



"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__a      =				logging.get_logger(__name__)


__a      =				{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all LED models at https://huggingface.co/models?filter=LED
__a      =				{
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

__a      =				{
    "allenai/led-base-16384": 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Tuple						=			(
						    list(range(ord("""!"""	),					ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	),					ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	),					ord("""ÿ"""	) + 1	)	)
						)
						snake_case_ :List[Any]						=			bs[:]
						snake_case_ :int						=			0
						for b in range(2**8	):
												if b not in bs:
																		bs.append(_lowercase	)
																		cs.append(2**8 + n	)
																		n += 1
						snake_case_ :List[Any]						=			[chr(_lowercase	) for n in cs]
						return dict(zip(_lowercase,					_lowercase	)	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			set()
						snake_case_ :int						=			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)	)
												snake_case_ :Optional[int]						=			char
						return pairs

class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			Dict						=     VOCAB_FILES_NAMES
					_A		:			Tuple						=     PRETRAINED_VOCAB_FILES_MAP
					_A		:			int						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_A		:			List[str]						=     ["""input_ids""", """attention_mask"""]






					def __init__(       self:							List[str]						,     snake_case:							Tuple						,     snake_case:							Optional[int]						,     snake_case:							Optional[Any]="replace"						,     snake_case:							Optional[Any]="<s>"						,     snake_case:							Any="</s>"						,     snake_case:							int="</s>"						,     snake_case:							Optional[int]="<s>"						,     snake_case:							int="<unk>"						,     snake_case:							Any="<pad>"						,     snake_case:							str="<mask>"						,     snake_case:							Optional[int]=False						,     **snake_case:							str						,     )   ->	List[Any]:
											snake_case_ :int						=			AddedToken(snake_case						,     lstrip=snake_case						,     rstrip=snake_case						) if isinstance(snake_case						,     snake_case						) else bos_token
											snake_case_ :Union[str, Any]						=			AddedToken(snake_case						,     lstrip=snake_case						,     rstrip=snake_case						) if isinstance(snake_case						,     snake_case						) else eos_token
											snake_case_ :Union[str, Any]						=			AddedToken(snake_case						,     lstrip=snake_case						,     rstrip=snake_case						) if isinstance(snake_case						,     snake_case						) else sep_token
											snake_case_ :int						=			AddedToken(snake_case						,     lstrip=snake_case						,     rstrip=snake_case						) if isinstance(snake_case						,     snake_case						) else cls_token
											snake_case_ :List[str]						=			AddedToken(snake_case						,     lstrip=snake_case						,     rstrip=snake_case						) if isinstance(snake_case						,     snake_case						) else unk_token
											snake_case_ :List[str]						=			AddedToken(snake_case						,     lstrip=snake_case						,     rstrip=snake_case						) if isinstance(snake_case						,     snake_case						) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_ :str						=			AddedToken(snake_case						,     lstrip=snake_case						,     rstrip=snake_case						) if isinstance(snake_case						,     snake_case						) else mask_token

											super().__init__(
											    errors=snake_case						,     bos_token=snake_case						,     eos_token=snake_case						,     unk_token=snake_case						,     sep_token=snake_case						,     cls_token=snake_case						,     pad_token=snake_case						,     mask_token=snake_case						,     add_prefix_space=snake_case						,     **snake_case						,     )

											with open(snake_case						,     encoding="""utf-8"""						) as vocab_handle:
																	snake_case_ :int						=			json.load(snake_case						)
											snake_case_ :str						=			{v: k for k, v in self.encoder.items()}
											snake_case_ :Dict						=			errors  # how to handle errors in decoding
											snake_case_ :Any						=			bytes_to_unicode()
											snake_case_ :List[str]						=			{v: k for k, v in self.byte_encoder.items()}
											with open(snake_case						,     encoding="""utf-8"""						) as merges_handle:
																	snake_case_ :List[str]						=			merges_handle.read().split("""\n"""						)[1:-1]
											snake_case_ :str						=			[tuple(merge.split()						) for merge in bpe_merges]
											snake_case_ :Optional[int]						=			dict(zip(snake_case						,     range(len(snake_case						)						)						)						)
											snake_case_ :int						=			{}
											snake_case_ :Union[str, Any]						=			add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											snake_case_ :Union[str, Any]						=			re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""						)






