


"""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:
		a__       :							int        =  None

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

a__       :							Tuple        =  {
    '''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'''
        ),
    },
}

a__       :							int        =  {
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}


a__       :							Any        =  '''▁'''




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Optional[Any]					      =    VOCAB_FILES_NAMES
	snake_case__       :						Any					      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__       :						int					      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__       :						Tuple					      =    BigBirdTokenizer
	snake_case__       :						Union[str, Any]					      =    ["input_ids", "attention_mask"]
	snake_case__       :						List[int]					      =    []




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

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

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

		__SCREAMING_SNAKE_CASE		     =       vocab_file
		__SCREAMING_SNAKE_CASE		     =       False if not self.vocab_file else True




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None     ) ->			List[int]:
		__SCREAMING_SNAKE_CASE		     =       [self.sep_token_id]
		__SCREAMING_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 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None    ,    UpperCAmelCase__    :       bool = False     ) ->			List[int]:

		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(UpperCAmelCase__     )) + [1]
		return [1] + ([0] * len(UpperCAmelCase__     )) + [1] + ([0] * len(UpperCAmelCase__     )) + [1]




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       List[int]    ,    UpperCAmelCase__    :       Optional[List[int]] = None     ) ->			List[int]:
		__SCREAMING_SNAKE_CASE		     =       [self.sep_token_id]
		__SCREAMING_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 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Optional[str] = None     ) ->			Tuple[str]:
		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(UpperCAmelCase__     ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
			return
		__SCREAMING_SNAKE_CASE		     =       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__     ):
			copyfile(self.vocab_file    ,    UpperCAmelCase__     )

		return (out_vocab_file,)




"""simple docstring"""







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return EnvironmentCommand()




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       parser.add_parser("env"     )
						download_parser.set_defaults(func=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       huggingface_hub.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						__SCREAMING_SNAKE_CASE		     =       "NA"
						if is_torch_available():
											import torch

											__SCREAMING_SNAKE_CASE		     =       torch.__version__
											__SCREAMING_SNAKE_CASE		     =       torch.cuda.is_available()

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_transformers_available():
											import transformers

											__SCREAMING_SNAKE_CASE		     =       transformers.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_accelerate_available():
											import accelerate

											__SCREAMING_SNAKE_CASE		     =       accelerate.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_xformers_available():
											import xformers

											__SCREAMING_SNAKE_CASE		     =       xformers.__version__

						__SCREAMING_SNAKE_CASE		     =       {
						    "`diffusers` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
						    "Huggingface_hub version": hub_version,
						    "Transformers version": transformers_version,
						    "Accelerate version": accelerate_version,
						    "xFormers version": xformers_version,
						    "Using GPU in script?": "<fill in>",
						    "Using distributed or parallel set-up in script?": "<fill in>",
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"     )
						print(self.format_dict(UpperCAmelCase__     )     )

						return info




	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"
