
'''simple docstring'''
import os
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


lowercase__											=logging.get_logger(__name__)

lowercase__											={'vocab_file': 'sentencepiece.bpe.model'}

lowercase__											={
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowercase__											={
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

lowercase__											='▁'







class   a_ (      lowerCamelCase__					):
			lowerCamelCase__	:			Any       =    VOCAB_FILES_NAMES
			lowerCamelCase__	:			Optional[int]       =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__	:			str       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__	:			Any       =    ["""input_ids""", """attention_mask"""]




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase="<s>"					,   UpperCAmelCase="</s>"					,   UpperCAmelCase="</s>"					,   UpperCAmelCase="<s>"					,   UpperCAmelCase="<unk>"					,   UpperCAmelCase="<pad>"					,   UpperCAmelCase="<mask>"					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				a_      				=						AddedToken(snake_case__					,   lstrip=snake_case__					,   rstrip=snake_case__	) if isinstance(snake_case__					,   snake_case__	) else mask_token

				a_      				=						{} if sp_model_kwargs is None else sp_model_kwargs

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

				a_      				=						vocab_file
				a_      				=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(str(snake_case__	)	)

				a_      				=						{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

				a_      				=						len(self.sp_model	) - 1
				a_      				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None	):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				a_      				=						[self.cls_token_id]
				a_      				=						[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = False	):
				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=snake_case__					,   token_ids_a=snake_case__					,   already_has_special_tokens=snake_case__	)

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




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None	):
				a_      				=						[self.sep_token_id]
				a_      				=						[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]




			@property
			def 					lowerCAmelCase__					(       self	):
				return len(self.sp_model	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						{self.convert_ids_to_tokens(snake_case__	): i for i in range(self.vocab_size	)}
				vocab.update(self.added_tokens_encoder	)
				return vocab




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				return self.sp_model.encode(snake_case__					,   out_type=snake_case__	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
				a_      				=						self.sp_model.PieceToId(snake_case__	)

				return spm_id if spm_id else self.unk_token_id




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(snake_case__	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						[]
				a_      				=						""""""
				a_      				=						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(snake_case__	) + token
						a_      				=						True
						a_      				=						[]
					else:
						current_sub_tokens.append(snake_case__	)
						a_      				=						False
				out_string += self.sp_model.decode(snake_case__	)
				return out_string.strip()




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




			def __setstate__(       self					,   UpperCAmelCase	):
				a_      				=						d

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

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






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None	):
				if not os.path.isdir(snake_case__	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
				a_      				=						os.path.join(
				    snake_case__					,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

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

				return (out_vocab_file,)







from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
							from fastapi import Body, FastAPI, HTTPException
							from fastapi.routing import APIRoute
							from pydantic import BaseModel
							from starlette.responses import JSONResponse
							from uvicorn import run

							UpperCAmelCase_							=				True
except (ImportError, AttributeError):
							UpperCAmelCase_							=				object





							def  __magic_name__  (				*lowercase				,       **lowercase				)				-> Optional[Any]:

														"""simple docstring"""



														pass




							UpperCAmelCase_							=				False


UpperCAmelCase_							=				logging.get_logger("""transformers-cli/serving""")





def  __magic_name__  (				lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   Any  				=     pipeline(
							    task=args.task				,       model=args.model if args.model else None				,       config=args.config				,       tokenizer=args.tokenizer				,       device=args.device				,       )
							return ServeCommand(lowercase				,       args.host				,       args.port				,       args.workers				)
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       dict




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[str]
		__a       :       Optional[List[int]]




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       str




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Any




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		@staticmethod
		def 						snake_case__				(							snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     parser.add_parser(
									    """serve""",   help="""CLI tool to run inference requests through REST and GraphQL endpoints."""  )
									serve_parser.add_argument(
									    """--task""",   type=snake_case__,   choices=get_supported_tasks(),   help="""The task to run the pipeline on""",   )
									serve_parser.add_argument("""--host""",   type=snake_case__,   default="""localhost""",   help="""Interface the server will listen on."""  )
									serve_parser.add_argument("""--port""",   type=snake_case__,   default=88_88,   help="""Port the serving will listen to."""  )
									serve_parser.add_argument("""--workers""",   type=snake_case__,   default=1,   help="""Number of http workers"""  )
									serve_parser.add_argument("""--model""",   type=snake_case__,   help="""Model's name or path to stored model."""  )
									serve_parser.add_argument("""--config""",   type=snake_case__,   help="""Model's config name or path to stored model."""  )
									serve_parser.add_argument("""--tokenizer""",   type=snake_case__,   help="""Tokenizer name to use."""  )
									serve_parser.add_argument(
									    """--device""",   type=snake_case__,   default=-1,   help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""",   )
									serve_parser.set_defaults(func=snake_case__  )






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   str  				=     pipeline

									lowercase_	:   List[str]  				=     host
									lowercase_	:   int  				=     port
									lowercase_	:   Optional[Any]  				=     workers

									if not _serve_dependencies_installed:
																raise RuntimeError(
																    """Using serve command requires FastAPI and uvicorn. """
																    """Please install transformers with [serving]: pip install \"transformers[serving]\"."""
																    """Or install FastAPI and uvicorn separately."""  )
									else:
																logger.info(f"""Serving model over {host}:{port}"""  )
																lowercase_	:   Union[str, Any]  				=     FastAPI(
																    routes=[
																        APIRoute(
																            """/""",   self.model_info,   response_model=snake_case__,   response_class=snake_case__,   methods=["""GET"""],   ),
																        APIRoute(
																            """/tokenize""",   self.tokenize,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																        APIRoute(
																            """/detokenize""",   self.detokenize,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																        APIRoute(
																            """/forward""",   self.forward,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																    ],   timeout=6_00,   )






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






									"""simple docstring"""
									run(self._app,   host=self.host,   port=self.port,   workers=self.workers  )






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )






		def 						snake_case__				(							self,   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  )  )       -> Optional[int]:






									"""simple docstring"""
									try:
																lowercase_	:   Tuple  				=     self._pipeline.tokenizer.tokenize(snake_case__  )

																if return_ids:
																							lowercase_	:   Union[str, Any]  				=     self._pipeline.tokenizer.convert_tokens_to_ids(snake_case__  )
																							return ServeTokenizeResult(tokens=snake_case__,   tokens_ids=snake_case__  )
																else:
																							return ServeTokenizeResult(tokens=snake_case__  )

									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(snake_case__  )}  )






		def 						snake_case__				(							self,   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   )       -> Dict:






									"""simple docstring"""
									try:
																lowercase_	:   List[Any]  				=     self._pipeline.tokenizer.decode(snake_case__,   snake_case__,   snake_case__  )
																return ServeDeTokenizeResult(model="""""",   text=snake_case__  )
									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(snake_case__  )}  )






		async def 						snake_case__				(							self,   snake_case__=Body(snake_case__,   embed=snake_case__  )  )       -> Tuple:






									"""simple docstring"""

									# Check we don't have empty string
									if len(snake_case__  ) == 0:
																return ServeForwardResult(output=[],   attention=[]  )

									try:
																# Forward through the model
																lowercase_	:   Optional[int]  				=     self._pipeline(snake_case__  )
																return ServeForwardResult(output=snake_case__  )
									except Exception as e:
																raise HTTPException(5_00,   {"""error""": str(snake_case__  )}  )