






import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor

class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Dict					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   int=13					,    __lowerCamelCase							:   List[Any]=32					,    __lowerCamelCase							:   Union[str, Any]=2					,    __lowerCamelCase							:   List[Any]=3					,    __lowerCamelCase							:   int=16					,    __lowerCamelCase							:   List[str]=[1, 2, 1]					,    __lowerCamelCase							:   List[str]=[2, 2, 4]					,    __lowerCamelCase							:   Dict=2					,    __lowerCamelCase							:   Union[str, Any]=2.0					,    __lowerCamelCase							:   Tuple=True					,    __lowerCamelCase							:   List[str]=0.0					,    __lowerCamelCase							:   Union[str, Any]=0.0					,    __lowerCamelCase							:   int=0.1					,    __lowerCamelCase							:   Tuple="gelu"					,    __lowerCamelCase							:   int=False					,    __lowerCamelCase							:   Optional[Any]=True					,    __lowerCamelCase							:   Optional[int]=0.02					,    __lowerCamelCase							:   List[Any]=1E-5					,    __lowerCamelCase							:   int=True					,    __lowerCamelCase							:   Optional[Any]=None					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Dict=10					,    __lowerCamelCase							:   int=8					,    __lowerCamelCase							:   List[Any]=["stage1", "stage2"]					,    __lowerCamelCase							:   Union[str, Any]=[1, 2]					,    ):
		UpperCamelCase	:str		=      parent
		UpperCamelCase	:Tuple		=      batch_size
		UpperCamelCase	:Tuple		=      image_size
		UpperCamelCase	:Dict		=      patch_size
		UpperCamelCase	:Dict		=      num_channels
		UpperCamelCase	:str		=      embed_dim
		UpperCamelCase	:Optional[int]		=      depths
		UpperCamelCase	:str		=      num_heads
		UpperCamelCase	:Any		=      window_size
		UpperCamelCase	:Any		=      mlp_ratio
		UpperCamelCase	:Any		=      qkv_bias
		UpperCamelCase	:str		=      hidden_dropout_prob
		UpperCamelCase	:List[str]		=      attention_probs_dropout_prob
		UpperCamelCase	:Dict		=      drop_path_rate
		UpperCamelCase	:List[Any]		=      hidden_act
		UpperCamelCase	:List[str]		=      use_absolute_embeddings
		UpperCamelCase	:int		=      patch_norm
		UpperCamelCase	:Union[str, Any]		=      layer_norm_eps
		UpperCamelCase	:int		=      initializer_range
		UpperCamelCase	:int		=      is_training
		UpperCamelCase	:Union[str, Any]		=      scope
		UpperCamelCase	:List[Any]		=      use_labels
		UpperCamelCase	:List[str]		=      type_sequence_label_size
		UpperCamelCase	:Optional[int]		=      encoder_stride
		UpperCamelCase	:Dict		=      out_features
		UpperCamelCase	:Optional[Any]		=      out_indices





	def 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:List[Any]		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

		UpperCamelCase	:Tuple		=      None
		if self.use_labels:
			UpperCamelCase	:int		=      ids_tensor([self.batch_size]					,    self.type_sequence_label_size      )

		UpperCamelCase	:Optional[Any]		=      self.get_config()

		return config, pixel_values, labels





	def 		_A					(						self							:   Union[str, Any]      ):
		return SwinConfig(
		    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 		_A					(						self							:   str					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   List[str]					,    __lowerCamelCase							:   Optional[int]      ):
		UpperCamelCase	:str		=      SwinModel(config=__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()
		UpperCamelCase	:int		=      model(__lowerCamelCase      )

		UpperCamelCase	:List[str]		=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
		UpperCamelCase	:Dict		=      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 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Optional[int]      ):
		UpperCamelCase	:Optional[Any]		=      SwinBackbone(config=__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()
		UpperCamelCase	:Dict		=      model(__lowerCamelCase      )

		# verify hidden states
		self.parent.assertEqual(len(result.feature_maps      )					,    len(config.out_features      )      )
		self.parent.assertListEqual(list(result.feature_maps[0].shape      )					,    [self.batch_size, model.channels[0], 16, 16]      )

		# verify channels
		self.parent.assertEqual(len(model.channels      )					,    len(config.out_features      )      )

		# verify backbone works with out_features=None
		UpperCamelCase	:Union[str, Any]		=      None
		UpperCamelCase	:int		=      SwinBackbone(config=__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()
		UpperCamelCase	:int		=      model(__lowerCamelCase      )

