



'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

							if is_vision_available():
														from transformers import MaskFormerImageProcessor

if is_vision_available():
							from PIL import Image

class 	__magic_name__						:







							def __init__(							self					:       Tuple	,_UpperCAmelCase					:       Optional[Any]	,_UpperCAmelCase					:       Union[str, Any]=2	,_UpperCAmelCase					:       Tuple=True	,_UpperCAmelCase					:       Dict=False	,_UpperCAmelCase					:       Dict=10	,_UpperCAmelCase					:       Any=3	,_UpperCAmelCase					:       Tuple=32 * 4	,_UpperCAmelCase					:       Optional[int]=32 * 6	,_UpperCAmelCase					:       Tuple=4	,_UpperCAmelCase					:       List[str]=32	,):
									_a     :      Optional[int]        =							parent
									_a     :      Optional[int]        =							batch_size
									_a     :      Optional[int]        =							is_training
									_a     :      Dict        =							use_auxiliary_loss
									_a     :      Union[str, Any]        =							num_queries
									_a     :      str        =							num_channels
									_a     :      List[str]        =							min_size
									_a     :      int        =							max_size
									_a     :      Optional[Any]        =							num_labels
									_a     :      List[Any]        =							mask_feature_size







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      str        =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
									    __UpperCAmelCase		)

									_a     :      str        =							torch.ones([self.batch_size, self.min_size, self.max_size]	,device=__UpperCAmelCase		)

									_a     :      Any        =							(
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]	,device=__UpperCAmelCase		) > 0.5
									).float()
									_a     :      Optional[int]        =							(torch.rand((self.batch_size, self.num_labels)	,device=__UpperCAmelCase		) > 0.5).long()

									_a     :      Any        =							self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels







							def 							__lowercase			(							self					:       Dict		):
									return MaskFormerConfig.from_backbone_and_decoder_configs(
									    backbone_config=SwinConfig(
									        depths=[1, 1, 1, 1]	,)	,decoder_config=DetrConfig(
									        decoder_ffn_dim=128	,num_queries=self.num_queries	,decoder_attention_heads=2	,d_model=self.mask_feature_size	,)	,mask_feature_size=self.mask_feature_size	,fpn_feature_size=self.mask_feature_size	,num_channels=self.num_channels	,num_labels=self.num_labels	,)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      List[str]        =							self.prepare_config_and_inputs()
									_a     :      List[str]        =							{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
									return config, inputs_dict







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Any		):
									_a     :      Optional[int]        =							output.encoder_hidden_states
									_a     :      Optional[int]        =							output.pixel_decoder_hidden_states
									_a     :      Dict        =							output.transformer_decoder_hidden_states

									self.parent.assertTrue(len(__UpperCAmelCase		)	,len(config.backbone_config.depths		)		)
									self.parent.assertTrue(len(__UpperCAmelCase		)	,len(config.backbone_config.depths		)		)
									self.parent.assertTrue(len(__UpperCAmelCase		)	,config.decoder_config.decoder_layers		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       List[Any]=False		):
									with torch.no_grad():
											_a     :      int        =							MaskFormerModel(config=__UpperCAmelCase		)
											model.to(__UpperCAmelCase		)
											model.eval()

											_a     :      str        =							model(pixel_values=__UpperCAmelCase	,pixel_mask=__UpperCAmelCase		)
											_a     :      int        =							model(__UpperCAmelCase	,output_hidden_states=__UpperCAmelCase		)
									# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
									# encoder and pixel decoder
									self.parent.assertEqual(
									    output.transformer_decoder_last_hidden_state.shape	,(self.batch_size, self.num_queries, self.mask_feature_size)	,)
									# let's ensure the other two hidden state exists
									self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
									self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

									if output_hidden_states:
											self.check_output_hidden_state(__UpperCAmelCase	,__UpperCAmelCase		)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Dict        =							MaskFormerForInstanceSegmentation(config=__UpperCAmelCase		)
									model.to(__UpperCAmelCase		)
									model.eval()

									def comm_check_on_output(_UpperCAmelCase					:       Optional[Any]		):
											# let's still check that all the required stuff is there
											self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None		)
											self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None		)
											self.parent.assertTrue(result.encoder_last_hidden_state is not None		)
											# okay, now we need to check the logits shape
											# due to the encoder compression, masks have a //4 spatial size
											self.parent.assertEqual(
											    result.masks_queries_logits.shape	,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)	,)
											# + 1 for null class
											self.parent.assertEqual(
											    result.class_queries_logits.shape	,(self.batch_size, self.num_queries, self.num_labels + 1)		)

									with torch.no_grad():
											_a     :      List[Any]        =							model(pixel_values=__UpperCAmelCase	,pixel_mask=__UpperCAmelCase		)
											_a     :      Dict        =							model(__UpperCAmelCase		)

											comm_check_on_output(__UpperCAmelCase		)

