


"""simple docstring"""






import inspect
import unittest

from transformers import BitConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
   from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=32    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=10    ,				__UpperCAmelCase=[8, 16, 32, 64]    ,				__UpperCAmelCase=[1, 1, 2, 1]    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase="relu"    ,				__UpperCAmelCase=3    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=["stage2", "stage3", "stage4"]    ,				__UpperCAmelCase=[2, 3, 4]    ,				__UpperCAmelCase=1    ,				)       ->							Dict:
          _a    	=				parent
          _a    	=				batch_size
          _a    	=				image_size
          _a    	=				num_channels
          _a    	=				embeddings_size
          _a    	=				hidden_sizes
          _a    	=				depths
          _a    	=				is_training
          _a    	=				use_labels
          _a    	=				hidden_act
          _a    	=				num_labels
          _a    	=				scope
          _a    	=				len(__UpperCAmelCase							)
          _a    	=				out_features
          _a    	=				out_indices
          _a    	=				num_groups


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

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

          _a    	=				self.get_config()

          return config, pixel_values, labels


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          return BitConfig(
              num_channels=self.num_channels    ,				embeddings_size=self.embeddings_size    ,				hidden_sizes=self.hidden_sizes    ,				depths=self.depths    ,				hidden_act=self.hidden_act    ,				num_labels=self.num_labels    ,				out_features=self.out_features    ,				out_indices=self.out_indices    ,				num_groups=self.num_groups    ,				)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _a    	=				BitModel(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(
              result.last_hidden_state.shape    ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,				)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Dict:
          _a    	=				self.num_labels
          _a    	=				BitForImageClassification(__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase    ,				labels=__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							List[Any]:
          _a    	=				BitBackbone(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase							)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps							)    ,				len(config.out_features							)							)
          self.parent.assertListEqual(list(result.feature_maps[0].shape							)    ,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

          # verify channels
          self.parent.assertEqual(len(model.channels							)    ,				len(config.out_features							)							)
          self.parent.assertListEqual(model.channels    ,				config.hidden_sizes[1:]							)

          # verify backbone works with out_features=None
          _a    	=				None
          _a    	=				BitBackbone(config=__UpperCAmelCase							)
          model.to(__UpperCAmelCase							)
          model.eval()
          _a    	=				model(__UpperCAmelCase							)

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

          # verify channels
          self.parent.assertEqual(len(model.channels							)    ,				1							)
          self.parent.assertListEqual(model.channels    ,				[config.hidden_sizes[-1]]							)





     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				self.prepare_config_and_inputs()
          _a	,      _a	,      _a    	=				config_and_inputs
          _a    	=				{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class     __lowerCamelCase  (					a__  ,					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     A_       :	str    =		(
         {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
         if is_torch_available()
         else {}
     )

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


     def 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				BitModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          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 							_UpperCAmelCase      (      self							)       ->							Any:
          return


     @unittest.skip(reason='''Bit does not output attentions'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          pass


     @unittest.skip(reason='''Bit does not use inputs_embeds'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          pass


     @unittest.skip(reason='''Bit does not support input and output embeddings'''							)
     def 							_UpperCAmelCase      (      self							)       ->							int:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()

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

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


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


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*__UpperCAmelCase							)


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

          for model_class in self.all_model_classes:
               _a    	=				model_class(config=__UpperCAmelCase							)
               for name, module in model.named_modules():
                    if isinstance(__UpperCAmelCase    ,				(nn.BatchNormad, nn.GroupNorm)							):
                         self.assertTrue(
                             torch.all(module.weight == 1							)    ,				msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,				)
                         self.assertTrue(
                             torch.all(module.bias == 0							)    ,				msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,				)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          def check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							):
               _a    	=				model_class(__UpperCAmelCase							)
               model.to(__UpperCAmelCase							)
               model.eval()

               with torch.no_grad():
                    _a    	=				model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase							)							)

               _a    	=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _a    	=				self.model_tester.num_stages
               self.assertEqual(len(__UpperCAmelCase							)    ,				expected_num_stages + 1							)