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps      )					,    1      )
		self.parent.assertListEqual(list(result.feature_maps[0].shape      )					,    [self.batch_size, model.channels[-1], 4, 4]      )

		# verify channels
		self.parent.assertEqual(len(model.channels      )					,    1      )





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   Any      ):
		UpperCamelCase	:str		=      SwinForMaskedImageModeling(config=__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()
		UpperCamelCase	:List[str]		=      model(__lowerCamelCase      )
		self.parent.assertEqual(
		    result.logits.shape					,    (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

		# test greyscale images
		UpperCamelCase	:Any		=      1
		UpperCamelCase	:Dict		=      SwinForMaskedImageModeling(__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()

		UpperCamelCase	:str		=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
		UpperCamelCase	:str		=      model(__lowerCamelCase      )
		self.parent.assertEqual(result.logits.shape					,    (self.batch_size, 1, self.image_size, self.image_size)      )





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   List[str]      ):
		UpperCamelCase	:List[Any]		=      self.type_sequence_label_size
		UpperCamelCase	:int		=      SwinForImageClassification(__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()
		UpperCamelCase	:List[str]		=      model(__lowerCamelCase					,    labels=__lowerCamelCase      )
		self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.type_sequence_label_size)      )

		# test greyscale images
		UpperCamelCase	:List[Any]		=      1
		UpperCamelCase	:Optional[int]		=      SwinForImageClassification(__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()

		UpperCamelCase	:Union[str, Any]		=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
		UpperCamelCase	:Union[str, Any]		=      model(__lowerCamelCase      )
		self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.type_sequence_label_size)      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Dict		=      self.prepare_config_and_inputs()
		(
		    (
		    UpperCamelCase
		)	, (
		    UpperCamelCase
		)	, (
		    UpperCamelCase
		)	, 
		)	:Optional[Any]		=      config_and_inputs
		UpperCamelCase	:List[Any]		=      {"""pixel_values""": pixel_values}
		return config, inputs_dict

@require_torch
class        _SCREAMING_SNAKE_CASE			(     _a						,  _a						,  unittest.TestCase ):
	snake_case__				:	int	      =    (
	    (
	        SwinModel,
	        SwinBackbone,
	        SwinForImageClassification,
	        SwinForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case__				:	Any	      =    (
	    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
	    if is_torch_available()
	    else {}
	)
	snake_case__				:	Optional[int]	      =    True

	snake_case__				:	int	      =    False
	snake_case__				:	Tuple	      =    False
	snake_case__				:	List[str]	      =    False





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Tuple		=      SwinModelTester(self      )
		UpperCamelCase	:Tuple		=      ConfigTester(self					,    config_class=__lowerCamelCase					,    embed_dim=37      )





	def 		_A					(						self							:   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 		_A					(						self							:   Optional[int]      ):
		return





	def 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:Dict		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowerCamelCase      )





	@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""      )
	def 		_A					(						self							:   Any      ):
		pass





	def 		_A					(						self							:   Optional[Any]      ):
		super().test_training_gradient_checkpointing()





	def 		_A					(						self							:   str      ):
		UpperCamelCase	:Optional[int]		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*__lowerCamelCase      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Union[str, Any]		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase      )





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )





	@unittest.skip(reason="""Swin does not use inputs_embeds"""      )
	def 		_A					(						self							:   Optional[Any]      ):
		pass





	@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""      )
	def 		_A					(						self							:   Tuple      ):
		pass





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	, UpperCamelCase	:Optional[int]		=      self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			UpperCamelCase	:int		=      model_class(__lowerCamelCase      )
			self.assertIsInstance(model.get_input_embeddings()					,    (nn.Module)      )
			UpperCamelCase	:List[Any]		=      model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(__lowerCamelCase					,    nn.Linear      )      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	, UpperCamelCase	:Optional[int]		=      self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			UpperCamelCase	:List[str]		=      model_class(__lowerCamelCase      )
			UpperCamelCase	:List[str]		=      inspect.signature(model.forward      )
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			UpperCamelCase	:Any		=      [*signature.parameters.keys()]

			UpperCamelCase	:Tuple		=      ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,    __lowerCamelCase      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	, UpperCamelCase	:str		=      self.model_tester.prepare_config_and_inputs_for_common()
		UpperCamelCase	:Dict		=      True

		for model_class in self.all_model_classes:
			UpperCamelCase	:List[Any]		=      True
			UpperCamelCase	:Any		=      False
			UpperCamelCase	:int		=      True
			UpperCamelCase	:List[Any]		=      model_class(__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()
			with torch.no_grad():
				UpperCamelCase	:Optional[Any]		=      model(**self._prepare_for_class(__lowerCamelCase					,    __lowerCamelCase      )      )
			UpperCamelCase	:int		=      outputs.attentions
			UpperCamelCase	:Any		=      len(self.model_tester.depths      )
			self.assertEqual(len(__lowerCamelCase      )					,    __lowerCamelCase      )

