


'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


__snake_case							   =  get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				(
							    os.path.join(UpperCamelCase_					,     config.EXTRACTED_DATASETS_DIR							) if cache_dir else config.EXTRACTED_DATASETS_PATH
							)
							UpperCamelCase__     :Any							      =				Extractor




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							from .file_utils import hash_url_to_filename

							# Path where we extract compressed archives
							# We extract in the cache dir, and get the extracted path name by hashing the original path"
							UpperCamelCase__     :Union[str, Any]							      =				os.path.abspath(UpperCamelCase_							)
							return os.path.join(self.extract_dir					,     hash_url_to_filename(UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							return force_extract or (
							    not os.path.isfile(UpperCamelCase_							) and not (os.path.isdir(UpperCamelCase_							) and os.listdir(UpperCamelCase_							))
							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = False							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				self.extractor.infer_extractor_format(UpperCamelCase_							)
							if not extractor_format:
												return input_path
							UpperCamelCase__     :Any							      =				self._get_output_path(UpperCamelCase_							)
							if self._do_extract(UpperCamelCase_					,     UpperCamelCase_							):
												self.extractor.extract(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
							return output_path



class      lowercase			(							A__      ):

		"""simple docstring"""

		@classmethod
		@abstractmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							...




		@staticmethod
		@abstractmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							...



class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       []




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							with open(UpperCamelCase_					,     '''rb'''							) as f:
												return f.read(UpperCamelCase_							)




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     UpperCamelCase_ = b""							):

							'''simple docstring'''


							if not magic_number:
												UpperCamelCase__     :Optional[Any]							      =				max(len(UpperCamelCase_							) for cls_magic_number in cls.magic_numbers							)
												try:
																	UpperCamelCase__     :Dict							      =				cls.read_magic_number(UpperCamelCase_					,     UpperCamelCase_							)
												except OSError:
																	return False
							return any(magic_number.startswith(UpperCamelCase_							) for cls_magic_number in cls.magic_numbers							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return tarfile.is_tarfile(UpperCamelCase_							)




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							def resolved(UpperCamelCase_							) -> str:
												return os.path.realpath(os.path.abspath(UpperCamelCase_							)							)

							def badpath(UpperCamelCase_					,     UpperCamelCase_							) -> bool:
												# joinpath will ignore base if path is absolute
												return not resolved(os.path.join(UpperCamelCase_					,     UpperCamelCase_							)							).startswith(UpperCamelCase_							)

							def badlink(UpperCamelCase_					,     UpperCamelCase_							) -> bool:
												# Links are interpreted relative to the directory containing the link
												UpperCamelCase__     :List[str]							      =				resolved(os.path.join(UpperCamelCase_					,     os.path.dirname(info.name							)							)							)
												return badpath(info.linkname					,     base=UpperCamelCase_							)

							UpperCamelCase__     :List[str]							      =				resolved(UpperCamelCase_							)

							for finfo in members:
												if badpath(finfo.name					,     UpperCamelCase_							):
																	logger.error(F'''Extraction of {finfo.name} is blocked (illegal path)'''							)
												elif finfo.issym() and badlink(UpperCamelCase_					,     UpperCamelCase_							):
																	logger.error(F'''Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}'''							)
												elif finfo.islnk() and badlink(UpperCamelCase_					,     UpperCamelCase_							):
																	logger.error(F'''Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}'''							)
												else:
																	yield finfo




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							os.makedirs(UpperCamelCase_					,     exist_ok=UpperCamelCase_							)
							UpperCamelCase__     :str							      =				tarfile.open(UpperCamelCase_							)
							tar_file.extractall(UpperCamelCase_					,     members=TarExtractor.safemembers(UpperCamelCase_					,     UpperCamelCase_							)							)
							tar_file.close()



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [b'\x1F\x8B']




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							with gzip.open(UpperCamelCase_					,     '''rb'''							) as gzip_file:
												with open(UpperCamelCase_					,     '''wb'''							) as extracted_file:
																	shutil.copyfileobj(UpperCamelCase_					,     UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [
		    b'PK\x03\x04',
		    b'PK\x05\x06',  # empty archive
		    b'PK\x07\x08',  # spanned archive
		]




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     UpperCamelCase_ = b""							):

