



"""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


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

snake_case   					=				{
    '''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''',
    }
}

snake_case   					=				{
    '''AI-Sweden/gpt-sw3-126m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-350m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-20b''': 2_0_4_8,
}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Dict           =  VOCAB_FILES_NAMES
   A__		:	Union[str, Any]           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	Any           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Tuple           =  ['''input_ids''', '''attention_mask''']



   def __init__(       self    :	int							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Dict=False							,       __lowerCamelCase    :	int=False							,       __lowerCamelCase    :	Dict=False							,       __lowerCamelCase    :	Union[str, Any]=None							,       __lowerCamelCase    :	Any=None							,       __lowerCamelCase    :	Union[str, Any]=None							,       __lowerCamelCase    :	int=None							,       __lowerCamelCase    :	Optional[Dict[str, Any]] = None							,       **__lowerCamelCase    :	List[str]							,       ):



     """simple docstring"""
     _snake_case   	=      {} if sp_model_kwargs is None else sp_model_kwargs

     _snake_case   	=      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'''				)
       _snake_case   	=      '''None'''

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     _snake_case   	=      '''<|endoftext|>''' if eos_token is None else eos_token
     _snake_case   	=      '''<unk>''' if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
       _snake_case   	=      unk_token if pad_token is None else pad_token
       _snake_case   	=      eos_token if bos_token is None else bos_token
     else:
       _snake_case   	=      '''<pad>''' if pad_token is None else pad_token
       _snake_case   	=      '''<s>''' if bos_token is None else bos_token

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

     _snake_case   	=      do_lower_case
     _snake_case   	=      remove_space
     _snake_case   	=      keep_accents
     _snake_case   	=      vocab_file

     _snake_case   	=      spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(__lowerCamelCase				)

     # Used for whitespace normalization in input texts
     # fmt : off
     _snake_case   	=      {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
     # fmt : on

     # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
     _snake_case   	=      re.compile(
         f"""[{''.join(map(__lowerCamelCase							,       list(range(0							,       9				)				) + list(range(1_1							,       3_2				)				) + list(range(1_2_7							,       1_6_0				)				) + [1_6_0, 1_7_3, 8_2_0_3]				)				)}]"""				)



   def __getstate__(       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      self.__dict__.copy()
     _snake_case   	=      None
     return state



   def __setstate__(       self    :	List[Any]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      d

     # for backward compatibility
     if not hasattr(self							,       '''sp_model_kwargs'''				):
       _snake_case   	=      {}

     _snake_case   	=      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    :	Tuple				):



     """simple docstring"""
     return len(self.sp_model				)



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      self.non_printing_characters_re.sub(''''''							,       __lowerCamelCase				)

     # Normalize whitespaces
     _snake_case   	=      ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]				)

     # NFC Unicode normalization
     _snake_case   	=      unicodedata.normalize('''NFC'''							,       __lowerCamelCase				)
     return text



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	str							,       **__lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.preprocess_text(__lowerCamelCase				)
     return self.sp_model.encode(__lowerCamelCase							,       out_type=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     return self.sp_model.PieceToId(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.sp_model.IdToPiece(__lowerCamelCase				)



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	str				):



     """simple docstring"""
     return out_string



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      []
     _snake_case   	=      ''''''
     _snake_case   	=      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(__lowerCamelCase				) + token
         _snake_case   	=      True
         _snake_case   	=      []
       else:
         current_sub_tokens.append(__lowerCamelCase				)
         _snake_case   	=      False
     out_string += self.sp_model.decode(__lowerCamelCase				)

     return out_string



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      {self.convert_ids_to_tokens(__lowerCamelCase				): i for i in range(self.vocab_size				)}
     vocab.update(self.added_tokens_encoder				)
     return vocab



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if not os.path.isdir(__lowerCamelCase				):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
       return
     _snake_case   	=      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				) and os.path.isfile(self.vocab_file				):
       copyfile(self.vocab_file							,       __lowerCamelCase				)
     elif not os.path.isfile(self.vocab_file				):
       with open(__lowerCamelCase							,       '''wb'''				) as fi:
         _snake_case   	=      self.sp_model.serialized_model_proto()
         fi.write(__lowerCamelCase				)

     return (out_vocab_file,)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Union[str, List[str]]							,       __lowerCamelCase    :	Union[str, bool] = False				):



     """simple docstring"""

     if isinstance(__lowerCamelCase							,       __lowerCamelCase				):
       _snake_case   	=      self.preprocess_text(__lowerCamelCase				)
       _snake_case   	=      self.sp_model.encode(__lowerCamelCase				)
     else:
       _snake_case   	=      [self.preprocess_text(__lowerCamelCase				) for t in text]
       _snake_case   	=      self.sp_model.encode(__lowerCamelCase				)

     if return_tensors is True or return_tensors == "pt":
       _snake_case   	=      torch.tensor(__lowerCamelCase				)

     return token_ids



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Union[int, List[int]]				):



     """simple docstring"""
     return self.sp_model.decode(__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	"Conversation"				):



     """simple docstring"""
     _snake_case   	=      [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
     _snake_case   	=      (
         f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__lowerCamelCase				) + f"""{self.bos_token}Bot:"""
     )
     return self.encode(text=__lowerCamelCase				)








import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class 							UpperCAmelCase_						:
							"""simple docstring"""







							def __init__(     self			,				_a			,				_a=1_3			,				_a=3			,				_a=True			,				_a=True			,				_a=0.1			,				_a=0.1			,				_a=2_2_4			,				_a=1_0_0_0			,				_a=[3, 3, 6, 4]			,				_a=[4_8, 5_6, 1_1_2, 2_2_0]			,				)       ->       Tuple:
												_a      :       Dict   =						parent
												_a      :       Optional[int]   =						batch_size
												_a      :       Optional[Any]   =						num_channels
												_a      :       Union[str, Any]   =						is_training
												_a      :       Tuple   =						use_labels
												_a      :       Dict   =						hidden_dropout_prob
												_a      :       List[Any]   =						attention_probs_dropout_prob
												_a      :       Dict   =						num_labels
												_a      :       List[str]   =						image_size
												_a      :       Dict   =						layer_depths
												_a      :       str   =						embed_dims