					@property
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											return len(self.encoder						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	int:
											return dict(self.encoder						,     **self.added_tokens_encoder						)






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							Optional[Any]						)   ->	Union[str, Any]:
											if token in self.cache:
																	return self.cache[token]
											snake_case_ :Optional[Any]						=			tuple(snake_case						)
											snake_case_ :Any						=			get_pairs(snake_case						)

											if not pairs:
																	return token

											while True:
																	snake_case_ :Union[str, Any]						=			min(snake_case						,     key=lambda snake_case						: self.bpe_ranks.get(snake_case						,     float("""inf"""						)						)						)
																	if bigram not in self.bpe_ranks:
																							break
																	snake_case_,       snake_case_ :int						=			bigram
																	snake_case_ :int						=			[]
																	snake_case_ :str						=			0
																	while i < len(snake_case						):
																							try:
																													snake_case_ :Any						=			word.index(snake_case						,     snake_case						)
																							except ValueError:
																													new_word.extend(word[i:]						)
																													break
																							else:
																													new_word.extend(word[i:j]						)
																													snake_case_ :Any						=			j

																							if word[i] == first and i < len(snake_case						) - 1 and word[i + 1] == second:
																													new_word.append(first + second						)
																													i += 2
																							else:
																													new_word.append(word[i]						)
																													i += 1
																	snake_case_ :Dict						=			tuple(snake_case						)
																	snake_case_ :Optional[Any]						=			new_word
																	if len(snake_case						) == 1:
																							break
																	else:
																							snake_case_ :Optional[int]						=			get_pairs(snake_case						)
											snake_case_ :Optional[int]						=			""" """.join(snake_case						)
											snake_case_ :Optional[int]						=			word
											return word






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							List[str]						)   ->	str:
											snake_case_ :List[str]						=			[]
											for token in re.findall(self.pat						,     snake_case						):
																	snake_case_ :str						=			"""""".join(
																	    self.byte_encoder[b] for b in token.encode("""utf-8"""						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																	bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case						).split(""" """						)						)
											return bpe_tokens






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Tuple						)   ->	Optional[int]:
											return self.encoder.get(snake_case						,     self.encoder.get(self.unk_token						)						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							List[str]						)   ->	Tuple:
											return self.decoder.get(snake_case						)






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Dict						)   ->	str:
											snake_case_ :Optional[Any]						=			"""""".join(snake_case						)
											snake_case_ :Dict						=			bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""						,     errors=self.errors						)
											return text






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							str						,     snake_case:							Optional[str] = None						)   ->	Tuple[str]:
											if not os.path.isdir(snake_case						):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
																	return
											snake_case_ :Dict						=			os.path.join(
											    snake_case						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
											snake_case_ :Dict						=			os.path.join(
											    snake_case						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

											with open(snake_case						,     """w"""						,     encoding="""utf-8"""						) as f:
																	f.write(json.dumps(self.encoder						,     indent=2						,     sort_keys=snake_case						,     ensure_ascii=snake_case						) + """\n"""						)

											snake_case_ :Optional[Any]						=			0
											with open(snake_case						,     """w"""						,     encoding="""utf-8"""						) as writer:
																	writer.write("""#version: 0.2\n"""						)
																	for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,     key=lambda snake_case						: kv[1]						):
																							if index != token_index:
																													logger.warning(
																													    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																													    """ Please check that the tokenizer is not corrupted!"""						)
																													snake_case_ :Optional[int]						=			token_index
																							writer.write(""" """.join(snake_case						) + """\n"""						)
																							index += 1

											return vocab_file, merge_file






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							List[int]						,     snake_case:							Optional[List[int]] = None						)   ->	List[int]:
											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											snake_case_ :List[str]						=			[self.cls_token_id]
											snake_case_ :List[str]						=			[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							List[int]						,     snake_case:							Optional[List[int]] = None						,     snake_case:							bool = False						)   ->	List[int]:
											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=snake_case						,     token_ids_a=snake_case						,     already_has_special_tokens=snake_case						)

											if token_ids_a is None:
																	return [1] + ([0] * len(snake_case						)) + [1]
											return [1] + ([0] * len(snake_case						)) + [1, 1] + ([0] * len(snake_case						)) + [1]