               # Bit's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]							)    ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,				)

          _a	,      _a    	=				self.model_tester.prepare_config_and_inputs_for_common()
          _a    	=				['''preactivation''', '''bottleneck''']
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    _a    	=				layer_type
                    _a    	=				True
                    check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)

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

                    check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)


     @unittest.skip(reason='''Bit does not use feedforward chunking'''							)
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          pass


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a    	=				BitModel.from_pretrained(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)







def       A_   (   ):






     """simple docstring"""

     _a    	=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image


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

     '''simple docstring'''


     @cached_property
     def 							_UpperCAmelCase      (      self							)       ->							Any:
          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
          )





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(__UpperCAmelCase							)

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

          # forward pass
          with torch.no_grad():
               _a    	=				model(**__UpperCAmelCase							)

          # verify the logits
          _a    	=				torch.Size((1, 1000)							)
          self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase							)

          _a    	=				torch.tensor([[-0.6526, -0.5263, -1.4398]]							).to(__UpperCAmelCase							)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,				__UpperCAmelCase    ,				atol=1e-4							)							)







@require_torch
class     __lowerCamelCase  (					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	Dict    =		(BitBackbone,) if is_torch_available() else ()
     A_       :	Union[str, Any]    =		BitConfig

     A_       :	List[Any]    =		False


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				BitModelTester(self							)



"""simple docstring"""






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

import sentencepiece as spm

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


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

__snake_case		=       {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

__snake_case		=       {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

__snake_case		=       '''▁'''


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		VOCAB_FILES_NAMES
     A_       :	int    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	Optional[int]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[MASK]"    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          _a    	=				(
              AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase    ,				normalized=__UpperCAmelCase							)
              if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							)
              else mask_token
          )

          _a    	=				{} if sp_model_kwargs is None else sp_model_kwargs

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

          _a    	=				do_lower_case
          _a    	=				remove_space
          _a    	=				keep_accents
          _a    	=				vocab_file

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							str:
          return len(self.sp_model							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				{self.convert_ids_to_tokens(__UpperCAmelCase							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def __getstate__(      self							)       ->							int:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							Tuple:
          _a    	=				d

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

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(self.vocab_file							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          if self.remove_space:
               _a    	=				''' '''.join(inputs.strip().split()							)
          else:
               _a    	=				inputs
          _a    	=				outputs.replace('''``'''    ,				'''"'''							).replace('''\'\''''    ,				'''"'''							)

          if not self.keep_accents:
               _a    	=				unicodedata.normalize('''NFKD'''    ,				__UpperCAmelCase							)
               _a    	=				''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase							)]							)
          if self.do_lower_case:
               _a    	=				outputs.lower()

          return outputs


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				self.preprocess_text(__UpperCAmelCase							)
          _a    	=				self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase							)
          _a    	=				[]
          for piece in pieces:
               if len(__UpperCAmelCase							) > 1 and piece[-1] == str(''','''							) and piece[-2].isdigit():
                    _a    	=				self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase    ,				''''''							)							)
                    if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0]							) == 1:
                              _a    	=				cur_pieces[1:]
                         else:
                              _a    	=				cur_pieces[0][1:]
                    cur_pieces.append(piece[-1]							)
                    new_pieces.extend(__UpperCAmelCase							)
               else:
                    new_pieces.append(__UpperCAmelCase							)

          return new_pieces


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							str:
          return self.sp_model.PieceToId(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							int:
          return self.sp_model.IdToPiece(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				[]
          _a    	=				''''''
          _a    	=				False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special:
                         out_string += " "
                    out_string += self.sp_model.decode(__UpperCAmelCase							) + token
                    _a    	=				True
                    _a    	=				[]
               else:
                    current_sub_tokens.append(__UpperCAmelCase							)
                    _a    	=				False
          out_string += self.sp_model.decode(__UpperCAmelCase							)
          return out_string.strip()


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase							)

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[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 							_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
          _a    	=				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:
                    _a    	=				self.sp_model.serialized_model_proto()
                    fi.write(__UpperCAmelCase							)

          return (out_vocab_file,)