


'''simple docstring'''


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__    	=     get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class 	lowerCAmelCase__			(			__snake_case    ,       unittest.TestCase	):
			lowerCAmelCase :	Tuple          =     XLMRobertaTokenizer
			lowerCAmelCase :	List[Any]          =     XLMRobertaTokenizerFast
			lowerCAmelCase :	Optional[Any]          =     True
			lowerCAmelCase :	Dict          =     True




			def 							lowerCAmelCase__    (		self  :     Any )   ->int:



					'''simple docstring'''







					super().setUp()

					# We have a SentencePiece fixture for testing
					_UpperCAmelCase		:       Tuple								=      XLMRobertaTokenizer(__UpperCamelCase	,  keep_accents=__UpperCamelCase )
					tokenizer.save_pretrained(self.tmpdirname )




			def 							lowerCAmelCase__    (		self  :     str )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      "<pad>"
					_UpperCAmelCase		:       Dict								=      1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase )	,  __UpperCamelCase )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase )	,  __UpperCamelCase )




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      list(self.get_tokenizer().get_vocab().keys() )

					self.assertEqual(vocab_keys[0]	,  "<s>" )
					self.assertEqual(vocab_keys[1]	,  "<pad>" )
					self.assertEqual(vocab_keys[-1]	,  "<mask>" )
					self.assertEqual(len(__UpperCamelCase )	,  10_02 )




			def 							lowerCAmelCase__    (		self  :     Any )   ->str:



					'''simple docstring'''







					self.assertEqual(self.get_tokenizer().vocab_size	,  10_02 )




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      XLMRobertaTokenizer(__UpperCamelCase	,  keep_accents=__UpperCamelCase )

					_UpperCAmelCase		:       int								=      tokenizer.tokenize("This is a test" )
					self.assertListEqual(__UpperCamelCase	,  ["▁This", "▁is", "▁a", "▁t", "est"] )

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(__UpperCamelCase )	,  [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]	,  )

					_UpperCAmelCase		:       Dict								=      tokenizer.tokenize("I was born in 92000, and this is falsé." )
					self.assertListEqual(
					    __UpperCamelCase	,  [
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "9",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "é",
					        ".",
					    ]	,  )
					_UpperCAmelCase		:       Tuple								=      tokenizer.convert_tokens_to_ids(__UpperCamelCase )
					self.assertListEqual(
					    __UpperCamelCase	,  [
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]	,  )

					_UpperCAmelCase		:       str								=      tokenizer.convert_ids_to_tokens(__UpperCamelCase )
					self.assertListEqual(
					    __UpperCamelCase	,  [
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "<unk>",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "<unk>",
					        ".",
					    ]	,  )




			def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



					'''simple docstring'''







					if not self.test_slow_tokenizer:
							# as we don't have a slow version, we can't compare the outputs between slow and fast versions
							return

					_UpperCAmelCase		:       int								=      (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
									_UpperCAmelCase		:       List[Any]								=      self.rust_tokenizer_class.from_pretrained(__UpperCamelCase	,  **__UpperCamelCase )
									_UpperCAmelCase		:       str								=      self.tokenizer_class.from_pretrained(__UpperCamelCase	,  **__UpperCamelCase )

									_UpperCAmelCase		:       List[Any]								=      tempfile.mkdtemp()

									_UpperCAmelCase		:       Tuple								=      tokenizer_r.save_pretrained(__UpperCamelCase )
									_UpperCAmelCase		:       Tuple								=      tokenizer_p.save_pretrained(__UpperCamelCase )

									# Checks it save with the same files + the tokenizer.json file for the fast one
									self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
									_UpperCAmelCase		:       List[Any]								=      tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
									self.assertSequenceEqual(__UpperCamelCase	,  __UpperCamelCase )

									# Checks everything loads correctly in the same way
									_UpperCAmelCase		:       List[str]								=      tokenizer_r.from_pretrained(__UpperCamelCase )
									_UpperCAmelCase		:       List[str]								=      tokenizer_p.from_pretrained(__UpperCamelCase )

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(__UpperCamelCase	,  __UpperCamelCase ) )
											# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
											# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

									shutil.rmtree(__UpperCamelCase )

									# Save tokenizer rust, legacy_format=True
									_UpperCAmelCase		:       str								=      tempfile.mkdtemp()

									_UpperCAmelCase		:       int								=      tokenizer_r.save_pretrained(__UpperCamelCase	,  legacy_format=__UpperCamelCase )
									_UpperCAmelCase		:       List[str]								=      tokenizer_p.save_pretrained(__UpperCamelCase )

									# Checks it save with the same files
									self.assertSequenceEqual(__UpperCamelCase	,  __UpperCamelCase )

									# Checks everything loads correctly in the same way
									_UpperCAmelCase		:       List[Any]								=      tokenizer_r.from_pretrained(__UpperCamelCase )
									_UpperCAmelCase		:       Any								=      tokenizer_p.from_pretrained(__UpperCamelCase )