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							List[int]						,     snake_case:							Optional[List[int]] = None						)   ->	List[int]:
											snake_case_ :Optional[Any]						=			[self.sep_token_id]
											snake_case_ :Dict						=			[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep						) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Tuple						,     snake_case:							Optional[int]=False						,     **snake_case:							List[Any]						)   ->	Dict:
											snake_case_ :List[Any]						=			kwargs.pop("""add_prefix_space"""						,     self.add_prefix_space						)
											if (is_split_into_words or add_prefix_space) and (len(snake_case						) > 0 and not text[0].isspace()):
																	snake_case_ :Any						=			""" """ + text
											return (text, kwargs)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Union[Dict[str, EncodedInput], BatchEncoding]						,     snake_case:							Optional[int] = None						,     snake_case:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[bool] = None						,     )   ->	dict:
											snake_case_ :Any						=			super()._pad(
											    encoded_inputs=snake_case						,     max_length=snake_case						,     padding_strategy=snake_case						,     pad_to_multiple_of=snake_case						,     return_attention_mask=snake_case						,     )

											# Load from model defaults
											if return_attention_mask is None:
																	snake_case_ :List[str]						=			"""attention_mask""" in self.model_input_names

											if return_attention_mask and "global_attention_mask" in encoded_inputs:
																	snake_case_ :str						=			encoded_inputs[self.model_input_names[0]]
																	# `global_attention_mask` need to have the same length as other (sequential) inputs.
																	snake_case_ :Tuple						=			len(encoded_inputs["""global_attention_mask"""]						) != len(snake_case						)

																	if needs_to_be_padded:
																							snake_case_ :Tuple						=			len(snake_case						) - len(encoded_inputs["""global_attention_mask"""]						)

																							if self.padding_side == "right":
																													# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																													snake_case_ :Union[str, Any]						=			(
																													    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																													)
																							elif self.padding_side == "left":
																													snake_case_ :Optional[int]						=			[-1] * difference + encoded_inputs[
																													    """global_attention_mask"""
																													]
																							else:
																													raise ValueError("""Invalid padding strategy:""" + str(self.padding_side						)						)

											return encoded_inputs





"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
						from transformers.models.maskformer import MaskFormerSwinModel
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Dict						,     snake_case:							Optional[Any]						,     snake_case:							Tuple=13						,     snake_case:							Any=32						,     snake_case:							Union[str, Any]=2						,     snake_case:							Tuple=3						,     snake_case:							Union[str, Any]=16						,     snake_case:							Union[str, Any]=[1, 2, 1]						,     snake_case:							Optional[Any]=[2, 2, 4]						,     snake_case:							str=2						,     snake_case:							List[str]=2.0						,     snake_case:							Optional[int]=True						,     snake_case:							Union[str, Any]=0.0						,     snake_case:							Optional[int]=0.0						,     snake_case:							Optional[Any]=0.1						,     snake_case:							List[str]="gelu"						,     snake_case:							Any=False						,     snake_case:							Optional[Any]=True						,     snake_case:							Optional[int]=0.0_2						,     snake_case:							Any=1E-5						,     snake_case:							Optional[int]=True						,     snake_case:							int=None						,     snake_case:							Any=True						,     snake_case:							str=10						,     snake_case:							Optional[Any]=8						,     snake_case:							Union[str, Any]=["stage1", "stage2", "stage3"]						,     snake_case:							Tuple=[1, 2, 3]						,     )   ->	Dict:
											snake_case_ :Dict						=			parent
											snake_case_ :List[Any]						=			batch_size
											snake_case_ :Dict						=			image_size
											snake_case_ :Dict						=			patch_size
											snake_case_ :Tuple						=			num_channels
											snake_case_ :List[Any]						=			embed_dim
											snake_case_ :List[str]						=			depths
											snake_case_ :str						=			num_heads
											snake_case_ :Tuple						=			window_size
											snake_case_ :Tuple						=			mlp_ratio
											snake_case_ :int						=			qkv_bias
											snake_case_ :Tuple						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :Dict						=			drop_path_rate
											snake_case_ :Any						=			hidden_act
											snake_case_ :Any						=			use_absolute_embeddings
											snake_case_ :int						=			patch_norm
											snake_case_ :List[Any]						=			layer_norm_eps
											snake_case_ :Tuple						=			initializer_range
											snake_case_ :str						=			is_training
											snake_case_ :int						=			scope
											snake_case_ :Tuple						=			use_labels
											snake_case_ :Tuple						=			type_sequence_label_size
											snake_case_ :str						=			encoder_stride
											snake_case_ :List[Any]						=			out_features
											snake_case_ :str						=			out_indices






