

'''simple docstring'''






import os
import re
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


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__   		=      {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase__   		=      {
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Tuple							     =     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE       :    Tuple							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE       :    str							     =     ['input_ids', 'attention_mask']
  SCREAMING_SNAKE_CASE       :    List[int]							     =     []






  def __init__( self  :	str   ,lowercase__  :	int   ,lowercase__  :	Optional[Any]="<unk>"   ,lowercase__  :	str="<s>"   ,lowercase__  :	List[Any]="</s>"   ,lowercase__  :	Dict="<pad>"   ,lowercase__  :	int="[SEP]"   ,lowercase__  :	str="[MASK]"   ,lowercase__  :	Tuple="[CLS]"   ,lowercase__  :	Optional[Dict[str, Any]] = None   ,**lowercase__  :	str   ,):
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else bos_token
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else eos_token
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else unk_token
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else pad_token
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else cls_token
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else sep_token

   # Mask token behave like a normal word, i.e. include the space before it
   __lowercase				    =					AddedToken(lowercase__   ,lstrip=lowercase__   ,rstrip=lowercase__     ) if isinstance(lowercase__   ,lowercase__     ) else mask_token

   __lowercase				    =					{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=lowercase__   ,eos_token=lowercase__   ,unk_token=lowercase__   ,pad_token=lowercase__   ,sep_token=lowercase__   ,mask_token=lowercase__   ,cls_token=lowercase__   ,sp_model_kwargs=self.sp_model_kwargs   ,**lowercase__   ,)

   __lowercase				    =					vocab_file

   __lowercase				    =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(lowercase__     )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   return self.sp_model.get_piece_size()






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					{self.convert_ids_to_tokens(lowercase__     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def __getstate__( self  :	Dict     ):
   __lowercase				    =					self.__dict__.copy()
   __lowercase				    =					None
   return state






  def __setstate__( self  :	str   ,lowercase__  :	Optional[int]     ):
   __lowercase				    =					d

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

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






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	str     ):
   return self.sp_model.encode(lowercase__   ,out_type=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	Dict     ):
   return self.sp_model.piece_to_id(lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	List[str]     ):
   __lowercase				    =					self.sp_model.IdToPiece(lowercase__     )
   return token






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	Tuple     ):
   __lowercase				    =					[]
   __lowercase				    =					''''''
   __lowercase				    =					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(lowercase__     ) + token
     __lowercase				    =					True
     __lowercase				    =					[]
    else:
     current_sub_tokens.append(lowercase__     )
     __lowercase				    =					False
   out_string += self.sp_model.decode(lowercase__     )
   return out_string.strip()






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	List[int]   ,lowercase__  :	bool = False   ,lowercase__  :	bool = None   ,lowercase__  :	bool = True   ,**lowercase__  :	Optional[Any]   ,):
   __lowercase				    =					kwargs.pop('''use_source_tokenizer'''   ,lowercase__     )

   __lowercase				    =					self.convert_ids_to_tokens(lowercase__   ,skip_special_tokens=lowercase__     )

   # To avoid mixing byte-level and unicode for byte-level BPT
   # we need to build string separately for added tokens and byte-level tokens
   # cf. https://github.com/huggingface/transformers/issues/1133
   __lowercase				    =					[]
   __lowercase				    =					[]
   for token in filtered_tokens:
    if skip_special_tokens and token in self.all_special_ids:
     continue
    if token in self.added_tokens_encoder:
     if current_sub_text:
      sub_texts.append(self.convert_tokens_to_string(lowercase__     )     )
      __lowercase				    =					[]
     sub_texts.append(lowercase__     )
    else:
     current_sub_text.append(lowercase__     )
   if current_sub_text:
    sub_texts.append(self.convert_tokens_to_string(lowercase__     )     )

   # Mimic the behavior of the Rust tokenizer:
   # No space before [MASK] and [SEP]
   if spaces_between_special_tokens:
    __lowercase				    =					re.sub(r''' (\[(MASK|SEP)\])'''   ,r'''\1'''   ,''' '''.join(lowercase__     )     )
   else:
    __lowercase				    =					''''''.join(lowercase__     )