							'''simple docstring'''


							if super().is_extractable(UpperCamelCase_					,     magic_number=UpperCamelCase_							):
												return True
							try:
												# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
												# From: https://github.com/python/cpython/pull/5053
												from zipfile import (
												    _CD_SIGNATURE,
												    _ECD_DISK_NUMBER,
												    _ECD_DISK_START,
												    _ECD_ENTRIES_TOTAL,
												    _ECD_OFFSET,
												    _ECD_SIZE,
												    _EndRecData,
												    sizeCentralDir,
												    stringCentralDir,
												    structCentralDir,
												)

												with open(UpperCamelCase_					,     '''rb'''							) as fp:
																	UpperCamelCase__     :List[str]							      =				_EndRecData(UpperCamelCase_							)
																	if endrec:
																						if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
																											return True  # Empty zipfiles are still zipfiles
																						elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
																											fp.seek(endrec[_ECD_OFFSET]							)  # Central directory is on the same disk
																											if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
																																UpperCamelCase__     :Optional[Any]							      =				fp.read(UpperCamelCase_							)  # CD is where we expect it to be
																																if len(UpperCamelCase_							) == sizeCentralDir:
																																					UpperCamelCase__     :Any							      =				struct.unpack(UpperCamelCase_					,     UpperCamelCase_							)  # CD is the right size
																																					if centdir[_CD_SIGNATURE] == stringCentralDir:
																																										return True  # First central directory entry  has correct magic number
												return False
							except Exception:  # catch all errors in case future python versions change the zipfile internals
												return False




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							os.makedirs(UpperCamelCase_					,     exist_ok=UpperCamelCase_							)
							with zipfile.ZipFile(UpperCamelCase_					,     '''r'''							) as zip_file:
												zip_file.extractall(UpperCamelCase_							)
												zip_file.close()



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [b'\xFD\x37\x7A\x58\x5A\x00']




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							with lzma.open(UpperCamelCase_							) as compressed_file:
												with open(UpperCamelCase_					,     '''wb'''							) as extracted_file:
																	shutil.copyfileobj(UpperCamelCase_					,     UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [b'Rar!\x1a\x07\x00', b'Rar!\x1a\x07\x01\x00']  # RAR_ID  # RAR5_ID




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if not config.RARFILE_AVAILABLE:
												raise ImportError('''Please pip install rarfile'''							)
							import rarfile

							os.makedirs(UpperCamelCase_					,     exist_ok=UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				rarfile.RarFile(UpperCamelCase_							)
							rf.extractall(UpperCamelCase_							)
							rf.close()



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [b'\x28\xb5\x2F\xFD']




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if not config.ZSTANDARD_AVAILABLE:
												raise ImportError('''Please pip install zstandard'''							)
							import zstandard as zstd

							UpperCamelCase__     :Union[str, Any]							      =				zstd.ZstdDecompressor()
							with open(UpperCamelCase_					,     '''rb'''							) as ifh, open(UpperCamelCase_					,     '''wb'''							) as ofh:
												dctx.copy_stream(UpperCamelCase_					,     UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [b'\x42\x5A\x68']




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							with bza.open(UpperCamelCase_					,     '''rb'''							) as compressed_file:
												with open(UpperCamelCase_					,     '''wb'''							) as extracted_file:
																	shutil.copyfileobj(UpperCamelCase_					,     UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [b'\x37\x7A\xBC\xAF\x27\x1C']




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if not config.PY7ZR_AVAILABLE:
												raise ImportError('''Please pip install py7zr'''							)
							import pyazr

							os.makedirs(UpperCamelCase_					,     exist_ok=UpperCamelCase_							)
							with pyazr.SevenZipFile(UpperCamelCase_					,     '''r'''							) as archive:
												archive.extractall(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [b'\x04\x22\x4D\x18']




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if not config.LZ4_AVAILABLE:
												raise ImportError('''Please pip install lz4'''							)
							import lza.frame

							with lza.frame.open(UpperCamelCase_					,     '''rb'''							) as compressed_file:
												with open(UpperCamelCase_					,     '''wb'''							) as extracted_file:
																	shutil.copyfileobj(UpperCamelCase_					,     UpperCamelCase_							)



class      lowercase			:

		"""simple docstring"""

