






"""simple docstring"""



import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase		:  Union[str, Any]						=		get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')

@require_sentencepiece
@require_tokenizers
class        a     (   A__,				unittest.TestCase	):
    UpperCAmelCase_ :     str	     =GPTSwaTokenizer
    UpperCAmelCase_ :     Any	     =False
    UpperCAmelCase_ :     str	     =True
    UpperCAmelCase_ :     Optional[int]	     =False







    def 			UpperCamelCase_      (			self       ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      lowercase							    =       GPTSwaTokenizer(_lowerCamelCase							,		eos_token='<unk>'							,		bos_token='<unk>'							,		pad_token='<unk>'       )

      tokenizer.save_pretrained(self.tmpdirname       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       'This is a test'
      lowercase							    =       'This is a test'
      return input_text, output_text







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       '<s>'
      lowercase							    =       1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase       )							,		_lowerCamelCase       )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase       )							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       list(self.get_tokenizer().get_vocab().keys()       )

      self.assertEqual(vocab_keys[0]							,		'<unk>'       )
      self.assertEqual(vocab_keys[1]							,		'<s>'       )
      self.assertEqual(vocab_keys[-1]							,		'j'       )
      self.assertEqual(len(_lowerCamelCase       )							,		2_0_0_0       )







    def 			UpperCamelCase_      (			self       ):
      self.assertEqual(self.get_tokenizer().vocab_size							,		2_0_0_0       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       GPTSwaTokenizer(_lowerCamelCase       )

      lowercase							    =       tokenizer.tokenize('This is a test'       )
      self.assertListEqual(_lowerCamelCase							,		['▁This', '▁is', '▁a', '▁t', 'est']       )

      self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase       )							,		[4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]       )

      lowercase							    =       tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
      # fmt: off
      self.assertListEqual(
          _lowerCamelCase							,		['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']							,		)
      # fmt: on

      lowercase							    =       tokenizer.convert_tokens_to_ids(_lowerCamelCase       )
      self.assertListEqual(
          _lowerCamelCase							,		[2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]							,		)

      lowercase							    =       tokenizer.convert_ids_to_tokens(_lowerCamelCase       )
      # fmt: off
      self.assertListEqual(
          _lowerCamelCase							,		['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']       )
      # fmt: on







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       GPTSwaTokenizer(_lowerCamelCase       )
      lowercase							    =       ['This is a test', 'I was born in 92000, and this is falsé.']
      lowercase							    =       [
          [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
          [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
      ]

      # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
      for text, expected_ids in zip(_lowerCamelCase							,		_lowerCamelCase       ):
        self.assertListEqual(tokenizer.encode_fast(_lowerCamelCase       )							,		_lowerCamelCase       )

      # Test that decode_fast returns the input text
      for text, token_ids in zip(_lowerCamelCase							,		_lowerCamelCase       ):
        self.assertEqual(tokenizer.decode_fast(_lowerCamelCase       )							,		_lowerCamelCase       )







    @slow
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       [
          '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
          'Hey there, how are you doing this fine day?',
          'This is a text with a trailing spaces followed by a dot     .',
          'Häj sväjs lillebrör! =)',
          'Det är inget fel på Mr. Cool',
      ]

      # fmt: off
      lowercase							    =       {'input_ids': [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
      # fmt: on
      self.tokenizer_integration_test_util(
          expected_encoding=_lowerCamelCase							,		model_name='AI-Sweden/gpt-sw3-126m'							,		sequences=_lowerCamelCase							,		)










from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




