






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a      :       Tuple				  =   logging.get_logger(__name__)

__a      :       Dict				  =   "▁"

__a      :       Union[str, Any]				  =   {"vocab_file": "sentencepiece.bpe.model"}

__a      :       str				  =   {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__a      :       str				  =   {
    "facebook/xglm-564M": 2048,
}


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE   						=				["input_ids", "attention_mask"]




     def __init__(				self       :			Any ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Optional[int]="<s>" ,	UpperCamelCase_       :			List[Any]="</s>" ,	UpperCamelCase_       :			Optional[Any]="</s>" ,	UpperCamelCase_       :			str="<s>" ,	UpperCamelCase_       :			Optional[int]="<unk>" ,	UpperCamelCase_       :			Dict="<pad>" ,	UpperCamelCase_       :			Optional[Dict[str, Any]] = None ,	**UpperCamelCase_       :			Optional[int] ,	):


         """simple docstring"""



         __A         =	{} if sp_model_kwargs is None else sp_model_kwargs

         # Compatibility with the original tokenizer
         __A         =	7
         __A         =	[F"<madeupword{i}>" for i in range(self.num_madeup_words				)]

         __A         =	kwargs.get("""additional_special_tokens""" ,	[]				)
         kwargs["additional_special_tokens"] += [
             word for word in madeup_words if word not in kwargs["additional_special_tokens"]
         ]

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

         __A         =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(str(UpperCamelCase_				)				)
         __A         =	vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         __A         =	1

         # Mimic fairseq token-to-id alignment for the first 4 token
         __A         =	{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

         __A         =	len(self.sp_model				)
         __A         =	{F"<madeupword{i}>": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words				)}
         self.fairseq_tokens_to_ids.update(UpperCamelCase_				)

         __A         =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}




     def __getstate__(				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	self.__dict__.copy()
         __A         =	None
         __A         =	self.sp_model.serialized_model_proto()
         return state




     def __setstate__(				self       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



         __A         =	d

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

         __A         =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			List[int] ,	UpperCamelCase_       :			Optional[List[int]] = None				):


         """simple docstring"""



         if token_ids_a is None:
             return [self.sep_token_id] + token_ids_a
         __A         =	[self.sep_token_id]
         return sep + token_ids_a + sep + sep + token_ids_a




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			List[int] ,	UpperCamelCase_       :			Optional[List[int]] = None ,	UpperCamelCase_       :			bool = False				):


         """simple docstring"""



         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 None:
             return [1] + ([0] * len(UpperCamelCase_				))
         return [1] + ([0] * len(UpperCamelCase_				)) + [1, 1] + ([0] * len(UpperCamelCase_				))




     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			List[int] ,	UpperCamelCase_       :			Optional[List[int]] = None				):


         """simple docstring"""



         __A         =	[self.sep_token_id]

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




     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return len(self.sp_model				) + self.fairseq_offset + self.num_madeup_words




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	{self.convert_ids_to_tokens(UpperCamelCase_				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab




     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         return self.sp_model.encode(UpperCamelCase_ ,	out_type=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         __A         =	self.sp_model.PieceToId(UpperCamelCase_				)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




     def 		lowerCAmelCase_     (				self       :			Optional[int] ,	UpperCamelCase_       :			int				):


         """simple docstring"""



         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset				)




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



         __A         =	"""""".join(UpperCamelCase_				).replace(UpperCamelCase_ ,	""" """				).strip()
         return out_string





     def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[str] = None				):


         """simple docstring"""



         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,)












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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any    )						->      Optional[int]:





    """simple docstring"""

    return EnvironmentCommand()






class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			ArgumentParser				):


         """simple docstring"""



         __A         =	parser.add_parser("""env"""				)
         download_parser.set_defaults(func=UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	huggingface_hub.__version__

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

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

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

             __A         =	transformers.__version__

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

             __A         =	accelerate.__version__

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

             __A         =	xformers.__version__

         __A         =	{
             """`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 		lowerCAmelCase_     (				UpperCamelCase_       :			str				):


         """simple docstring"""



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