		_a									=       {
		    "tar": TarExtractor,
		    "gzip": GzipExtractor,
		    "zip": ZipExtractor,
		    "xz": XzExtractor,
		    "rar": RarExtractor,
		    "zstd": ZstdExtractor,
		    "bz2": BzipaExtractor,
		    "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
		    "lz4": LzaExtractor,  # <Added version="2.4.0"/>
		}




		@classmethod
		def 					lowerCAmelCase__       (	cls							):

							'''simple docstring'''


							return max(
							    len(UpperCamelCase_							)
							    for extractor in cls.extractors.values()
							    if issubclass(UpperCamelCase_					,     UpperCamelCase_							)
							    for extractor_magic_number in extractor.magic_numbers							)




		@staticmethod
		def 					lowerCAmelCase__       (	UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							try:
												return MagicNumberBaseExtractor.read_magic_number(UpperCamelCase_					,     magic_number_length=UpperCamelCase_							)
							except OSError:
												return b""




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     UpperCamelCase_ = False							):

							'''simple docstring'''


							warnings.warn(
							    '''Method \'is_extractable\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
							    '''Use \'infer_extractor_format\' instead.'''					,     category=UpperCamelCase_					,     )
							UpperCamelCase__     :Optional[Any]							      =				cls.infer_extractor_format(UpperCamelCase_							)
							if extractor_format:
												return True if not return_extractor else (True, cls.extractors[extractor_format])
							return False if not return_extractor else (False, None)




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_							):  # <Added version="2.4.0"/>

							'''simple docstring'''


							UpperCamelCase__     :str							      =				cls._get_magic_number_max_length()
							UpperCamelCase__     :List[str]							      =				cls._read_magic_number(UpperCamelCase_					,     UpperCamelCase_							)
							for extractor_format, extractor in cls.extractors.items():
												if extractor.is_extractable(UpperCamelCase_					,     magic_number=UpperCamelCase_							):
																	return extractor_format




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     UpperCamelCase_ = "deprecated"					,     ):

							'''simple docstring'''


							os.makedirs(os.path.dirname(UpperCamelCase_							)					,     exist_ok=UpperCamelCase_							)
							# Prevent parallel extractions
							UpperCamelCase__     :Dict							      =				str(Path(UpperCamelCase_							).with_suffix('''.lock'''							)							)
							with FileLock(UpperCamelCase_							):
												shutil.rmtree(UpperCamelCase_					,     ignore_errors=UpperCamelCase_							)
												if extractor_format or extractor != "deprecated":
																	if extractor != "deprecated" or not isinstance(UpperCamelCase_					,     UpperCamelCase_							):  # passed as positional arg
																						warnings.warn(
																						    '''Parameter \'extractor\' was deprecated in version 2.4.0 and will be removed in 3.0.0. '''
																						    '''Use \'extractor_format\' instead.'''					,     category=UpperCamelCase_					,     )
																						UpperCamelCase__     :List[str]							      =				extractor if extractor != '''deprecated''' else extractor_format
																	else:
																						UpperCamelCase__     :List[str]							      =				cls.extractors[extractor_format]
																	return extractor.extract(UpperCamelCase_					,     UpperCamelCase_							)
												else:
																	warnings.warn(
																	    '''Parameter \'extractor_format\' was made required in version 2.4.0 and not passing it will raise an '''
																	    '''exception in 3.0.0.'''					,     category=UpperCamelCase_					,     )
																	for extractor in cls.extractors.values():
																						if extractor.is_extractable(UpperCamelCase_							):
																											return extractor.extract(UpperCamelCase_					,     UpperCamelCase_							)



