
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__	=							{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase__	=							{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Tuple	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Optional[int]	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: str	=		["input_ids", "attention_mask"]
						UpperCamelCase_							: Optional[int]	=		RobertaTokenizer

						def __init__(			self       ,							a=None       ,							a=None       ,							a=None       ,							a="replace"       ,							a="<s>"       ,							a="</s>"       ,							a="</s>"       ,							a="<s>"       ,							a="<unk>"       ,							a="<pad>"       ,							a="<mask>"       ,							a=False       ,							a=True       ,							**a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__(
								    a       ,							a       ,							tokenizer_file=a       ,							errors=a       ,							bos_token=a       ,							eos_token=a       ,							sep_token=a       ,							cls_token=a       ,							unk_token=a       ,							pad_token=a       ,							mask_token=a       ,							add_prefix_space=a       ,							trim_offsets=a       ,							**a       ,							)

								_UpperCamelCase						    =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
								if pre_tok_state.get("""add_prefix_space"""       ,							a				) != add_prefix_space:
										_UpperCamelCase						    =	getattr(a       ,							pre_tok_state.pop("""type"""				)				)
										_UpperCamelCase						    =	add_prefix_space
										_UpperCamelCase						    =	pre_tok_class(**a				)

								_UpperCamelCase						    =	add_prefix_space

								_UpperCamelCase						    =	"""post_processor"""
								_UpperCamelCase						    =	getattr(self.backend_tokenizer       ,							a       ,							a				)
								if tokenizer_component_instance:
										_UpperCamelCase						    =	json.loads(tokenizer_component_instance.__getstate__()				)

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
												_UpperCamelCase						    =	tuple(state["""sep"""]				)
										if "cls" in state:
												_UpperCamelCase						    =	tuple(state["""cls"""]				)

										_UpperCamelCase						    =	False

										if state.get("""add_prefix_space"""       ,							a				) != add_prefix_space:
												_UpperCamelCase						    =	add_prefix_space
												_UpperCamelCase						    =	True

										if state.get("""trim_offsets"""       ,							a				) != trim_offsets:
												_UpperCamelCase						    =	trim_offsets
												_UpperCamelCase						    =	True

										if changes_to_apply:
												_UpperCamelCase						    =	getattr(a       ,							state.pop("""type"""				)				)
												_UpperCamelCase						    =	component_class(**a				)
												setattr(self.backend_tokenizer       ,							a       ,							a				)

						@property
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								if self._mask_token is None:
										if self.verbose:
												logger.error("""Using mask_token, but it is not set yet."""				)
										return None
								return str(self._mask_token				)

						@mask_token.setter
						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	AddedToken(a       ,							lstrip=a       ,							rstrip=a				) if isinstance(a       ,							a				) else value
								_UpperCamelCase						    =	value

						def  A_				(			self       ,							*a       ,							**a				)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	kwargs.get("""is_split_into_words"""       ,							a				)
								assert self.add_prefix_space or not is_split_into_words, (
								    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*a       ,							**a				)

						def  A_				(			self       ,							*a       ,							**a				)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	kwargs.get("""is_split_into_words"""       ,							a				)

								assert self.add_prefix_space or not is_split_into_words, (
								    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*a       ,							**a				)

						def  A_				(			self       ,							a       ,							a = None				)					->      Tuple[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._tokenizer.model.save(a       ,							name=a				)
								return tuple(a				)

						def  A_				(			self       ,							a       ,							a=None				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
										return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







						def  A_				(			self       ,							a       ,							a = None				)					->      List[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.sep_token_id]
								_UpperCamelCase						    =	[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 + sep + token_ids_a + sep				) * [0]





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 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		return EnvironmentCommand()







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser("""env"""				)
								download_parser.set_defaults(func=a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	huggingface_hub.__version__

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

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

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

										_UpperCamelCase						    =	transformers.__version__

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

										_UpperCamelCase						    =	accelerate.__version__

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

										_UpperCamelCase						    =	xformers.__version__

								_UpperCamelCase						    =	{
								    """`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(a				)				)

								return info







						@staticmethod
						def  A_				(			a				)					->      Dict:


								'''simple docstring'''

								return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]				) + "\n"