											_a     :      Optional[int]        =							model(
											    pixel_values=__UpperCAmelCase	,pixel_mask=__UpperCAmelCase	,mask_labels=__UpperCAmelCase	,class_labels=__UpperCAmelCase		)

									comm_check_on_output(__UpperCAmelCase		)

									self.parent.assertTrue(result.loss is not None		)
									self.parent.assertEqual(result.loss.shape	,torch.Size([1]		)		)

@require_torch
class 	__magic_name__						(    SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[Any]   				=					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
							lowerCAmelCase       :    int   				=					(
							    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    Union[str, Any]   				=					False
							lowerCAmelCase       :    Dict   				=					False
							lowerCAmelCase       :    Tuple   				=					False
							lowerCAmelCase       :    List[Any]   				=					False







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      str        =							MaskFormerModelTester(self		)
									_a     :      List[Any]        =							ConfigTester(self	,config_class=__UpperCAmelCase	,has_text_modality=__UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any		):
									self.config_tester.run_common_tests()







							def 							__lowercase			(							self					:       str		):
									_a     :      str        =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase	,**__UpperCAmelCase	,output_hidden_states=__UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__UpperCAmelCase		)







							@unittest.skip(reason='MaskFormer does not use inputs_embeds'		)
							def 							__lowercase			(							self					:       Any		):
									pass







							@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method'		)
							def 							__lowercase			(							self					:       List[str]		):
									pass







							@unittest.skip(reason='MaskFormer is not a generative model'		)
							def 							__lowercase			(							self					:       Union[str, Any]		):
									pass







							@unittest.skip(reason='MaskFormer does not use token embeddings'		)
							def 							__lowercase			(							self					:       Union[str, Any]		):
									pass







							@require_torch_multi_gpu
							@unittest.skip(
							    reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'		)
							def 							__lowercase			(							self					:       str		):
									pass







							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
							def 							__lowercase			(							self					:       str		):
									pass







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Union[str, Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

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

											_a     :      Tuple        =							["""pixel_values"""]
											self.assertListEqual(arg_names[:1]	,__UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[Any]		):
									for model_name in ["facebook/maskformer-swin-small-coco"]:
											_a     :      List[str]        =							MaskFormerModel.from_pretrained(__UpperCAmelCase		)
											self.assertIsNotNone(__UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      List[Any]        =							(self.model_tester.min_size,) * 2
									_a     :      Any        =							{
									    """pixel_values""": torch.randn((2, 3, *size)	,device=__UpperCAmelCase		),
									    """mask_labels""": torch.randn((2, 10, *size)	,device=__UpperCAmelCase		),
									    """class_labels""": torch.zeros(2	,10	,device=__UpperCAmelCase		).long(),
									}

									_a     :      Tuple        =							MaskFormerForInstanceSegmentation(MaskFormerConfig()		).to(__UpperCAmelCase		)
									_a     :      Union[str, Any]        =							model(**__UpperCAmelCase		)
									self.assertTrue(outputs.loss is not None		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase	,**__UpperCAmelCase	,output_hidden_states=__UpperCAmelCase		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Any        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      int        =							model_class(__UpperCAmelCase		).to(__UpperCAmelCase		)
											_a     :      List[Any]        =							model(**__UpperCAmelCase	,output_attentions=__UpperCAmelCase		)
											self.assertTrue(outputs.attentions is not None		)







							def 							__lowercase			(							self					:       List[Any]		):
									if not self.model_tester.is_training:
											return
									# only MaskFormerForInstanceSegmentation has the loss
									_a     :      Dict        =							self.all_model_classes[1]
									_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs()

									_a     :      List[Any]        =							model_class(__UpperCAmelCase		)
									model.to(__UpperCAmelCase		)
									model.train()

									_a     :      List[str]        =							model(__UpperCAmelCase	,mask_labels=__UpperCAmelCase	,class_labels=__UpperCAmelCase		).loss
									loss.backward()







							def 							__lowercase			(							self					:       List[Any]		):
									# only MaskFormerForInstanceSegmentation has the loss
									_a     :      Tuple        =							self.all_model_classes[1]
									_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs()
									_a     :      Union[str, Any]        =							True
									_a     :      Tuple        =							True