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(__UpperCamelCase	,  __UpperCamelCase ) )

									shutil.rmtree(__UpperCamelCase )

									# Save tokenizer rust, legacy_format=False
									_UpperCAmelCase		:       List[str]								=      tempfile.mkdtemp()

									_UpperCAmelCase		:       Union[str, Any]								=      tokenizer_r.save_pretrained(__UpperCamelCase	,  legacy_format=__UpperCamelCase )
									_UpperCAmelCase		:       Tuple								=      tokenizer_p.save_pretrained(__UpperCamelCase )

									# Checks it saved the tokenizer.json file
									self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )

									# Checks everything loads correctly in the same way
									_UpperCAmelCase		:       List[str]								=      tokenizer_r.from_pretrained(__UpperCamelCase )
									_UpperCAmelCase		:       str								=      tokenizer_p.from_pretrained(__UpperCamelCase )

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(__UpperCamelCase	,  __UpperCamelCase ) )

									shutil.rmtree(__UpperCamelCase )




			@cached_property
			def 							lowerCAmelCase__    (		self  :     List[str] )   ->int:



					'''simple docstring'''







					return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base" )




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Tuple:



					'''simple docstring'''







					with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(__UpperCamelCase	,  f.name )
							_UpperCAmelCase		:       Any								=      XLMRobertaTokenizer(f.name	,  keep_accents=__UpperCamelCase )
							_UpperCAmelCase		:       Tuple								=      pickle.dumps(__UpperCamelCase )
					pickle.loads(__UpperCamelCase )




			def 							lowerCAmelCase__    (		self  :     int )   ->Union[str, Any]:



					'''simple docstring'''







					if not self.test_rust_tokenizer:
							return

					_UpperCAmelCase		:       Union[str, Any]								=      self.get_tokenizer()
					_UpperCAmelCase		:       Tuple								=      self.get_rust_tokenizer()

					_UpperCAmelCase		:       List[Any]								=      "I was born in 92000, and this is falsé."

					_UpperCAmelCase		:       List[Any]								=      tokenizer.tokenize(__UpperCamelCase )
					_UpperCAmelCase		:       Union[str, Any]								=      rust_tokenizer.tokenize(__UpperCamelCase )
					self.assertListEqual(__UpperCamelCase	,  __UpperCamelCase )

					_UpperCAmelCase		:       Any								=      tokenizer.encode(__UpperCamelCase	,  add_special_tokens=__UpperCamelCase )
					_UpperCAmelCase		:       int								=      rust_tokenizer.encode(__UpperCamelCase	,  add_special_tokens=__UpperCamelCase )
					self.assertListEqual(__UpperCamelCase	,  __UpperCamelCase )

					_UpperCAmelCase		:       int								=      self.get_rust_tokenizer()
					_UpperCAmelCase		:       Dict								=      tokenizer.encode(__UpperCamelCase )
					_UpperCAmelCase		:       Any								=      rust_tokenizer.encode(__UpperCamelCase )
					self.assertListEqual(__UpperCamelCase	,  __UpperCamelCase )




			@slow
			def 							lowerCAmelCase__    (		self  :     Dict )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      "Hello World!"
					_UpperCAmelCase		:       Tuple								=      [0, 3_53_78, 66_61, 38, 2]
					# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
					# xlmr.eval()
					# xlmr.encode(symbols)

					self.assertListEqual(__UpperCamelCase	,  self.big_tokenizer.encode(__UpperCamelCase ) )




			@slow
			def 							lowerCAmelCase__    (		self  :     List[str] )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      (
					    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
					    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
					)
					_UpperCAmelCase		:       Any								=      [
					    0,
					    32_93,
					    83,
					    10,
					    45_52,
					    49_89,
					    79_86,
					    6_78,
					    10,
					    59_15,
					    1_11,
					    17_94_59,
					    12_48_50,
					    4,
					    60_44,
					    2_37,
					    12,
					    6,
					    5,
					    6,
					    4,
					    67_80,
					    7_05,
					    15,
					    13_88,
					    44,
					    3_78,
					    1_01_14,
					    7_11,
					    1_52,
					    20,
					    6,
					    5,
					    2_23_76,
					    6_42,
					    12_21,
					    1_51_90,
					    3_41_53,
					    4_50,
					    56_08,
					    9_59,
					    11_19,
					    5_77_02,
					    1_36,
					    1_86,
					    47,
					    10_98,
					    2_93_67,
					    47,
					    # 4426, # What fairseq tokenizes from "<unk>": "_<"
					    # 3678, # What fairseq tokenizes from "<unk>": "unk"
					    # 2740, # What fairseq tokenizes from "<unk>": ">"
					    3,  # What we tokenize from "<unk>": "<unk>"
					    6,  # Residue from the tokenization: an extra sentencepiece underline
					    4,
					    60_44,
					    2_37,
					    62_84,
					    5_09_01,
					    5_28,
					    31,
					    90,
					    34,
					    9_27,
					    2,
					]
					# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
					# xlmr.eval()
					# xlmr.encode(symbols)

					self.assertListEqual(__UpperCamelCase	,  self.big_tokenizer.encode(__UpperCamelCase ) )