'''simple docstring'''
import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
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 (
		    FocalNetBackbone,
		    FocalNetForImageClassification,
		    FocalNetForMaskedImageModeling,
		    FocalNetModel,
		)
		from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_=13					,     UpperCamelCase_=32					,     UpperCamelCase_=2					,     UpperCamelCase_=3					,     UpperCamelCase_=16					,     UpperCamelCase_=[32, 64, 128]					,     UpperCamelCase_=[1, 2, 1]					,     UpperCamelCase_=[2, 2, 4]					,     UpperCamelCase_=2					,     UpperCamelCase_=2.0					,     UpperCamelCase_=True					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.1					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=False					,     UpperCamelCase_=True					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=True					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=10					,     UpperCamelCase_=8					,     UpperCamelCase_=["stage1", "stage2"]					,     UpperCamelCase_=[1, 2]					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				parent
							UpperCamelCase__     :Dict							      =				batch_size
							UpperCamelCase__     :Tuple							      =				image_size
							UpperCamelCase__     :Any							      =				patch_size
							UpperCamelCase__     :Tuple							      =				num_channels
							UpperCamelCase__     :int							      =				embed_dim
							UpperCamelCase__     :Optional[int]							      =				hidden_sizes
							UpperCamelCase__     :List[str]							      =				depths
							UpperCamelCase__     :List[str]							      =				num_heads
							UpperCamelCase__     :Dict							      =				window_size
							UpperCamelCase__     :str							      =				mlp_ratio
							UpperCamelCase__     :List[str]							      =				qkv_bias
							UpperCamelCase__     :Optional[Any]							      =				hidden_dropout_prob
							UpperCamelCase__     :List[Any]							      =				attention_probs_dropout_prob
							UpperCamelCase__     :List[str]							      =				drop_path_rate
							UpperCamelCase__     :str							      =				hidden_act
							UpperCamelCase__     :Optional[Any]							      =				use_absolute_embeddings
							UpperCamelCase__     :str							      =				patch_norm
							UpperCamelCase__     :List[Any]							      =				layer_norm_eps
							UpperCamelCase__     :Dict							      =				initializer_range
							UpperCamelCase__     :int							      =				is_training
							UpperCamelCase__     :List[Any]							      =				scope
							UpperCamelCase__     :Dict							      =				use_labels
							UpperCamelCase__     :Dict							      =				type_sequence_label_size
							UpperCamelCase__     :Tuple							      =				encoder_stride
							UpperCamelCase__     :Optional[int]							      =				out_features
							UpperCamelCase__     :Optional[int]							      =				out_indices




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

							UpperCamelCase__     :Any							      =				None
							if self.use_labels:
												UpperCamelCase__     :int							      =				ids_tensor([self.batch_size]					,     self.type_sequence_label_size							)

							UpperCamelCase__     :Tuple							      =				self.get_config()

							return config, pixel_values, labels




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return FocalNetConfig(
							    image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     embed_dim=self.embed_dim					,     hidden_sizes=self.hidden_sizes					,     depths=self.depths					,     num_heads=self.num_heads					,     window_size=self.window_size					,     mlp_ratio=self.mlp_ratio					,     qkv_bias=self.qkv_bias					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     drop_path_rate=self.drop_path_rate					,     hidden_act=self.hidden_act					,     use_absolute_embeddings=self.use_absolute_embeddings					,     path_norm=self.patch_norm					,     layer_norm_eps=self.layer_norm_eps					,     initializer_range=self.initializer_range					,     encoder_stride=self.encoder_stride					,     out_features=self.out_features					,     out_indices=self.out_indices					,     )




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModel(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :str							      =				model(UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
							UpperCamelCase__     :Optional[Any]							      =				int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

							self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, expected_seq_len, expected_dim)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Any							      =				model(UpperCamelCase_							)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps							)					,     len(config.out_features							)							)
							self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,     [self.batch_size, self.image_size, 8, 8]							)

							# verify channels
							self.parent.assertEqual(len(model.channels							)					,     len(config.out_features							)							)
							self.parent.assertListEqual(model.channels					,     config.hidden_sizes[:-1]							)

							# verify backbone works with out_features=None
							UpperCamelCase__     :Tuple							      =				None
							UpperCamelCase__     :Optional[Any]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[str]							      =				model(UpperCamelCase_							)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps							)					,     1							)
							self.parent.assertListEqual(list(result.feature_maps[0].shape							)					,     [self.batch_size, self.image_size * 2, 4, 4]							)

							# verify channels
							self.parent.assertEqual(len(model.channels							)					,     1							)
							self.parent.assertListEqual(model.channels					,     [config.hidden_sizes[-1]]							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				FocalNetForMaskedImageModeling(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(
							    result.reconstruction.shape					,     (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

							# test greyscale images
							UpperCamelCase__     :Optional[int]							      =				1
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForMaskedImageModeling(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :str							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.reconstruction.shape					,     (self.batch_size, 1, self.image_size, self.image_size)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.type_sequence_label_size
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :int							      =				model(UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)