									_a     :      Optional[Any]        =							model_class(__UpperCAmelCase		)
									model.to(__UpperCAmelCase		)
									model.train()

									_a     :      Dict        =							model(__UpperCAmelCase	,mask_labels=__UpperCAmelCase	,class_labels=__UpperCAmelCase		)

									_a     :      Optional[Any]        =							outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									_a     :      str        =							outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()
									# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
									_a     :      Union[str, Any]        =							outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									_a     :      List[Any]        =							outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=__UpperCAmelCase		)

									self.assertIsNotNone(encoder_hidden_states.grad		)
									self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
									self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
									self.assertIsNotNone(attentions.grad		)
__lowerCAmelCase									=   1e-4



def      __lowerCamelCase     (      )			->      Dict:
		_a     :      Union[str, Any]        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

@require_vision
@slow
class 	__magic_name__						(    unittest.TestCase   ):







							@cached_property
							def 							__lowercase			(							self					:       Any		):
									return (
									    MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco'		)
									    if is_vision_available()
									    else None
									)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Any        =							MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco'		).to(__UpperCAmelCase		)
									_a     :      str        =							self.default_image_processor
									_a     :      str        =							prepare_img()
									_a     :      Optional[int]        =							image_processor(__UpperCAmelCase	,return_tensors='pt'		).to(__UpperCAmelCase		)
									_a     :      Dict        =							inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
									# check size
									self.assertEqual(__UpperCAmelCase	,(1, 3, 800, 1088)		)

									with torch.no_grad():
											_a     :      Union[str, Any]        =							model(**__UpperCAmelCase		)

									_a     :      Optional[Any]        =							torch.tensor(
									    [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]		).to(__UpperCAmelCase		)
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]	,__UpperCAmelCase	,atol=__UpperCAmelCase		)		)

									_a     :      Dict        =							torch.tensor(
									    [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]		).to(__UpperCAmelCase		)
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]	,__UpperCAmelCase	,atol=__UpperCAmelCase		)		)

									_a     :      Optional[int]        =							torch.tensor(
									    [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]		).to(__UpperCAmelCase		)
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]	,__UpperCAmelCase	,atol=__UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      List[Any]        =							(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'		)
									    .to(__UpperCAmelCase		)
									    .eval()
									)
									_a     :      Optional[Any]        =							self.default_image_processor
									_a     :      List[str]        =							prepare_img()
									_a     :      str        =							image_processor(__UpperCAmelCase	,return_tensors='pt'		).to(__UpperCAmelCase		)
									_a     :      Optional[int]        =							inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
									# check size
									self.assertEqual(__UpperCAmelCase	,(1, 3, 800, 1088)		)

									with torch.no_grad():
											_a     :      List[Any]        =							model(**__UpperCAmelCase		)
									# masks_queries_logits
									_a     :      Optional[int]        =							outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape	,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,)
									_a     :      Optional[int]        =							[
									    [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
									    [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
									    [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
									]
									_a     :      Optional[int]        =							torch.tensor(__UpperCAmelCase		).to(__UpperCAmelCase		)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,__UpperCAmelCase	,atol=__UpperCAmelCase		)		)
									# class_queries_logits
									_a     :      Tuple        =							outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape	,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
									_a     :      Union[str, Any]        =							torch.tensor(
									    [
									        [1.65_12E00, -5.25_72E00, -3.35_19E00],
									        [3.61_69E-02, -5.90_25E00, -2.93_13E00],
									        [1.07_66E-04, -7.76_30E00, -5.12_63E00],
									    ]		).to(__UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,__UpperCAmelCase	,atol=__UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      List[Any]        =							(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff'		)
									    .to(__UpperCAmelCase		)
									    .eval()
									)
									_a     :      Optional[Any]        =							self.default_image_processor
									_a     :      int        =							prepare_img()
									_a     :      Optional[Any]        =							image_processor(__UpperCAmelCase	,return_tensors='pt'		).to(__UpperCAmelCase		)
									_a     :      str        =							inputs["""pixel_values"""].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
									# check size
									self.assertEqual(__UpperCAmelCase	,(1, 3, 800, 1088)		)

