


'''simple docstring'''


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_big_bird import BigBirdTokenizer
else:
						lowerCamelCase__    	=							None

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

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'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__    	=							{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}


lowerCamelCase__    	=							'▁'






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Any          =     VOCAB_FILES_NAMES
			lowerCAmelCase :	Optional[Any]          =     PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase :	Any          =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase :	List[str]          =     BigBirdTokenizer
			lowerCAmelCase :	str          =     ["input_ids", "attention_mask"]
			lowerCAmelCase :	List[int]          =     []




			def __init__(		self  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     Optional[Any]="<unk>"	,  lowerCamelCase__  :     int="<s>"	,  lowerCamelCase__  :     str="</s>"	,  lowerCamelCase__  :     Optional[Any]="<pad>"	,  lowerCamelCase__  :     Tuple="[SEP]"	,  lowerCamelCase__  :     Union[str, Any]="[MASK]"	,  lowerCamelCase__  :     str="[CLS]"	,  **lowerCamelCase__  :     Optional[int]	,  )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      AddedToken(lowerCamelCase__	,  lstrip=lowerCamelCase__	,  rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) else bos_token
					_UpperCAmelCase		:       Optional[Any]								=      AddedToken(lowerCamelCase__	,  lstrip=lowerCamelCase__	,  rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) else eos_token
					_UpperCAmelCase		:       Any								=      AddedToken(lowerCamelCase__	,  lstrip=lowerCamelCase__	,  rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) else unk_token
					_UpperCAmelCase		:       List[str]								=      AddedToken(lowerCamelCase__	,  lstrip=lowerCamelCase__	,  rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) else pad_token
					_UpperCAmelCase		:       Tuple								=      AddedToken(lowerCamelCase__	,  lstrip=lowerCamelCase__	,  rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) else cls_token
					_UpperCAmelCase		:       List[str]								=      AddedToken(lowerCamelCase__	,  lstrip=lowerCamelCase__	,  rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) else sep_token

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

					super().__init__(
					    lowerCamelCase__	,  tokenizer_file=lowerCamelCase__	,  bos_token=lowerCamelCase__	,  eos_token=lowerCamelCase__	,  unk_token=lowerCamelCase__	,  sep_token=lowerCamelCase__	,  pad_token=lowerCamelCase__	,  cls_token=lowerCamelCase__	,  mask_token=lowerCamelCase__	,  **lowerCamelCase__	,  )

					_UpperCAmelCase		:       Optional[int]								=      vocab_file
					_UpperCAmelCase		:       Optional[Any]								=      False if not self.vocab_file else True




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     List[int]	,  lowerCamelCase__  :     Optional[List[int]] = None )   ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      [self.sep_token_id]
					_UpperCAmelCase		:       Optional[Any]								=      [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 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     List[int]	,  lowerCamelCase__  :     Optional[List[int]] = None	,  lowerCamelCase__  :     bool = False )   ->List[int]:



					'''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 None:
							return [1] + ([0] * len(lowerCamelCase__ )) + [1]
					return [1] + ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1]




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     List[int]	,  lowerCamelCase__  :     Optional[List[int]] = None )   ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      [self.sep_token_id]
					_UpperCAmelCase		:       List[str]								=      [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 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Optional[str] = None )   ->Tuple[str]:



					'''simple docstring'''







					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer." )

					if not os.path.isdir(lowerCamelCase__ ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
							return
					_UpperCAmelCase		:       str								=      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__ ):
							copyfile(self.vocab_file	,  lowerCamelCase__ )

					return (out_vocab_file,)







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase__    	=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase__    	=     '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Any )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      tempfile.mkdtemp()
					os.makedirs(os.path.join(self.diffusers_dir	,  "schedulers/" ) )
					_UpperCAmelCase		:       Optional[Any]								=      self.diffusers_dir
					shutil.copy(
					    os.path.join(lowerCamelCase__	,  "src/diffusers/schedulers/scheduling_ddpm.py" )	,  os.path.join(self.diffusers_dir	,  "schedulers/scheduling_ddpm.py" )	,  )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      "src/diffusers"
					shutil.rmtree(self.diffusers_dir )




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Any=None )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
					if overwrite_result is not None:
							_UpperCAmelCase		:       Tuple								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
					_UpperCAmelCase		:       Optional[Any]								=      black.Mode(target_versions={black.TargetVersion.PYaa}	,  line_length=1_19 )
					_UpperCAmelCase		:       Tuple								=      black.format_str(lowerCamelCase__	,  mode=lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      os.path.join(self.diffusers_dir	,  "new_code.py" )
					with open(lowerCamelCase__	,  "w"	,  newline="\n" ) as f:
							f.write(lowerCamelCase__ )
					if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase__ ) ) == 0 )
					else:
							check_copies.is_copy_consistent(f.name	,  overwrite=lowerCamelCase__ )
							with open(lowerCamelCase__	,  "r" ) as f:
									self.assertTrue(f.read()	,  lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     str )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
					self.assertEqual(lowerCamelCase__	,  lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Optional[int]:



					'''simple docstring'''







					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  REFERENCE_CODE + "\n"	,  )

					# With no empty line at the end
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  lowerCamelCase__	,  )

					# Copy consistency with rename
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )

					# Copy consistency with a really long name
					_UpperCAmelCase		:       int								=      "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
					self.check_copy_consistency(
					    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""	,  F"""{long_class_name}SchedulerOutput"""	,  re.sub("Bert"	,  lowerCamelCase__	,  lowerCamelCase__ )	,  )

					# Copy consistency with overwrite
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  lowerCamelCase__	,  overwrite_result=re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )



