

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE		:Any						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """microsoft/swin-tiny-patch4-window7-224""": (
        """https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"""
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 						__magic_name__					(							snake_case			,      snake_case				):
     UpperCamelCase_	:Union[str, Any]										=      """swin"""

     UpperCamelCase_	:Optional[int]										=      {
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
     }






     def __init__(				self		,						_lowercase=224		,						_lowercase=4		,						_lowercase=3		,						_lowercase=96		,						_lowercase=[2, 2, 6, 2]		,						_lowercase=[3, 6, 12, 24]		,						_lowercase=7		,						_lowercase=4.0		,						_lowercase=True		,						_lowercase=0.0		,						_lowercase=0.0		,						_lowercase=0.1		,						_lowercase="gelu"		,						_lowercase=False		,						_lowercase=0.02		,						_lowercase=1e-5		,						_lowercase=32		,						_lowercase=None		,						_lowercase=None		,						**_lowercase		,						)->     Optional[Any]:
         super().__init__(**_lowercase	)

         UpperCamelCase_			  =						image_size
         UpperCamelCase_			  =						patch_size
         UpperCamelCase_			  =						num_channels
         UpperCamelCase_			  =						embed_dim
         UpperCamelCase_			  =						depths
         UpperCamelCase_			  =						len(_lowercase	)
         UpperCamelCase_			  =						num_heads
         UpperCamelCase_			  =						window_size
         UpperCamelCase_			  =						mlp_ratio
         UpperCamelCase_			  =						qkv_bias
         UpperCamelCase_			  =						hidden_dropout_prob
         UpperCamelCase_			  =						attention_probs_dropout_prob
         UpperCamelCase_			  =						drop_path_rate
         UpperCamelCase_			  =						hidden_act
         UpperCamelCase_			  =						use_absolute_embeddings
         UpperCamelCase_			  =						layer_norm_eps
         UpperCamelCase_			  =						initializer_range
         UpperCamelCase_			  =						encoder_stride
         # 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
         UpperCamelCase_			  =						int(embed_dim * 2 ** (len(_lowercase	) - 1)	)
         UpperCamelCase_			  =						["stem"] + [F"stage{idx}" for idx in range(1		,						len(_lowercase	) + 1	)]
         UpperCamelCase_  ,       UpperCamelCase_			  =						get_aligned_output_features_output_indices(
             out_features=_lowercase		,						out_indices=_lowercase		,						stage_names=self.stage_names	)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Any										=      version.parse("""1.11"""				)






     @property
     def 	UpperCAmelCase_				(				self	)->     Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]	)






     @property
     def 	UpperCAmelCase_				(				self	)->     float:
         return 1e-4



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:List[Any]										=      LayoutLMTokenizer
     UpperCamelCase_	:Dict										=      LayoutLMTokenizerFast
     UpperCamelCase_	:List[str]										=      True
     UpperCamelCase_	:Dict										=      True






     def 	UpperCAmelCase_				(				self	)->     str:
         super().setUp()

         UpperCamelCase_			  =						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     List[str]:
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						"UNwant\u00E9d,running"
         UpperCamelCase_			  =						"unwanted, running"
         return input_text, output_text






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file	)

         UpperCamelCase_			  =						tokenizer.tokenize("UNwant\u00E9d,running"	)
         self.assertListEqual(_lowercase		,						["un", "##want", "##ed", ",", "runn", "##ing"]	)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase	)		,						[7, 4, 5, 10, 8, 9]	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         pass