									with torch.no_grad():
											_a     :      str        =							model(**__UpperCAmelCase		)
									# masks_queries_logits
									_a     :      Optional[Any]        =							outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape	,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)	,)
									_a     :      int        =							[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.7711]]
									_a     :      List[str]        =							torch.tensor(__UpperCAmelCase		).to(__UpperCAmelCase		)
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]	,__UpperCAmelCase	,atol=__UpperCAmelCase		)		)
									# class_queries_logits
									_a     :      Optional[Any]        =							outputs.class_queries_logits
									self.assertEqual(
									    class_queries_logits.shape	,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)		)
									_a     :      Tuple        =							torch.tensor(
									    [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]		).to(__UpperCAmelCase		)
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]	,__UpperCAmelCase	,atol=__UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       Any		):
									_a     :      str        =							(
									    MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco'		)
									    .to(__UpperCAmelCase		)
									    .eval()
									)
									_a     :      Dict        =							self.default_image_processor

									_a     :      Union[str, Any]        =							image_processor(
									    [np.zeros((3, 800, 1333)		), np.zeros((3, 800, 1333)		)]	,segmentation_maps=[np.zeros((384, 384)		).astype(np.floataa		), np.zeros((384, 384)		).astype(np.floataa		)]	,return_tensors='pt'	,)

									_a     :      Tuple        =							inputs["""pixel_values"""].to(__UpperCAmelCase		)
									_a     :      Union[str, Any]        =							[el.to(__UpperCAmelCase		) for el in inputs["""mask_labels"""]]
									_a     :      Union[str, Any]        =							[el.to(__UpperCAmelCase		) for el in inputs["""class_labels"""]]

									with torch.no_grad():
											_a     :      Any        =							model(**__UpperCAmelCase		)

									self.assertTrue(outputs.loss is not None		)









'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase      						=	logging.get_logger(__name__)
_lowerCAmelCase      						=	{'''vocab_file''': '''spiece.model'''}

_lowerCAmelCase      						=	{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

_lowerCAmelCase      						=	{
    '''AI-Sweden/gpt-sw3-126m''': 2048,
    '''AI-Sweden/gpt-sw3-350m''': 2048,
    '''AI-Sweden/gpt-sw3-1.6b''': 2048,
    '''AI-Sweden/gpt-sw3-6.7b''': 2048,
    '''AI-Sweden/gpt-sw3-20b''': 2048,
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      VOCAB_FILES_NAMES
  __lowercase							:      str				=      PRETRAINED_VOCAB_FILES_MAP
  __lowercase							:      Dict				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase							:      Optional[int]				=      ['''input_ids''', '''attention_mask''']




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase = None  ,**__UpperCAmelCase  ,)    ->		None:
      lowerCAmelCase__ :  Optional[int]							      =					{} if sp_model_kwargs is None else sp_model_kwargs

      lowerCAmelCase__ :  Dict							      =					kwargs.get("""name_or_path"""      )
      if name_or_path is None:
          logger.warning(
              """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
              """ you are testing the model, this can safely be ignored"""      )
          lowerCAmelCase__ :  Tuple							      =					"""None"""

      # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
      lowerCAmelCase__ :  Union[str, Any]							      =					"""<|endoftext|>""" if eos_token is None else eos_token
      lowerCAmelCase__ :  Dict							      =					"""<unk>""" if unk_token is None else unk_token
      if "gpt-sw3-7b" in name_or_path:
          lowerCAmelCase__ :  Any							      =					unk_token if pad_token is None else pad_token
          lowerCAmelCase__ :  Dict							      =					eos_token if bos_token is None else bos_token
      else:
          lowerCAmelCase__ :  List[str]							      =					"""<pad>""" if pad_token is None else pad_token
          lowerCAmelCase__ :  Optional[int]							      =					"""<s>""" if bos_token is None else bos_token

      super().__init__(
          do_lower_case=__UpperCAmelCase  ,remove_space=__UpperCAmelCase  ,keep_accents=__UpperCAmelCase  ,bos_token=__UpperCAmelCase  ,eos_token=__UpperCAmelCase  ,unk_token=__UpperCAmelCase  ,pad_token=__UpperCAmelCase  ,sp_model_kwargs=self.sp_model_kwargs  ,**__UpperCAmelCase  ,)

      lowerCAmelCase__ :  Optional[int]							      =					do_lower_case
      lowerCAmelCase__ :  Dict							      =					remove_space
      lowerCAmelCase__ :  Optional[Any]							      =					keep_accents
      lowerCAmelCase__ :  int							      =					vocab_file

      lowerCAmelCase__ :  str							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(__UpperCAmelCase      )

      # Used for whitespace normalization in input texts
      # fmt : off
      lowerCAmelCase__ :  int							      =					{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
      # fmt : on