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			UpperCamelCase	:List[Any]		=      True
			UpperCamelCase	:List[str]		=      config.window_size**2
			UpperCamelCase	:Optional[Any]		=      model_class(__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()
			with torch.no_grad():
				UpperCamelCase	:List[Any]		=      model(**self._prepare_for_class(__lowerCamelCase					,    __lowerCamelCase      )      )
			UpperCamelCase	:Union[str, Any]		=      outputs.attentions
			self.assertEqual(len(__lowerCamelCase      )					,    __lowerCamelCase      )

			self.assertListEqual(
			    list(attentions[0].shape[-3:]      )					,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,    )
			UpperCamelCase	:Dict		=      len(__lowerCamelCase      )

			# Check attention is always last and order is fine
			UpperCamelCase	:Tuple		=      True
			UpperCamelCase	:List[str]		=      True
			UpperCamelCase	:int		=      model_class(__lowerCamelCase      )
			model.to(__lowerCamelCase      )
			model.eval()
			with torch.no_grad():
				UpperCamelCase	:Any		=      model(**self._prepare_for_class(__lowerCamelCase					,    __lowerCamelCase      )      )

			# also another +1 for reshaped_hidden_states
			UpperCamelCase	:Tuple		=      1 if model_class.__name__ == """SwinBackbone""" else 2
			self.assertEqual(out_len + added_hidden_states					,    len(__lowerCamelCase      )      )

			UpperCamelCase	:Optional[int]		=      outputs.attentions

			self.assertEqual(len(__lowerCamelCase      )					,    __lowerCamelCase      )

			self.assertListEqual(
			    list(self_attentions[0].shape[-3:]      )					,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,    )





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   Any					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   Optional[Any]      ):
		UpperCamelCase	:Tuple		=      model_class(__lowerCamelCase      )
		model.to(__lowerCamelCase      )
		model.eval()

		with torch.no_grad():
			UpperCamelCase	:str		=      model(**self._prepare_for_class(__lowerCamelCase					,    __lowerCamelCase      )      )

		UpperCamelCase	:List[str]		=      outputs.hidden_states

		UpperCamelCase	:int		=      getattr(
		    self.model_tester					,    """expected_num_hidden_layers"""					,    len(self.model_tester.depths      ) + 1      )
		self.assertEqual(len(__lowerCamelCase      )					,    __lowerCamelCase      )

		# Swin has a different seq_length
		UpperCamelCase	:Optional[int]		=      (
		    config.patch_size
		    if isinstance(config.patch_size					,    collections.abc.Iterable      )
		    else (config.patch_size, config.patch_size)
		)

		UpperCamelCase	:Union[str, Any]		=      (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]					,    )

		if not model_class.__name__ == "SwinBackbone":
			UpperCamelCase	:int		=      outputs.reshaped_hidden_states
			self.assertEqual(len(__lowerCamelCase      )					,    __lowerCamelCase      )

			UpperCamelCase	, UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:Optional[Any]		=      reshaped_hidden_states[0].shape
			UpperCamelCase	:Tuple		=      (
			    reshaped_hidden_states[0].view(__lowerCamelCase					,    __lowerCamelCase					,    height * width      ).permute(0					,    2					,    1      )
			)
			self.assertListEqual(
			    list(reshaped_hidden_states.shape[-2:]      )					,    [num_patches, self.model_tester.embed_dim]					,    )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	, UpperCamelCase	:List[Any]		=      self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase	: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:
			UpperCamelCase	:Optional[Any]		=      True
			self.check_hidden_states_output(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			UpperCamelCase	:Union[str, Any]		=      True

			self.check_hidden_states_output(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	, UpperCamelCase	:Union[str, Any]		=      self.model_tester.prepare_config_and_inputs_for_common()
		UpperCamelCase	:str		=      3

		UpperCamelCase	:Dict		=      (
		    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)
		)
		UpperCamelCase	:Any		=      (
		    config.patch_size
		    if isinstance(config.patch_size					,    collections.abc.Iterable      )
		    else (config.patch_size, config.patch_size)
		)

		UpperCamelCase	:List[str]		=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
		UpperCamelCase	:List[Any]		=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

		for model_class in self.all_model_classes:
			UpperCamelCase	:Dict		=      True
			self.check_hidden_states_output(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    (padded_height, padded_width)      )