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

												_a      :       int   =						None
												if self.use_labels:
																	_a      :       Optional[Any]   =						ids_tensor([self.batch_size]			,				self.num_labels			)

												_a      :       Dict   =						self.get_config()

												return config, pixel_values, labels


							def 							__lowercase							(     self			)       ->       int:
												return SwiftFormerConfig(
												    depths=self.layer_depths			,				embed_dims=self.embed_dims			,				mlp_ratio=4			,				downsamples=[True, True, True, True]			,				hidden_act='''gelu'''			,				num_labels=self.num_labels			,				down_patch_size=3			,				down_stride=2			,				down_pad=1			,				drop_rate=0.0			,				drop_path_rate=0.0			,				use_layer_scale=_a			,				layer_scale_init_value=1e-5			,				)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       str:
												_a      :       List[Any]   =						SwiftFormerModel(config=_a			)
												model.to(_a			)
												model.eval()
												_a      :       Optional[int]   =						model(_a			)
												self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.embed_dims[-1], 7, 7)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       Optional[Any]:
												_a      :       List[str]   =						self.num_labels
												_a      :       Optional[int]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()
												_a      :       List[str]   =						model(_a			,				labels=_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)

												_a      :       Union[str, Any]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()

												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
												_a      :       Optional[Any]   =						model(_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)




							def 							__lowercase							(     self			)       ->       Tuple:
												((_a) ,   (_a) ,   (_a))      :       Optional[int]   =						self.prepare_config_and_inputs()
												_a      :       List[Any]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							UpperCAmelCase__			:		Optional[int]             =       (
							    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Optional[Any]             =       False
							UpperCAmelCase__			:		str             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		str             =       False


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Union[str, Any]   =						SwiftFormerModelTester(self			)
												_a      :       int   =						ConfigTester(
												    self			,				config_class=_a			,				has_text_modality=_a			,				hidden_size=3_7			,				num_attention_heads=1_2			,				num_hidden_layers=1_2			,				)


							def 							__lowercase							(     self			)       ->       int:
												self.config_tester.run_common_tests()


							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							def 							__lowercase							(     self			)       ->       Dict:
												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       Any   =						model_class(_a			)
																	_a      :       int   =						model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_a			,				nn.Linear			)			)


							def 							__lowercase							(     self			)       ->       str:
												_a ,   _a      :       str   =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a      :       List[str]   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       int   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Any   =						SwiftFormerModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)


							@unittest.skip(reason='''SwiftFormer does not output attentions'''			)
							def 							__lowercase							(     self			)       ->       List[Any]:
												pass


							def 							__lowercase							(     self			)       ->       int:
												def check_hidden_states_output(_a			,				_a			,				_a			):
																	_a      :       Optional[int]   =						model_class(_a			)
																	model.to(_a			)
																	model.eval()

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

																	_a      :       Optional[Any]   =						outputs.hidden_states

																	_a      :       Union[str, Any]   =						8
																	self.assertEqual(len(_a			)			,				_a			)  # TODO

																	# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																	# with the width and height being successively divided by 2, after every 2 blocks
																	for i in range(len(_a			)			):
																						self.assertEqual(
																						    hidden_states[i].shape			,				torch.Size(
																						        [
																						            self.model_tester.batch_size,
																						            self.model_tester.embed_dims[i // 2],
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						        ]			)			,				)

												_a ,   _a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       str   =						True
																	check_hidden_states_output(_a			,				_a			,				_a			)

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

																	check_hidden_states_output(_a			,				_a			,				_a			)


							def 							__lowercase							(     self			)       ->       str:
												def _config_zero_init(_a			):
																	_a      :       List[Any]   =						copy.deepcopy(_a			)
																	for key in configs_no_init.__dict__.keys():
																						if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																											setattr(_a			,				_a			,				1e-1_0			)
																						if isinstance(getattr(_a			,				_a			,				_a			)			,				_a			):
																											_a      :       int   =						_config_zero_init(getattr(_a			,				_a			)			)
																											setattr(_a			,				_a			,				_a			)
																	return configs_no_init

												_a ,   _a      :       Any   =						self.model_tester.prepare_config_and_inputs_for_common()

												_a      :       Dict   =						_config_zero_init(_a			)
												for model_class in self.all_model_classes:
																	_a      :       Dict   =						model_class(config=_a			)
																	for name, param in model.named_parameters():
																						if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9) / 1e9).round().item()			,				[0.0, 1.0]			,				msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""			,				)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



def   __UpperCAmelCase					(      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Optional[int]   =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image







@require_torch
@require_vision
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							@cached_property
							def 							__lowercase							(     self			)       ->       str:
												return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''			) if is_vision_available() else None




							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''			).to(_a			)

												_a      :       Any   =						self.default_image_processor
												_a      :       Any   =						prepare_img()
												_a      :       Any   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       Optional[Any]   =						model(**_a			)

												# verify the logits
												_a      :       List[str]   =						torch.Size((1, 1_0_0_0)			)
												self.assertEqual(outputs.logits.shape			,				_a			)

												_a      :       int   =						torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]			).to(_a			)
												self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				_a			,				atol=1e-4			)			)



