__       =    backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								A__       =    (self.image_size // 32) ** 2
								A__       =    num_patches + 1



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								A__       =    None
								if self.use_labels:
											A__       =    ids_tensor([self.batch_size]				,					self.type_sequence_label_size					)

								A__       =    self.get_config()

								return config, pixel_values, labels



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    {
								    'global_padding': 'same',
								    'layer_type': 'bottleneck',
								    'depths': [3, 4, 9],
								    'out_features': ['stage1', 'stage2', 'stage3'],
								    'embedding_dynamic_padding': True,
								    'hidden_sizes': [4, 8, 16, 32],
								    'num_groups': 2,
								}

								return ViTHybridConfig(
								    image_size=self.image_size				,					patch_size=self.patch_size				,					num_channels=self.num_channels				,					hidden_size=self.hidden_size				,					num_hidden_layers=self.num_hidden_layers				,					num_attention_heads=self.num_attention_heads				,					intermediate_size=self.intermediate_size				,					hidden_act=self.hidden_act				,					hidden_dropout_prob=self.hidden_dropout_prob				,					attention_probs_dropout_prob=self.attention_probs_dropout_prob				,					is_decoder=_snake_case				,					initializer_range=self.initializer_range				,					backbone_featmap_shape=self.backbone_featmap_shape				,					backbone_config=_snake_case				,					)



					def     _a							(     self					:       int				,					_snake_case					:       Optional[int]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ViTHybridModel(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)
								self.parent.assertEqual(result.last_hidden_state.shape				,					(self.batch_size, self.seq_length, self.hidden_size)					)



					def     _a							(     self					:       List[str]				,					_snake_case					:       str				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    self.type_sequence_label_size
								A__       =    ViTHybridForImageClassification(_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case				,					labels=_snake_case					)
								self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.type_sequence_label_size)					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.prepare_config_and_inputs()
								A__     , A__     , A__       =    config_and_inputs
								A__       =    {'pixel_values': pixel_values}
								return config, inputs_dict







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					A__				:		str			       =    (
					    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					A__				:		Union[str, Any]			       =    False
					A__				:		Any			       =    False
					A__				:		Union[str, Any]			       =    False



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    ViTHybridModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					@unittest.skip(reason='ViT does not use inputs_embeds'					)
					def     _a							(     self					:       int					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_snake_case					)
											self.assertIsInstance(model.get_input_embeddings()				,					(nn.Module)					)
											A__       =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_snake_case				,					nn.Linear					)					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_snake_case					)
											A__       =    inspect.signature(model.forward					)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											A__       =    [*signature.parameters.keys()]

											A__       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]				,					_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								A__       =    _config_zero_init(_snake_case					)
								for model_class in self.all_model_classes:
											A__       =    model_class(config=_snake_case					)
											# Skip the check for the backbone
											for name, module in model.named_modules():
														if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																	A__       =    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1E9).round() / 1E9).item()				,					[0.0, 1.0]				,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,					)



					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ViTHybridModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Union[str, Any]:
			A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image






@require_torch
@require_vision
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
								    if is_vision_available()
								    else None
								)



					@slow
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
								    _snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					).to(_snake_case					)

								# forward pass
								with torch.no_grad():
											A__       =    model(**_snake_case					)

								# verify the logits
								A__       =    torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape				,					_snake_case					)

								A__       =    torch.tensor([-1.9090, -0.4993, -0.2389]					).to(_snake_case					)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]				,					_snake_case				,					atol=1E-4					)					)



					@slow
					@require_accelerate
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'					)
								A__       =    ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'				,					device_map='auto'					)

								A__       =    prepare_img()

								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					)
								A__       =    model(**_snake_case					)
								A__       =    outputs.logits
								# model predicts one of the 1000 ImageNet classes
								A__       =    logits.argmax(-1					).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]				,					'tabby, tabby cat'					)