							# test greyscale images
							UpperCamelCase__     :Tuple							      =				1
							UpperCamelCase__     :Tuple							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :Optional[int]							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Dict							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.prepare_config_and_inputs()

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				config_and_inputs
							UpperCamelCase__     :Any							      =				{'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class      lowercase			(							A__			,	A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (
		    (
		        FocalNetModel,
		        FocalNetForImageClassification,
		        FocalNetForMaskedImageModeling,
		        FocalNetBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		_a									=       (
		    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
		    if is_torch_available()
		    else {}
		)
		_a									=       False

		_a									=       False
		_a									=       False
		_a									=       False
		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				FocalNetModelTester(self							)
							UpperCamelCase__     :Optional[int]							      =				ConfigTester(self					,     config_class=UpperCamelCase_					,     embed_dim=37					,     has_text_modality=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_							)




		@unittest.skip(reason='''FocalNet does not use inputs_embeds'''							)
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		@unittest.skip(reason='''FocalNet does not use feedforward chunking'''							)
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :str							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :Optional[Any]							      =				model_class(UpperCamelCase_							)
												self.assertIsInstance(model.get_input_embeddings()					,     (nn.Module)							)
												UpperCamelCase__     :Any							      =				model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(UpperCamelCase_					,     nn.Linear							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :List[str]							      =				model_class(UpperCamelCase_							)
												UpperCamelCase__     :Dict							      =				inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase__     :Union[str, Any]							      =				[*signature.parameters.keys()]

												UpperCamelCase__     :Tuple							      =				['''pixel_values''']
												self.assertListEqual(arg_names[:1]					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				model_class(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							with torch.no_grad():
												UpperCamelCase__     :str							      =				model(**self._prepare_for_class(UpperCamelCase_					,     UpperCamelCase_							)							)

							UpperCamelCase__     :List[Any]							      =				outputs.hidden_states

							UpperCamelCase__     :Optional[int]							      =				getattr(
							    self.model_tester					,     '''expected_num_hidden_layers'''					,     len(self.model_tester.depths							) + 1							)
							self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

							# FocalNet has a different seq_length
							UpperCamelCase__     :List[str]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]							)					,     [num_patches, self.model_tester.embed_dim]					,     )

							UpperCamelCase__     :List[Any]							      =				outputs.reshaped_hidden_states
							self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				reshaped_hidden_states[0].shape
							UpperCamelCase__     :List[str]							      =				(
							    reshaped_hidden_states[0].view(UpperCamelCase_					,     UpperCamelCase_					,     height * width							).permute(0					,     2					,     1							)
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]							)					,     [num_patches, self.model_tester.embed_dim]					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Any							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :List[str]							      =				(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size					,     collections.abc.Iterable							)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :Union[str, Any]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCamelCase__     :List[Any]							      =				True

												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__     :Optional[int]							      =				3

							UpperCamelCase__     :str							      =				(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size					,     collections.abc.Iterable							)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							UpperCamelCase__     :Optional[int]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							UpperCamelCase__     :Dict							      =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :List[str]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     (padded_height, padded_width)							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCamelCase__     :List[str]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     (padded_height, padded_width)							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__     :Union[str, Any]							      =				FocalNetModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :Dict							      =				_config_zero_init(UpperCamelCase_							)
							for model_class in self.all_model_classes:
												UpperCamelCase__     :int							      =				model_class(config=UpperCamelCase_							)
												for name, param in model.named_parameters():
																	if "embeddings" not in name and param.requires_grad:
																						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'''					,     )



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

		"""simple docstring"""

		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''							) if is_vision_available() else None




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''							).to(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.default_image_processor

							UpperCamelCase__     :str							      =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							UpperCamelCase__     :Any							      =				image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							).to(UpperCamelCase_							)

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

							# verify the logits
							UpperCamelCase__     :Any							      =				torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				torch.tensor([0.2166, -0.4368, 0.2191]							).to(UpperCamelCase_							)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     UpperCamelCase_					,     atol=1e-4							)							)
							self.assertTrue(outputs.logits.argmax(dim=-1							).item()					,     281							)



@require_torch
class      lowercase			(							A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (FocalNetBackbone,) if is_torch_available() else ()
		_a									=       FocalNetConfig

		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModelTester(self							)