   __lowercase				    =					(
       clean_up_tokenization_spaces
       if clean_up_tokenization_spaces is not None
       else self.clean_up_tokenization_spaces
   )
   if clean_up_tokenization_spaces:
    __lowercase				    =					self.clean_up_tokenization(lowercase__     )
    return clean_text
   else:
    return text






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	str   ,lowercase__  :	Optional[str] = None     ):
   if not os.path.isdir(lowercase__     ):
    logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
    return
   __lowercase				    =					os.path.join(
       lowercase__   ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase__     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file   ,lowercase__     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(lowercase__   ,'''wb'''     ) as fi:
     __lowercase				    =					self.sp_model.serialized_model_proto()
     fi.write(lowercase__     )

   return (out_vocab_file,)






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None     ):
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   __lowercase				    =					[self.cls_token_id]
   __lowercase				    =					[self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None   ,lowercase__  :	bool = False     ):
   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=lowercase__   ,token_ids_a=lowercase__   ,already_has_special_tokens=lowercase__     )

   if token_ids_a is None:
    return [1] + ([0] * len(lowercase__     )) + [1]
   return [1] + ([0] * len(lowercase__     )) + [1] + ([0] * len(lowercase__     )) + [1]






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None     ):
   __lowercase				    =					[self.sep_token_id]
   __lowercase				    =					[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]









"""simple docstring"""







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      UpperCamelCase		:






						def __init__(		self  :Union[str, Any]			, __magic_name__  :List[Any]			, __magic_name__  :Union[str, Any]=None			, __magic_name__  :List[Any]=None			, __magic_name__  :Dict=None			, __magic_name__  :List[Any]="resnet50"			, __magic_name__  :Tuple=3			, __magic_name__  :Optional[Any]=32			, __magic_name__  :str=3			, __magic_name__  :str=True			, __magic_name__  :Optional[Any]=True			, )      ->str:
										lowercase    :      Tuple       = parent
										lowercase    :      Tuple       = out_indices if out_indices is not None else [4]
										lowercase    :      Union[str, Any]       = stage_names
										lowercase    :      Tuple       = out_features
										lowercase    :      Optional[int]       = backbone
										lowercase    :      Optional[Any]       = batch_size
										lowercase    :      Tuple       = image_size
										lowercase    :      Union[str, Any]       = num_channels
										lowercase    :      List[Any]       = use_pretrained_backbone
										lowercase    :      str       = is_training






						def      __snake_case      (		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
										lowercase    :      Optional[int]       = self.get_config()

										return config, pixel_values






						def      __snake_case      (		self  :Tuple				)      ->int:
										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      __snake_case      (		self  :Any			, __magic_name__  :List[str]			, __magic_name__  :Optional[int]				)      ->Tuple:
										lowercase    :      List[str]       = TimmBackbone(config=__magic_name__				)
										model.to(__magic_name__				)
										model.eval()
										with torch.no_grad():
														lowercase    :      str       = model(__magic_name__				)
										self.parent.assertEqual(
										    result.feature_map[-1].shape			, (self.batch_size, model.channels[-1], 14, 14)			, )






						def      __snake_case      (		self  :str				)      ->Dict:
										lowercase    :      Tuple       = self.prepare_config_and_inputs()
										lowercase      ,      lowercase    :      List[Any]       = config_and_inputs
										lowercase    :      Union[str, Any]       = {"""pixel_values""": pixel_values}
										return config, inputs_dict


@require_torch
@require_timm
class      UpperCamelCase		(__snake_case						,						__snake_case						,						__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		(TimmBackbone,) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE  : str												=		{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		False
						_SCREAMING_SNAKE_CASE  : int												=		False
						_SCREAMING_SNAKE_CASE  : List[Any]												=		False
						_SCREAMING_SNAKE_CASE  : List[str]												=		False






						def      __snake_case      (		self  :Dict				)      ->List[str]:
										lowercase    :      List[str]       = TimmBackboneModelTester(self				)
										lowercase    :      Any       = ConfigTester(self			, config_class=__magic_name__			, has_text_modality=__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]				)      ->List[Any]:
										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      __snake_case      (		self  :Union[str, Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = """resnet18"""
										lowercase    :      int       = """microsoft/resnet-18"""

										lowercase    :      Union[str, Any]       = AutoBackbone.from_pretrained(__magic_name__			, use_timm_backbone=__magic_name__				)
										lowercase    :      int       = AutoBackbone.from_pretrained(__magic_name__				)

