





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_rembert import RemBertTokenizer
else:
    snake_case_				=			None

snake_case_				=			logging.get_logger(__name__)
snake_case_				=			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_				=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

snake_case_				=			{
    'google/rembert': 256,
}

snake_case_				=			'▁'

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				Optional[int]							=    VOCAB_FILES_NAMES
      A_						:				Optional[int]							=    PRETRAINED_VOCAB_FILES_MAP
      A_						:				Optional[int]							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A_						:				Tuple							=    RemBertTokenizer


      def __init__(self       :      Union[str, Any]				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			a__       :      Union[str, Any]=True				,			a__       :      Tuple=True				,			a__       :      Dict=False				,			a__       :      Tuple="[CLS]"				,			a__       :      Tuple="[SEP]"				,			a__       :      List[str]="<unk>"				,			a__       :      str="[SEP]"				,			a__       :      Any="<pad>"				,			a__       :      List[Any]="[CLS]"				,			a__       :      str="[MASK]"				,			**a__       :      Tuple				,			):





         """simple docstring"""



         __snake_case							       =   AddedToken(a__				,			lstrip=a__				,			rstrip=a__      ) if isinstance(a__				,			a__      ) else mask_token

         super().__init__(
             a__				,			tokenizer_file=a__				,			do_lower_case=a__				,			remove_space=a__				,			keep_accents=a__				,			bos_token=a__				,			eos_token=a__				,			unk_token=a__				,			sep_token=a__				,			pad_token=a__				,			cls_token=a__				,			mask_token=a__				,			**a__				,			)

         __snake_case							       =   do_lower_case
         __snake_case							       =   remove_space
         __snake_case							       =   keep_accents
         __snake_case							       =   vocab_file
         __snake_case							       =   False if not self.vocab_file else True


      def    a (self       :      List[Any]				,			a__       :      List[int]				,			a__       :      Optional[List[int]] = None      ):





         """simple docstring"""



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


      def    a (self       :      Tuple				,			a__       :      List[int]				,			a__       :      Optional[List[int]] = None				,			a__       :      bool = False      ):





         """simple docstring"""



         if already_has_special_tokens:
            if token_ids_a is not None:
               raise ValueError(
                   '''You should not supply a second sequence if the provided sequence of '''
                   '''ids is already formatted with special tokens for the model.'''      )
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
            return [1] + ([0] * len(a__      )) + [1] + ([0] * len(a__      )) + [1]
         return [1] + ([0] * len(a__      )) + [1]


      def    a (self       :      str				,			a__       :      List[int]				,			a__       :      Optional[List[int]] = None      ):





         """simple docstring"""



         __snake_case							       =   [self.sep_token_id]
         __snake_case							       =   [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      ) * [0] + len(token_ids_a + sep      ) * [1]


      def    a (self       :      Any				,			a__       :      str				,			a__       :      Optional[str] = None      ):





         """simple docstring"""



         if not os.path.isdir(a__      ):
            logger.error('''Vocabulary path ({}) should be a directory'''.format(a__      )      )
            return
         __snake_case							       =   os.path.join(
             a__				,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

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

         return (out_vocab_file,)








import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
    import torch

    from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      str				,			a__       :      Dict				,			a__       :      Tuple=None				,			a__       :      List[Any]=None				,			a__       :      Dict=None				,			a__       :      Union[str, Any]="resnet50"				,			a__       :      Dict=3				,			a__       :      str=32				,			a__       :      int=3				,			a__       :      Dict=True				,			a__       :      Any=True				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   out_indices if out_indices is not None else [4]
         __snake_case							       =   stage_names
         __snake_case							       =   out_features
         __snake_case							       =   backbone
         __snake_case							       =   batch_size
         __snake_case							       =   image_size
         __snake_case							       =   num_channels
         __snake_case							       =   use_pretrained_backbone
         __snake_case							       =   is_training


      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
         __snake_case							       =   self.get_config()

         return config, pixel_values


      def    a (self       :      Any      ):





         """simple docstring"""



         return TimmBackboneConfig(
             image_size=self.image_size				,			num_channels=self.num_channels				,			out_features=self.out_features				,			out_indices=self.out_indices				,			stage_names=self.stage_names				,			use_pretrained_backbone=self.use_pretrained_backbone				,			backbone=self.backbone				,			)


      def    a (self       :      List[Any]				,			a__       :      int				,			a__       :      int      ):





         """simple docstring"""