			@slow
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=__UpperCamelCase	,  model_name="xlm-roberta-base"	,  revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"	,  )







'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor






class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):
			lowerCAmelCase :	int          =     "pixel_values"
			lowerCAmelCase :	Dict          =     False
			lowerCAmelCase :	Union[str, Any]          =     TimmBackboneConfig




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					requires_backends(self	,  "timm" )
					super().__init__(lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      config

					if config.backbone is None:
							raise ValueError("backbone is not set in the config. Please set it to a timm model name." )

					if config.backbone not in timm.list_models():
							raise ValueError(F"""backbone {config.backbone} is not supported by timm.""" )

					if hasattr(lowerCamelCase__	,  "out_features" ) and config.out_features is not None:
							raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )

					_UpperCAmelCase		:       Optional[Any]								=      getattr(lowerCamelCase__	,  "use_pretrained_backbone"	,  lowerCamelCase__ )
					if pretrained is None:
							raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )

					# We just take the final layer by default. This matches the default for the transformers models.
					_UpperCAmelCase		:       int								=      config.out_indices if getattr(lowerCamelCase__	,  "out_indices"	,  lowerCamelCase__ ) is not None else (-1,)

					_UpperCAmelCase		:       List[Any]								=      timm.create_model(
					    config.backbone	,  pretrained=lowerCamelCase__	,  features_only=config.features_only	,  in_chans=config.num_channels	,  out_indices=lowerCamelCase__	,  **lowerCamelCase__	,  )
					# These are used to control the output of the model when called. If output_hidden_states is True, then
					# return_layers is modified to include all layers.
					_UpperCAmelCase		:       List[str]								=      self._backbone.return_layers
					_UpperCAmelCase		:       Optional[int]								=      {layer["module"]: str(lowerCamelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
					super()._init_backbone(lowerCamelCase__ )




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     List[str]	,  lowerCamelCase__  :     str	,  *lowerCamelCase__  :     Tuple	,  **lowerCamelCase__  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					requires_backends(cls	,  ["vision", "timm"] )
					from ...models.timm_backbone import TimmBackboneConfig

					_UpperCAmelCase		:       Any								=      kwargs.pop("config"	,  TimmBackboneConfig() )

					_UpperCAmelCase		:       Dict								=      kwargs.pop("use_timm_backbone"	,  lowerCamelCase__ )
					if not use_timm:
							raise ValueError("use_timm_backbone must be True for timm backbones" )

					_UpperCAmelCase		:       str								=      kwargs.pop("num_channels"	,  config.num_channels )
					_UpperCAmelCase		:       Dict								=      kwargs.pop("features_only"	,  config.features_only )
					_UpperCAmelCase		:       str								=      kwargs.pop("use_pretrained_backbone"	,  config.use_pretrained_backbone )
					_UpperCAmelCase		:       Optional[Any]								=      kwargs.pop("out_indices"	,  config.out_indices )
					_UpperCAmelCase		:       Dict								=      TimmBackboneConfig(
					    backbone=lowerCamelCase__	,  num_channels=lowerCamelCase__	,  features_only=lowerCamelCase__	,  use_pretrained_backbone=lowerCamelCase__	,  out_indices=lowerCamelCase__	,  )
					return super()._from_config(lowerCamelCase__	,  **lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







					pass






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     List[Any]=None	,  lowerCamelCase__  :     Union[str, Any]=None	,  **lowerCamelCase__  :     Dict )   ->Union[BackboneOutput, Tuple[Tensor, ...]]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      return_dict if return_dict is not None else self.config.use_return_dict
					_UpperCAmelCase		:       Optional[int]								=      (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					_UpperCAmelCase		:       Dict								=      output_attentions if output_attentions is not None else self.config.output_attentions

					if output_attentions:
							raise ValueError("Cannot output attentions for timm backbones at the moment" )

					if output_hidden_states:
							# We modify the return layers to include all the stages of the backbone
							_UpperCAmelCase		:       Optional[int]								=      self._all_layers
							_UpperCAmelCase		:       List[str]								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      self._return_layers
							_UpperCAmelCase		:       Tuple								=      tuple(hidden_states[i] for i in self.out_indices )
					else:
							_UpperCAmelCase		:       Any								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      None

					_UpperCAmelCase		:       Dict								=      tuple(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      tuple(lowerCamelCase__ ) if hidden_states is not None else None

					if not return_dict:
							_UpperCAmelCase		:       Dict								=      (feature_maps,)
							if output_hidden_states:
									_UpperCAmelCase		:       List[str]								=      output + (hidden_states,)
							return output

					return BackboneOutput(feature_maps=lowerCamelCase__	,  hidden_states=lowerCamelCase__	,  attentions=lowerCamelCase__ )



