
"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







class 							__magic_name__     (		lowercase__      ,		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   'maskformer-swin'

					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   {
					    'num_attention_heads': 'num_heads',
					    'num_hidden_layers': 'num_layers',
					}





					def __init__(				self   :							Optional[Any]							,						snake_case_   :							Any=224							,						snake_case_   :							Optional[int]=4							,						snake_case_   :							Any=3							,						snake_case_   :							List[str]=96							,						snake_case_   :							str=[2, 2, 6, 2]							,						snake_case_   :							Any=[3, 6, 12, 24]							,						snake_case_   :							List[Any]=7							,						snake_case_   :							List[Any]=4.0							,						snake_case_   :							str=True							,						snake_case_   :							List[Any]=0.0							,						snake_case_   :							Any=0.0							,						snake_case_   :							Any=0.1							,						snake_case_   :							Optional[int]="gelu"							,						snake_case_   :							str=False							,						snake_case_   :							Optional[int]=0.02							,						snake_case_   :							str=1e-5							,						snake_case_   :							Union[str, Any]=None							,						snake_case_   :							List[str]=None							,						**snake_case_   :							List[str]							,						):
												super().__init__(**snake_case_    )

												__snake_case     			=       image_size
												__snake_case     			=       patch_size
												__snake_case     			=       num_channels
												__snake_case     			=       embed_dim
												__snake_case     			=       depths
												__snake_case     			=       len(snake_case_    )
												__snake_case     			=       num_heads
												__snake_case     			=       window_size
												__snake_case     			=       mlp_ratio
												__snake_case     			=       qkv_bias
												__snake_case     			=       hidden_dropout_prob
												__snake_case     			=       attention_probs_dropout_prob
												__snake_case     			=       drop_path_rate
												__snake_case     			=       hidden_act
												__snake_case     			=       use_absolute_embeddings
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       initializer_range
												# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
												# this indicates the channel dimension after the last stage of the model
												__snake_case     			=       int(embed_dim * 2 ** (len(snake_case_    ) - 1)    )
												__snake_case     			=       ["stem"] + [F'''stage{idx}''' for idx in range(1							,						len(snake_case_    ) + 1    )]
												__snake_case			,   __snake_case     			=       get_aligned_output_features_output_indices(
												    out_features=snake_case_							,						out_indices=snake_case_							,						stage_names=self.stage_names    )


"""simple docstring"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