         __snake_case							       =   TimmBackbone(config=a__      )
         model.to(a__      )
         model.eval()
         with torch.no_grad():
            __snake_case							       =   model(a__      )
         self.parent.assertEqual(
             result.feature_map[-1].shape				,			(self.batch_size, model.channels[-1], 14, 14)				,			)


      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         __snake_case       ,	__snake_case							       =   config_and_inputs
         __snake_case							       =   {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
@require_timm
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Union[str, Any]							=    (TimmBackbone,) if is_torch_available() else ()
      A_						:				Optional[Any]							=    {'feature-extraction': TimmBackbone} if is_torch_available() else {}
      A_						:				List[Any]							=    False
      A_						:				Dict							=    False
      A_						:				Any							=    False
      A_						:				List[Any]							=    False


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   TimmBackboneModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			has_text_modality=a__      )


      def    a (self       :      Any      ):





         """simple docstring"""



         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       :      int      ):





         """simple docstring"""



         __snake_case							       =   '''resnet18'''
         __snake_case							       =   '''microsoft/resnet-18'''

         __snake_case							       =   AutoBackbone.from_pretrained(a__				,			use_timm_backbone=a__      )
         __snake_case							       =   AutoBackbone.from_pretrained(a__      )

         self.assertEqual(len(timm_model.out_features      )				,			len(transformers_model.out_features      )      )
         self.assertEqual(len(timm_model.stage_names      )				,			len(transformers_model.stage_names      )      )
         self.assertEqual(timm_model.channels				,			transformers_model.channels      )
         # Out indices are set to the last layer by default. For timm models, we don't know
         # the number of layers in advance, so we set it to (-1,), whereas for transformers
         # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
         self.assertEqual(timm_model.out_indices				,			(-1,)      )
         self.assertEqual(transformers_model.out_indices				,			[len(timm_model.stage_names      ) - 1]      )

         __snake_case							       =   AutoBackbone.from_pretrained(a__				,			use_timm_backbone=a__				,			out_indices=[1, 2, 3]      )
         __snake_case							       =   AutoBackbone.from_pretrained(a__				,			out_indices=[1, 2, 3]      )

         self.assertEqual(timm_model.out_indices				,			transformers_model.out_indices      )
         self.assertEqual(len(timm_model.out_features      )				,			len(transformers_model.out_features      )      )
         self.assertEqual(timm_model.channels				,			transformers_model.channels      )


      @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''      )
      def    a (self       :      str      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone initialization is managed on the timm side'''      )
      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''      )
      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         pass


      @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''      )
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''model weights aren\'t tied in TimmBackbone.'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         pass


      @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
      def    a (self       :      Dict      ):





         """simple docstring"""



         pass


      @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''      )
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      @unittest.skip('''Safetensors is not supported by timm.'''      )
      def    a (self       :      Tuple      ):





         """simple docstring"""



         pass


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





         """simple docstring"""



         pass


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

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

            __snake_case							       =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,			a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case							       =   True
         __snake_case							       =   self.has_attentions

         # no need to test all models as different heads yield the same functionality
         __snake_case							       =   self.all_model_classes[0]
         __snake_case							       =   model_class(a__      )
         model.to(a__      )

         __snake_case							       =   self._prepare_for_class(a__				,			a__      )
         __snake_case							       =   model(**a__      )
         __snake_case							       =   outputs[0][-1]

         # Encoder-/Decoder-only models
         __snake_case							       =   outputs.hidden_states[0]
         hidden_states.retain_grad()

         if self.has_attentions:
            __snake_case							       =   outputs.attentions[0]
            attentions.retain_grad()

         output.flatten()[0].backward(retain_graph=a__      )

         self.assertIsNotNone(hidden_states.grad      )

         if self.has_attentions:
            self.assertIsNotNone(attentions.grad      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()
            __snake_case							       =   model(**a__      )

            self.assertEqual(len(result.feature_maps      )				,			len(config.out_indices      )      )
            self.assertEqual(len(model.channels      )				,			len(config.out_indices      )      )

            # Check output of last stage is taken if out_features=None, out_indices=None
            __snake_case							       =   copy.deepcopy(a__      )
            __snake_case							       =   None
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()
            __snake_case							       =   model(**a__      )

            self.assertEqual(len(result.feature_maps      )				,			1      )
            self.assertEqual(len(model.channels      )				,			1      )

            # Check backbone can be initialized with fresh weights
            __snake_case							       =   copy.deepcopy(a__      )
            __snake_case							       =   False
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()
            __snake_case							       =   model(**a__      )

