

import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwiftFormerForImageClassification, SwiftFormerModel
	from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=13    ,       A__=3    ,       A__=True    ,       A__=True    ,       A__=0.1    ,       A__=0.1    ,       A__=224    ,       A__=1000    ,       A__=[3, 3, 6, 4]    ,       A__=[48, 56, 112, 220]    ,       ):
						A__							:    Any        =		parent
						A__							:    Dict        =		batch_size
						A__							:    Optional[Any]        =		num_channels
						A__							:    Tuple        =		is_training
						A__							:    Union[str, Any]        =		use_labels
						A__							:    Dict        =		hidden_dropout_prob
						A__							:    List[str]        =		attention_probs_dropout_prob
						A__							:    Any        =		num_labels
						A__							:    Union[str, Any]        =		image_size
						A__							:    List[Any]        =		layer_depths
						A__							:    Optional[int]        =		embed_dims






					def   __A			( self			):
						A__							:    str        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

						A__							:    Union[str, Any]        =		None
						if self.use_labels:
							A__							:    Optional[int]        =		ids_tensor([self.batch_size]    ,       self.num_labels			)

						A__							:    Optional[Any]        =		self.get_config()

						return config, pixel_values, labels






					def   __A			( self			):
						return SwiftFormerConfig(
						    depths=self.layer_depths    ,       embed_dims=self.embed_dims    ,       mlp_ratio=4    ,       downsamples=[True, True, True, True]    ,       hidden_act="""gelu"""    ,       num_labels=self.num_labels    ,       down_patch_size=3    ,       down_stride=2    ,       down_pad=1    ,       drop_rate=0.0    ,       drop_path_rate=0.0    ,       use_layer_scale=A__    ,       layer_scale_init_value=1e-5    ,       )






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						A__							:    List[Any]        =		SwiftFormerModel(config=A__			)
						model.to(A__			)
						model.eval()
						A__							:    str        =		model(A__			)
						self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, self.embed_dims[-1], 7, 7)			)






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						A__							:    Optional[Any]        =		self.num_labels
						A__							:    List[str]        =		SwiftFormerForImageClassification(A__			)
						model.to(A__			)
						model.eval()
						A__							:    Tuple        =		model(A__    ,       labels=A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.num_labels)			)

						A__							:    int        =		SwiftFormerForImageClassification(A__			)
						model.to(A__			)
						model.eval()

						A__							:    Any        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
						A__							:    Dict        =		model(A__			)
						self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.num_labels)			)






					def   __A			( self			):
						((A__)   ,   (A__)   ,   (A__))							:    Tuple        =		self.prepare_config_and_inputs()
						A__							:    Any        =		{"""pixel_values""": pixel_values}
						return config, inputs_dict



@require_torch
class     _a						(__magic_name__		,   __magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   Optional[int]  							=       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					UpperCAmelCase__:   int  							=       (
					    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__:   Optional[Any]  							=       False
					UpperCAmelCase__:   int  							=       False
					UpperCAmelCase__:   Optional[Any]  							=       False
					UpperCAmelCase__:   List[Any]  							=       False
					UpperCAmelCase__:   Union[str, Any]  							=       False






					def   __A			( self			):
						A__							:    Any        =		SwiftFormerModelTester(self			)
						A__							:    str        =		ConfigTester(
						    self    ,       config_class=A__    ,       has_text_modality=A__    ,       hidden_size=37    ,       num_attention_heads=12    ,       num_hidden_layers=12    ,       )






					def   __A			( self			):
						self.config_tester.run_common_tests()






					@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""			)
					def   __A			( self			):
						pass






					def   __A			( self			):
						A__   ,   A__							:    Any        =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:    Dict        =		model_class(A__			)
							A__							:    str        =		model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(A__    ,       nn.Linear			)			)






					def   __A			( self			):
						A__   ,   A__							:    List[str]        =		self.model_tester.prepare_config_and_inputs_for_common()

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

							A__							:    Any        =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]    ,       A__			)






					def   __A			( self			):
						A__							:    Any        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__			)






					def   __A			( self			):
						A__							:    int        =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__			)






					@slow
					def   __A			( self			):
						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:    Tuple        =		SwiftFormerModel.from_pretrained(A__			)
							self.assertIsNotNone(A__			)






					@unittest.skip(reason="""SwiftFormer does not output attentions"""			)
					def   __A			( self			):
						pass






					def   __A			( self			):
						def check_hidden_states_output(A__    ,       A__    ,       A__			):
							A__							:    Dict        =		model_class(A__			)
							model.to(A__			)
							model.eval()

							with torch.no_grad():
								A__							:    List[Any]        =		model(**self._prepare_for_class(A__    ,       A__			)			)

							A__							:    Union[str, Any]        =		outputs.hidden_states

							A__							:    Tuple        =		8
							self.assertEqual(len(A__			)    ,       A__			)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(A__			)			):
								self.assertEqual(
								    hidden_states[i].shape    ,       torch.Size(
								        [
								            self.model_tester.batch_size,
								            self.model_tester.embed_dims[i // 2],
								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
								            (self.model_tester.image_size // 4) // 2 ** (i // 2),
								        ]			)    ,       )