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_ :Optional[int]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case_ :str						=			None
											if self.use_labels:
																	snake_case_ :Optional[int]						=			ids_tensor([self.batch_size]						,     self.type_sequence_label_size						)

											snake_case_ :Union[str, Any]						=			self.get_config()

											return config, pixel_values, labels






					def    lowerCAmelCase_				(       self:							int						)   ->	Optional[Any]:
											return MaskFormerSwinConfig(
											    image_size=self.image_size						,     patch_size=self.patch_size						,     num_channels=self.num_channels						,     embed_dim=self.embed_dim						,     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:							List[Any]						,     snake_case:							str						,     snake_case:							int						,     snake_case:							List[str]						)   ->	Any:
											snake_case_ :Dict						=			MaskFormerSwinModel(config=snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Tuple						=			model(snake_case						)

											snake_case_ :Dict						=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
											snake_case_ :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:							Optional[Any]						,     snake_case:							int						,     snake_case:							List[str]						,     snake_case:							Tuple						)   ->	Union[str, Any]:
											snake_case_ :Any						=			MaskFormerSwinBackbone(config=snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Optional[Any]						=			model(snake_case						)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps						)						,     len(config.out_features						)						)
											self.parent.assertListEqual(list(result.feature_maps[0].shape						)						,     [13, 16, 16, 16]						)

											# verify channels
											self.parent.assertEqual(len(model.channels						)						,     len(config.out_features						)						)
											self.parent.assertListEqual(model.channels						,     [16, 32, 64]						)

											# verify ValueError
											with self.parent.assertRaises(snake_case						):
																	snake_case_ :Optional[Any]						=			["""stem"""]
																	snake_case_ :str						=			MaskFormerSwinBackbone(config=snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Optional[Any]:
											snake_case_ :Optional[int]						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_ :str						=			config_and_inputs
											snake_case_ :Tuple						=			{"""pixel_values""": pixel_values}
											return config, inputs_dict

@require_torch
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     (
					    (
					        MaskFormerSwinModel,
					        MaskFormerSwinBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					_A		:			str						=     {"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
					_A		:			List[str]						=     False
					_A		:			Any						=     False
					_A		:			Dict						=     False
					_A		:			List[Any]						=     False
					_A		:			Optional[int]						=     False






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Any:
											snake_case_ :str						=			MaskFormerSwinModelTester(self						)
											snake_case_ :Optional[Any]						=			ConfigTester(self						,     config_class=snake_case						,     embed_dim=37						)






					@require_torch_multi_gpu
					@unittest.skip(
					    reason=(
					        """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
					        """ `nn.DataParallel`"""
					    )						)
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Optional[int]:
											pass






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Dict:
											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:							Any						)   ->	Tuple:
											return






					def    lowerCAmelCase_				(       self:							Any						)   ->	Any:
											snake_case_ :List[Any]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case						)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	int:
											snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*snake_case						)






					@unittest.skip("""Swin does not use inputs_embeds"""						)
					def    lowerCAmelCase_				(       self:							str						)   ->	List[str]:
											pass