										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]				)

										lowercase    :      Tuple       = AutoBackbone.from_pretrained(__magic_name__			, use_timm_backbone=__magic_name__			, out_indices=[1, 2, 3]				)
										lowercase    :      Any       = AutoBackbone.from_pretrained(__magic_name__			, 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      __snake_case      (		self  :Tuple				)      ->Optional[int]:
										pass






						@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""				)
						def      __snake_case      (		self  :Optional[Any]				)      ->int:
										pass






						@unittest.skip("""TimmBackbone initialization is managed on the timm side"""				)
						def      __snake_case      (		self  :Optional[Any]				)      ->Optional[Any]:
										pass






						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""				)
						def      __snake_case      (		self  :int				)      ->List[Any]:
										pass






						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""				)
						def      __snake_case      (		self  :int				)      ->Tuple:
										pass






						@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""				)
						def      __snake_case      (		self  :List[Any]				)      ->List[str]:
										pass






						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""				)
						def      __snake_case      (		self  :int				)      ->Any:
										pass






						@unittest.skip("""model weights aren't tied in TimmBackbone."""				)
						def      __snake_case      (		self  :int				)      ->int:
										pass






						@unittest.skip("""model weights aren't tied in TimmBackbone."""				)
						def      __snake_case      (		self  :str				)      ->Union[str, Any]:
										pass






						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""				)
						def      __snake_case      (		self  :int				)      ->Optional[int]:
										pass






						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""				)
						def      __snake_case      (		self  :List[Any]				)      ->Optional[Any]:
										pass






						@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""				)
						def      __snake_case      (		self  :Union[str, Any]				)      ->List[Any]:
										pass






						@unittest.skip("""TimmBackbone doesn't support output_attentions."""				)
						def      __snake_case      (		self  :Tuple				)      ->List[Any]:
										pass






						@unittest.skip("""Safetensors is not supported by timm."""				)
						def      __snake_case      (		self  :List[Any]				)      ->int:
										pass






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






						def      __snake_case      (		self  :Union[str, Any]				)      ->Union[str, Any]:
										lowercase      ,      lowercase    :      Optional[Any]       = self.model_tester.prepare_config_and_inputs_for_common()

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

														lowercase    :      Dict       = ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]			, __magic_name__				)






						def      __snake_case      (		self  :Any				)      ->List[str]:
										lowercase      ,      lowercase    :      str       = self.model_tester.prepare_config_and_inputs_for_common()
										lowercase    :      Any       = True
										lowercase    :      int       = self.has_attentions

										# no need to test all models as different heads yield the same functionality
										lowercase    :      Union[str, Any]       = self.all_model_classes[0]
										lowercase    :      Tuple       = model_class(__magic_name__				)
										model.to(__magic_name__				)

										lowercase    :      Optional[Any]       = self._prepare_for_class(__magic_name__			, __magic_name__				)
										lowercase    :      Dict       = model(**__magic_name__				)
										lowercase    :      List[str]       = outputs[0][-1]

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

										if self.has_attentions:
														lowercase    :      List[Any]       = outputs.attentions[0]
														attentions.retain_grad()

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

										self.assertIsNotNone(hidden_states.grad				)

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






						def      __snake_case      (		self  :Any				)      ->List[Any]:
										lowercase      ,      lowercase    :      int       = self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowercase    :      Optional[int]       = model_class(__magic_name__				)
														model.to(__magic_name__				)
														model.eval()
														lowercase    :      Any       = model(**__magic_name__				)

														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
														lowercase    :      List[Any]       = copy.deepcopy(__magic_name__				)
														lowercase    :      Dict       = None
														lowercase    :      str       = model_class(__magic_name__				)
														model.to(__magic_name__				)
														model.eval()
														lowercase    :      Optional[Any]       = model(**__magic_name__				)

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

														# Check backbone can be initialized with fresh weights
														lowercase    :      str       = copy.deepcopy(__magic_name__				)
														lowercase    :      int       = False
														lowercase    :      List[str]       = model_class(__magic_name__				)
														model.to(__magic_name__				)
														model.eval()
														lowercase    :      Dict       = model(**__magic_name__				)