						A__   ,   A__							:    Optional[int]        =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:    int        =		True
							check_hidden_states_output(A__    ,       A__    ,       A__			)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A__							:    Union[str, Any]        =		True

							check_hidden_states_output(A__    ,       A__    ,       A__			)






					def   __A			( self			):
						def _config_zero_init(A__			):
							A__							:    Optional[int]        =		copy.deepcopy(A__			)
							for key in configs_no_init.__dict__.keys():
								if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
									setattr(A__    ,       A__    ,       1e-10			)
								if isinstance(getattr(A__    ,       A__    ,       A__			)    ,       A__			):
									A__							:    str        =		_config_zero_init(getattr(A__    ,       A__			)			)
									setattr(A__    ,       A__    ,       A__			)
							return configs_no_init

						A__   ,   A__							:    List[str]        =		self.model_tester.prepare_config_and_inputs_for_common()

						A__							:    Tuple        =		_config_zero_init(A__			)
						for model_class in self.all_model_classes:
							A__							:    List[str]        =		model_class(config=A__			)
							for name, param in model.named_parameters():
								if param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9) / 1e9).round().item()    ,       [0.0, 1.0]    ,       msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""    ,       )






					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
					def   __A			( self			):
						pass



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



@require_torch
@require_vision
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@cached_property
					def   __A			( self			):
						return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""			) if is_vision_available() else None






					@slow
					def   __A			( self			):
						A__							:    Optional[int]        =		SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""			).to(A__			)

						A__							:    Tuple        =		self.default_image_processor
						A__							:    Optional[Any]        =		prepare_img()
						A__							:    Tuple        =		image_processor(images=A__    ,       return_tensors="""pt"""			).to(A__			)

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

						# verify the logits
						A__							:    int        =		torch.Size((1, 1000)			)
						self.assertEqual(outputs.logits.shape    ,       A__			)

						A__							:    Tuple        =		torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]			).to(A__			)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,       A__    ,       atol=1e-4			)			)






import argparse
import os
import re


A_       : List[str]         =			'src/diffusers'

# 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_       : int         =			re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A_       : Optional[int]         =			re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
A_       : List[Any]         =			re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A_       : List[str]         =			re.compile(r'\[([^\]]+)\]')

def 						UpperCamelCase					(lowercase_: List[str]					)     ->      Dict:
	A__							:    Optional[Any]        =		_re_indent.search(lowercase_					)
	return "" if search is None else search.groups()[0]

def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: Any=""    ,      lowercase_: Any=None    ,      lowercase_: Any=None					)     ->      Tuple:
	A__							:    Optional[Any]        =		0
	A__							:    str        =		code.split("""\n"""					)
	if start_prompt is not None:
		while not lines[index].startswith(lowercase_					):
			index += 1
		A__							:    Tuple        =		["""\n""".join(lines[:index]					)]
	else:
		A__							:    Optional[Any]        =		[]

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

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

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

	return blocks

def 						UpperCamelCase					(lowercase_: str					)     ->      str:

	def _inner(lowercase_: Union[str, Any]					):
		return key(lowercase_					).lower().replace("""_"""    ,      """"""					)

	return _inner

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Any=None					)     ->      str:

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

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

	A__							:    Any        =		ignore_underscore(lowercase_					)
	return sorted(lowercase_    ,      key=lowercase_					) + sorted(lowercase_    ,      key=lowercase_					) + sorted(lowercase_    ,      key=lowercase_					)

def 						UpperCamelCase					(lowercase_: List[Any]					)     ->      List[Any]:

	# This inner function sort imports between [ ].
	def _replace(lowercase_: List[Any]					):
		A__							:    Tuple        =		match.groups()[0]
		if "," not in imports:
			return f"""[{imports}]"""
		A__							:    Optional[int]        =		[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__							:    Any        =		keys[:-1]
		return "[" + ", ".join([f"""\"{k}\"""" for k in sort_objects(lowercase_					)]					) + "]"

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

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: str=True					)     ->      Any:
	with open(lowercase_    ,      """r"""					) as f:
		A__							:    Optional[int]        =		f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	A__							:    Tuple        =		split_code_in_indented_blocks(
	    lowercase_    ,      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(lowercase_					) - 1					):
		# Check if the block contains some `_import_structure`s thingy to sort.
		A__							:    int        =		main_blocks[block_idx]
		A__							:    Optional[Any]        =		block.split("""\n"""					)

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

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

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

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

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

def 						UpperCamelCase					(lowercase_: Any=True					)     ->      Any:
	A__							:    Dict        =		[]
	for root, _, files in os.walk(lowercase_					):
		if "__init__.py" in files:
			A__							:    List[Any]        =		sort_imports(os.path.join(lowercase_    ,      """__init__.py"""					)    ,      check_only=lowercase_					)
			if result:
				A__							:    Optional[int]        =		[os.path.join(lowercase_    ,      """__init__.py"""					)]
	if len(lowercase_					) > 0:
		raise ValueError(f"""Would overwrite {len(lowercase_					)} files, run `make style`."""					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
	A_       : List[str]         =			parser.parse_args()

	sort_imports_in_all_inits(check_only=args.check_only)