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      lowerCAmelCase__ :  List[str]							      =					re.compile(
          F"""[{''.join(map(__UpperCAmelCase  ,list(range(0  ,9      )      ) + list(range(11  ,32      )      ) + list(range(127  ,160      )      ) + [160, 173, 8203]      )      )}]"""      )




  def __getstate__(    self      )    ->		Any:
      lowerCAmelCase__ :  int							      =					self.__dict__.copy()
      lowerCAmelCase__ :  Optional[int]							      =					None
      return state




  def __setstate__(    self  ,__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[str]							      =					d

      # for backward compatibility
      if not hasattr(self  ,"""sp_model_kwargs"""      ):
          lowerCAmelCase__ :  Tuple							      =					{}

      lowerCAmelCase__ :  Optional[int]							      =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(self.vocab_file      )




  @property
  # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
  def 							UpperCAmelCase_	(    self      )    ->		int:
      return len(self.sp_model      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  Tuple							      =					self.non_printing_characters_re.sub(""""""  ,__UpperCAmelCase      )

      # Normalize whitespaces
      lowerCAmelCase__ :  List[Any]							      =					"""""".join([char if char not in self.whitespaces else """ """ for char in text]      )

      # NFC Unicode normalization
      lowerCAmelCase__ :  List[Any]							      =					unicodedata.normalize("""NFC"""  ,__UpperCAmelCase      )
      return text




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		List[str]:
      lowerCAmelCase__ :  List[Any]							      =					self.preprocess_text(__UpperCAmelCase      )
      return self.sp_model.encode(__UpperCAmelCase  ,out_type=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      return self.sp_model.PieceToId(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.sp_model.IdToPiece(__UpperCAmelCase      )




  @staticmethod
  def 							UpperCAmelCase_	(    __UpperCAmelCase      )    ->		str:
      return out_string




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      lowerCAmelCase__ :  int							      =					[]
      lowerCAmelCase__ :  Optional[int]							      =					""""""
      lowerCAmelCase__ :  Tuple							      =					False
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
              if not prev_is_special:
                  out_string += " "

              out_string += self.sp_model.decode(__UpperCAmelCase      ) + token
              lowerCAmelCase__ :  Union[str, Any]							      =					True
              lowerCAmelCase__ :  Optional[Any]							      =					[]
          else:
              current_sub_tokens.append(__UpperCAmelCase      )
              lowerCAmelCase__ :  Any							      =					False
      out_string += self.sp_model.decode(__UpperCAmelCase      )

      return out_string




  def 							UpperCAmelCase_	(    self      )    ->		Dict[str, int]:
      lowerCAmelCase__ :  Optional[int]							      =					{self.convert_ids_to_tokens(__UpperCAmelCase      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None      )    ->		Tuple[str]:
      if not os.path.isdir(__UpperCAmelCase      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      lowerCAmelCase__ :  Optional[int]							      =					os.path.join(
          __UpperCAmelCase  ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

      if os.path.abspath(self.vocab_file      ) != os.path.abspath(__UpperCAmelCase      ) and os.path.isfile(self.vocab_file      ):
          copyfile(self.vocab_file  ,__UpperCAmelCase      )
      elif not os.path.isfile(self.vocab_file      ):
          with open(__UpperCAmelCase  ,"""wb"""      ) as fi:
              lowerCAmelCase__ :  str							      =					self.sp_model.serialized_model_proto()
              fi.write(__UpperCAmelCase      )

      return (out_vocab_file,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = False      )    ->		Union[List[int], List[List[int]], "torch.Tensor"]:

      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase      ):
          lowerCAmelCase__ :  Tuple							      =					self.preprocess_text(__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					self.sp_model.encode(__UpperCAmelCase      )
      else:
          lowerCAmelCase__ :  int							      =					[self.preprocess_text(__UpperCAmelCase      ) for t in text]
          lowerCAmelCase__ :  Any							      =					self.sp_model.encode(__UpperCAmelCase      )

      if return_tensors is True or return_tensors == "pt":
          lowerCAmelCase__ :  Tuple							      =					torch.tensor(__UpperCAmelCase      )

      return token_ids




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		str:
      return self.sp_model.decode(__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[int]:
      lowerCAmelCase__ :  List[Any]							      =					[F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
      lowerCAmelCase__ :  Any							      =					(
          F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase      ) + F"""{self.bos_token}Bot:"""
      )
      return self.encode(text=__UpperCAmelCase      )


