import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


__A      =				{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

__A      =				logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"maskformer"
							A_						  =					{"hidden_size": "mask_feature_size"}
							A_						  =					["resnet", "swin"]
							A_						  =					["detr"]

							def __init__(  self:   int		,     __A:   int = 2_56		,     __A:   int = 2_56		,     __A:   float = 0.1		,     __A:   bool = False		,     __A:   Optional[Dict] = None		,     __A:   Optional[Dict] = None		,     __A:   float = 0.02		,     __A:   float = 1.0		,     __A:   float = 1.0		,     __A:   float = 1.0		,     __A:   float = 20.0		,     __A:   Optional[bool] = None		,     **__A:   Optional[Any]		,     )    ->				List[Any]:
												if backbone_config is None:
																	# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																	_A								= SwinConfig(
																	    image_size=3_84		,     in_channels=3		,     patch_size=4		,     embed_dim=1_28		,     depths=[2, 2, 18, 2]		,     num_heads=[4, 8, 16, 32]		,     window_size=12		,     drop_path_rate=0.3		,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']		,     )

												if isinstance(__A		,     __A   ):
																	_A								= backbone_config.pop('''model_type'''   )
																	_A								= CONFIG_MAPPING[backbone_model_type]
																	_A								= config_class.from_dict(__A   )

												# verify that the backbone is supported
												if backbone_config.model_type not in self.backbones_supported:
																	logger.warning_once(
																	    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
																	    f"""Supported model types: {",".join(self.backbones_supported   )}"""   )

												if decoder_config is None:
																	# fall back to https://huggingface.co/facebook/detr-resnet-50
																	_A								= DetrConfig()
												else:
																	# verify that the decoder is supported
																	_A								= (
																	    decoder_config.pop('''model_type'''   ) if isinstance(__A		,     __A   ) else decoder_config.model_type
																	)
																	if decoder_type not in self.decoders_supported:
																						raise ValueError(
																						    f"""Transformer Decoder {decoder_type} not supported, please use one of"""
																						    f""" {",".join(self.decoders_supported   )}"""   )
																	if isinstance(__A		,     __A   ):
																						_A								= CONFIG_MAPPING[decoder_type]
																						_A								= config_class.from_dict(__A   )

												_A								= backbone_config
												_A								= decoder_config
												# main feature dimension for the model
												_A								= fpn_feature_size
												_A								= mask_feature_size
												# initializer
												_A								= init_std
												_A								= init_xavier_std
												# Hungarian matcher && loss
												_A								= cross_entropy_weight
												_A								= dice_weight
												_A								= mask_weight
												_A								= use_auxiliary_loss
												_A								= no_object_weight
												_A								= output_auxiliary_logits

												_A								= self.decoder_config.encoder_attention_heads
												_A								= self.decoder_config.num_hidden_layers
												super().__init__(**__A   )

							@classmethod
							def       __A (  cls:   Dict		,     __A:   PretrainedConfig		,     __A:   PretrainedConfig		,     **__A:   Any   )    ->				int:
												return cls(
												    backbone_config=__A		,     decoder_config=__A		,     **__A		,     )



							def       __A (  self:   int   )    ->				Dict[str, any]:
												_A								= copy.deepcopy(self.__dict__   )
												_A								= self.backbone_config.to_dict()
												_A								= self.decoder_config.to_dict()
												_A								= self.__class__.model_type
												return output











import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__A      =				'\\n    Text data.\n    Second line of data.'

__A      =				'file'




@pytest.fixture(scope='''session''' )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / (FILE_PATH + '''.zstd''')
					_A								= bytes(_lowercase					,			'''utf-8''' )
					with zstd.open(_lowercase					,			'''wb''' ) as f:
										f.write(_lowercase )
					return path




@pytest.fixture
def      __A					(     _lowercase ):

					'''simple docstring'''







					with open(os.path.join(tmpfs.local_root_dir					,			_lowercase )					,			'''w''' ) as f:
										f.write(_lowercase )
					return FILE_PATH




@pytest.mark.parametrize('''compression_format'''					,			['''gzip''', '''xz''', '''zstd'''] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
					_A								= input_paths[compression_format]
					_A								= tmp_path / '''cache'''
					_A								= DownloadConfig(cache_dir=_lowercase					,			extract_compressed_file=_lowercase )
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					with open(_lowercase ) as f:
										_A								= f.read()
					with open(_lowercase ) as f:
										_A								= f.read()
					assert extracted_file_content == expected_file_content




@pytest.mark.parametrize('''default_extracted'''					,			[True, False] )
@pytest.mark.parametrize('''default_cache_dir'''					,			[True, False] )
def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= '''custom_cache'''
					_A								= '''custom_extracted_dir'''
					_A								= tmp_path / '''custom_extracted_path'''
					if default_extracted:
										_A								= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
					else:
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''					,			_lowercase )
										monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''					,			str(_lowercase ) )
										_A								= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

					_A								= xz_file
					_A								= (
					    DownloadConfig(extract_compressed_file=_lowercase )
					    if default_cache_dir
					    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir					,			extract_compressed_file=_lowercase )
					)
					_A								= cached_path(_lowercase					,			download_config=_lowercase )
					assert Path(_lowercase ).parent.parts[-2:] == expected




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(Path(_lowercase ).resolve() )
					assert cached_path(_lowercase ) == text_file
					# relative path
					_A								= str(Path(_lowercase ).resolve().relative_to(Path(os.getcwd() ) ) )
					assert cached_path(_lowercase ) == text_file




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= str(tmp_path.resolve() / '''__missing_file__.txt''' )
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )
					# relative path
					_A								= '''./__missing_file__.txt'''
					with pytest.raises(_lowercase ):
										cached_path(_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= get_from_cache(f"""tmp://{tmpfs_file}""" )
					with open(_lowercase ) as f:
										_A								= f.read()
					assert output_file_content == FILE_CONTENT




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     ):

					'''simple docstring'''







					with pytest.raises(_lowercase ):
										cached_path('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										http_get('''https://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										http_head('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										ftp_get('''ftp://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										ftp_head('''ftp://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			_lowercase )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= tmp_path_factory.mktemp('''data''' ) / '''file.html'''
					with pytest.raises(_lowercase ):
										fsspec_get('''s3://huggingface.co'''					,			temp_file=_lowercase )
					with pytest.raises(_lowercase ):
										fsspec_head('''s3://huggingface.co''' )