					@unittest.skip("""Swin does not support feedforward chunking"""						)
					def    lowerCAmelCase_				(       self:							int						)   ->	Optional[int]:
											pass






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[Any]:
											snake_case_,       snake_case_ :List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_ :str						=			model_class(snake_case						)
																	self.assertIsInstance(model.get_input_embeddings()						,     (nn.Module)						)
																	snake_case_ :Dict						=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case						,     nn.Linear						)						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_,       snake_case_ :int						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	snake_case_ :str						=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]						,     snake_case						)






					@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""						)
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	List[Any]:
											pass






					@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""						)
					def    lowerCAmelCase_				(       self:							Dict						)   ->	List[Any]:
											pass






					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							Union[str, Any]						,     snake_case:							int						,     snake_case:							Any						,     snake_case:							List[str]						)   ->	str:
											snake_case_ :List[str]						=			model_class(snake_case						)
											model.to(snake_case						)
											model.eval()

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

											snake_case_ :Any						=			outputs.hidden_states

											snake_case_ :Optional[int]						=			getattr(
											    self.model_tester						,     """expected_num_hidden_layers"""						,     len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(snake_case						)						,     snake_case						)

											# Swin has a different seq_length
											snake_case_ :str						=			(
											    config.patch_size
											    if isinstance(config.patch_size						,     collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											snake_case_ :int						=			(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]						,     )






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Optional[int]:
											snake_case_,       snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs_for_common()

											snake_case_ :List[Any]						=			(
											    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:
																	snake_case_ :Tuple						=			True
																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     snake_case						)

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

																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Tuple:
											snake_case_,       snake_case_ :int						=			self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_ :List[Any]						=			3

											snake_case_ :List[Any]						=			(
											    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)
											)
											snake_case_ :Any						=			(
											    config.patch_size
											    if isinstance(config.patch_size						,     collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											snake_case_ :Tuple						=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											snake_case_ :List[str]						=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
																	snake_case_ :str						=			True
																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     (padded_height, padded_width)						)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	snake_case_ :Any						=			True
																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     (padded_height, padded_width)						)






					@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""						)
					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[str]:
											pass






					@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""						)
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	str:
											pass






					@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""						)
					def    lowerCAmelCase_				(       self:							str						)   ->	List[Any]:
											pass






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :Dict						=			self.model_tester.prepare_config_and_inputs_for_common()

											def set_nan_tensor_to_zero(snake_case:							str						):
																	snake_case_ :Optional[int]						=			0
																	return t

											def check_equivalence(snake_case:							List[Any]						,     snake_case:							Union[str, Any]						,     snake_case:							int						,     snake_case:							Tuple={}						):
																	with torch.no_grad():
																							snake_case_ :List[Any]						=			model(**snake_case						,     return_dict=snake_case						,     **snake_case						)
																							snake_case_ :Any						=			model(**snake_case						,     return_dict=snake_case						,     **snake_case						).to_tuple()

																							def recursive_check(snake_case:							List[Any]						,     snake_case:							int						):
																													if isinstance(snake_case						,     (List, Tuple)						):
																																			for tuple_iterable_value, dict_iterable_value in zip(snake_case						,     snake_case						):
																																									recursive_check(snake_case						,     snake_case						)
																													elif isinstance(snake_case						,     snake_case						):
																																			for tuple_iterable_value, dict_iterable_value in zip(
																																			    tuple_object.values()						,     dict_object.values()						):
																																									recursive_check(snake_case						,     snake_case						)
																													elif tuple_object is None:
																																			return
																													else:
																																			self.assertTrue(
																																			    torch.allclose(
																																			        set_nan_tensor_to_zero(snake_case						)						,     set_nan_tensor_to_zero(snake_case						)						,     atol=1E-5						)						,     msg=(
																																			        """Tuple and dict output are not equal. Difference:"""
																																			        f""" {torch.max(torch.abs(tuple_object - dict_object						)						)}. Tuple has `nan`:"""
																																			        f""" {torch.isnan(snake_case						).any()} and `inf`: {torch.isinf(snake_case						)}. Dict has"""
																																			        f""" `nan`: {torch.isnan(snake_case						).any()} and `inf`: {torch.isinf(snake_case						)}."""
																																			    )						,     )