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





	@slow
	def 		_A					(						self							:   Union[str, Any]      ):
		for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCamelCase	:str		=      SwinModel.from_pretrained(__lowerCamelCase      )
			self.assertIsNotNone(__lowerCamelCase      )





	def 		_A					(						self							:   int      ):
		UpperCamelCase	, UpperCamelCase	:str		=      self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase	:Any		=      _config_zero_init(__lowerCamelCase      )
		for model_class in self.all_model_classes:
			UpperCamelCase	:Tuple		=      model_class(config=__lowerCamelCase      )
			for name, param in model.named_parameters():
				if "embeddings" not in name and param.requires_grad:
					self.assertIn(
					    ((param.data.mean() * 1E9).round() / 1E9).item()					,    [0.0, 1.0]					,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,    )

@require_vision
@require_torch
class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase ):





	@cached_property
	def 		_A					(						self							:   str      ):
		return (
		    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""      )
		    if is_vision_available()
		    else None
		)





	@slow
	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Dict		=      SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""      ).to(__lowerCamelCase      )
		UpperCamelCase	:Dict		=      self.default_image_processor

		UpperCamelCase	:List[Any]		=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
		UpperCamelCase	:Any		=      image_processor(images=__lowerCamelCase					,    return_tensors="""pt"""      ).to(__lowerCamelCase      )

		# forward pass
		with torch.no_grad():
			UpperCamelCase	:List[Any]		=      model(**__lowerCamelCase      )

		# verify the logits
		UpperCamelCase	:List[Any]		=      torch.Size((1, 1_000)      )
		self.assertEqual(outputs.logits.shape					,    __lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      torch.tensor([-0.0948, -0.6454, -0.0921]      ).to(__lowerCamelCase      )
		self.assertTrue(torch.allclose(outputs.logits[0, :3]					,    __lowerCamelCase					,    atol=1E-4      )      )

@require_torch
class        _SCREAMING_SNAKE_CASE			(     unittest.TestCase						,  _a ):
	snake_case__				:	List[str]	      =    (SwinBackbone,) if is_torch_available() else ()
	snake_case__				:	Union[str, Any]	      =    SwinConfig





	def 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:int		=      SwinModelTester(self      )








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
	UpperCAmelCase_					:		str           = None


UpperCAmelCase_					:		int           = logging.get_logger(__name__)

UpperCAmelCase_					:		List[str]           = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_					:		Any           = {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_					:		Any           = {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Dict	      =    VOCAB_FILES_NAMES
	snake_case__				:	Optional[int]	      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:	str	      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:	int	      =    ["""input_ids""", """attention_mask"""]
	snake_case__				:	Dict	      =    XLMRobertaTokenizer





	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   List[Any]=None					,    __lowerCamelCase							:   int=None					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[int]="</s>"					,    __lowerCamelCase							:   int="</s>"					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[Any]="<unk>"					,    __lowerCamelCase							:   Union[str, Any]="<pad>"					,    __lowerCamelCase							:   Any="<mask>"					,    **__lowerCamelCase							:   List[str]					,    ):
		# Mask token behave like a normal word, i.e. include the space before it
		UpperCamelCase	:int		=      AddedToken(__lowerCamelCase					,    lstrip=__lowerCamelCase					,    rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase					,    __lowerCamelCase      ) else mask_token

		super().__init__(
		    __lowerCamelCase					,    tokenizer_file=__lowerCamelCase					,    bos_token=__lowerCamelCase					,    eos_token=__lowerCamelCase					,    sep_token=__lowerCamelCase					,    cls_token=__lowerCamelCase					,    unk_token=__lowerCamelCase					,    pad_token=__lowerCamelCase					,    mask_token=__lowerCamelCase					,    **__lowerCamelCase					,    )

		UpperCamelCase	:Optional[int]		=      vocab_file
		UpperCamelCase	:List[Any]		=      False if not self.vocab_file else True





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):

		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		UpperCamelCase	:int		=      [self.cls_token_id]
		UpperCamelCase	:Any		=      [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):
		UpperCamelCase	:str		=      [self.sep_token_id]
		UpperCamelCase	:int		=      [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 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[str] = None      ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
			    """tokenizer."""      )

		if not os.path.isdir(__lowerCamelCase      ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
			return
		UpperCamelCase	:Union[str, Any]		=      os.path.join(
		    __lowerCamelCase					,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

		if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ):
			copyfile(self.vocab_file					,    __lowerCamelCase      )

		return (out_vocab_file,)