																							recursive_check(snake_case						,     snake_case						)

											for model_class in self.all_model_classes:
																	snake_case_ :int						=			model_class(snake_case						)
																	model.to(snake_case						)
																	model.eval()

																	snake_case_ :Any						=			self._prepare_for_class(snake_case						,     snake_case						)
																	snake_case_ :List[Any]						=			self._prepare_for_class(snake_case						,     snake_case						)
																	check_equivalence(snake_case						,     snake_case						,     snake_case						)

																	snake_case_ :Tuple						=			self._prepare_for_class(snake_case						,     snake_case						,     return_labels=snake_case						)
																	snake_case_ :Dict						=			self._prepare_for_class(snake_case						,     snake_case						,     return_labels=snake_case						)
																	check_equivalence(snake_case						,     snake_case						,     snake_case						)

																	snake_case_ :Tuple						=			self._prepare_for_class(snake_case						,     snake_case						)
																	snake_case_ :Any						=			self._prepare_for_class(snake_case						,     snake_case						)
																	check_equivalence(snake_case						,     snake_case						,     snake_case						,     {"""output_hidden_states""": True}						)

																	snake_case_ :Dict						=			self._prepare_for_class(snake_case						,     snake_case						,     return_labels=snake_case						)
																	snake_case_ :List[str]						=			self._prepare_for_class(snake_case						,     snake_case						,     return_labels=snake_case						)
																	check_equivalence(snake_case						,     snake_case						,     snake_case						,     {"""output_hidden_states""": True}						)

@require_torch
class 				lowerCamelCase  (			unittest.TestCase    ,						_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			int						=     (MaskFormerSwinBackbone,) if is_torch_available() else ()
					_A		:			Tuple						=     MaskFormerSwinConfig






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Optional[int]:
											snake_case_ :Optional[Any]						=			MaskFormerSwinModelTester(self						)






					def    lowerCAmelCase_				(       self:							int						)   ->	Optional[int]:
											snake_case_,       snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_ :Tuple						=			inputs_dict["""pixel_values"""].shape[0]

											for backbone_class in self.all_model_classes:
																	snake_case_ :List[str]						=			backbone_class(snake_case						)
																	backbone.to(snake_case						)
																	backbone.eval()

																	snake_case_ :List[Any]						=			backbone(**snake_case						)

																	# Test default outputs and verify feature maps
																	self.assertIsInstance(outputs.feature_maps						,     snake_case						)
																	self.assertTrue(len(outputs.feature_maps						) == len(backbone.channels						)						)
																	for feature_map, n_channels in zip(outputs.feature_maps						,     backbone.channels						):
																							self.assertTrue(feature_map.shape[:2]						,     (batch_size, n_channels)						)
																	self.assertIsNone(outputs.hidden_states						)
																	self.assertIsNone(outputs.attentions						)

																	# Test output_hidden_states=True
																	snake_case_ :Union[str, Any]						=			backbone(**snake_case						,     output_hidden_states=snake_case						)
																	self.assertIsNotNone(outputs.hidden_states						)
																	self.assertTrue(len(outputs.hidden_states						)						,     len(backbone.stage_names						)						)
																	# We skip the stem layer
																	for hidden_states, n_channels in zip(outputs.hidden_states[1:]						,     backbone.channels						):
																							for hidden_state in hidden_states:
																													# Hidden states are in the format (batch_size, (height * width), n_channels)
																													snake_case_,       snake_case_,       snake_case_ :List[Any]						=			hidden_state.shape
																													self.assertTrue((h_batch_size, h_n_channels)						,     (batch_size, n_channels)						)

            # Test output_attentions=True
																	if self.has_attentions:
																							snake_case_ :List[Any]						=			backbone(**snake_case						,     output_attentions=snake_case						)
																							self.assertIsNotNone(outputs.attentions						)
