





import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							from .features import FeatureType


UpperCAmelCase_				,			UpperCAmelCase_				,			UpperCAmelCase_							=				False, False, False
@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       Optional[int]       =	None
		__a       :       bool       =	True
		__a       :       bool       =	True
		__a       :       Optional[str]       =	None
		# Automatically constructed
		__a       :       ClassVar[str]       =	"dict"
		__a       :       ClassVar[Any]       =	pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}				)
		__a       :       str       =	field(default="""Audio"""				, init=lowerCamelCase__				, repr=lowerCamelCase__				)






		def __call__(							self  )       -> Any:






									"""simple docstring"""
									return self.pa_type






		def 						snake_case__				(							self,   snake_case__  )       -> dict:






									"""simple docstring"""
									try:
																import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
									except ImportError as err:
																raise ImportError("""To support encoding audio data, please install 'soundfile'."""  ) from err
									if isinstance(snake_case__,   snake_case__  ):
																return {"bytes": None, "path": value}
									elif isinstance(snake_case__,   snake_case__  ):
																return {"bytes": value, "path": None}
									elif "array" in value:
																# convert the audio array to wav bytes
																lowercase_	:   List[str]  				=     BytesIO()
																sf.write(snake_case__,   value["""array"""],   value["""sampling_rate"""],   format="""wav"""  )
																return {"bytes": buffer.getvalue(), "path": None}
									elif value.get("""path"""  ) is not None and os.path.isfile(value["""path"""]  ):
																# we set "bytes": None to not duplicate the data if they're already available locally
																if value["path"].endswith("""pcm"""  ):
																							# "PCM" only has raw audio bytes
																							if value.get("""sampling_rate"""  ) is None:
																														# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																														raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object"""  )
																							if value.get("""bytes"""  ):
																														# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																														lowercase_	:   List[Any]  				=     np.frombuffer(value["""bytes"""],   dtype=np.intaa  ).astype(np.floataa  ) / 3_27_67
																							else:
																														lowercase_	:   Optional[Any]  				=     np.memmap(value["""path"""],   dtype="""h""",   mode="""r"""  ).astype(np.floataa  ) / 3_27_67

																							lowercase_	:   Any  				=     BytesIO(bytes()  )
																							sf.write(snake_case__,   snake_case__,   value["""sampling_rate"""],   format="""wav"""  )
																							return {"bytes": buffer.getvalue(), "path": None}
																else:
																							return {"bytes": None, "path": value.get("""path"""  )}
									elif value.get("""bytes"""  ) is not None or value.get("""path"""  ) is not None:
																# store the audio bytes, and path is used to infer the audio format using the file extension
																return {"bytes": value.get("""bytes"""  ), "path": value.get("""path"""  )}
									else:
																raise ValueError(
																    f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> dict:






									"""simple docstring"""
									if not self.decode:
																raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""  )

									lowercase_				,		lowercase_	:   Union[str, Any]  				=     (value["""path"""], BytesIO(value["""bytes"""]  )) if value["""bytes"""] is not None else (value["""path"""], None)
									if path is None and file is None:
																raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""  )

									try:
																import librosa
																import soundfile as sf
									except ImportError as err:
																raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'."""  ) from err

									lowercase_	:   Dict  				=     xsplitext(snake_case__  )[1][1:].lower() if path is not None else None
									if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
																raise RuntimeError(
																    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
																    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """  )
									elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
																raise RuntimeError(
																    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
																    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """  )

									if file is None:
																lowercase_	:   int  				=     token_per_repo_id or {}
																lowercase_	:   List[Any]  				=     path.split("""::"""  )[-1]
																try:
																							lowercase_	:   int  				=     string_to_dict(snake_case__,   config.HUB_DATASETS_URL  )["""repo_id"""]
																							lowercase_	:   Dict  				=     token_per_repo_id[repo_id]
																except (ValueError, KeyError):
																							lowercase_	:   Optional[Any]  				=     None

																with xopen(snake_case__,   """rb""",   use_auth_token=snake_case__  ) as f:
																							lowercase_				,		lowercase_	:   Optional[int]  				=     sf.read(snake_case__  )

									else:
																lowercase_				,		lowercase_	:   Union[str, Any]  				=     sf.read(snake_case__  )

									lowercase_	:   Dict  				=     array.T
									if self.mono:
																lowercase_	:   List[Any]  				=     librosa.to_mono(snake_case__  )
									if self.sampling_rate and self.sampling_rate != sampling_rate:
																lowercase_	:   Optional[Any]  				=     librosa.resample(snake_case__,   orig_sr=snake_case__,   target_sr=self.sampling_rate  )
																lowercase_	:   Dict  				=     self.sampling_rate

									return {"path": path, "array": array, "sampling_rate": sampling_rate}






		def 						snake_case__				(							self  )       -> Union["FeatureType", Dict[str, "FeatureType"]]:






									"""simple docstring"""
									from .features import Value

									if self.decode:
																raise ValueError("""Cannot flatten a decoded Audio feature."""  )
									return {
									    "bytes": Value("""binary"""  ),
									    "path": Value("""string"""  ),
									}






		def 						snake_case__				(							self,   snake_case__  )       -> pa.StructArray:






									"""simple docstring"""
									if pa.types.is_string(storage.type  ):
																lowercase_	:   Optional[Any]  				=     pa.array([None] * len(snake_case__  ),   type=pa.binary()  )
																lowercase_	:   List[str]  				=     pa.StructArray.from_arrays([bytes_array, storage],   ["""bytes""", """path"""],   mask=storage.is_null()  )
									elif pa.types.is_binary(storage.type  ):
																lowercase_	:   Union[str, Any]  				=     pa.array([None] * len(snake_case__  ),   type=pa.string()  )
																lowercase_	:   Union[str, Any]  				=     pa.StructArray.from_arrays([storage, path_array],   ["""bytes""", """path"""],   mask=storage.is_null()  )
									elif pa.types.is_struct(storage.type  ) and storage.type.get_all_field_indices("""array"""  ):
																lowercase_	:   int  				=     pa.array([Audio().encode_example(snake_case__  ) if x is not None else None for x in storage.to_pylist()]  )
									elif pa.types.is_struct(storage.type  ):
																if storage.type.get_field_index("""bytes"""  ) >= 0:
																							lowercase_	:   str  				=     storage.field("""bytes"""  )
																else:
																							lowercase_	:   int  				=     pa.array([None] * len(snake_case__  ),   type=pa.binary()  )
																if storage.type.get_field_index("""path"""  ) >= 0:
																							lowercase_	:   Tuple  				=     storage.field("""path"""  )
																else:
																							lowercase_	:   Optional[int]  				=     pa.array([None] * len(snake_case__  ),   type=pa.string()  )
																lowercase_	:   Any  				=     pa.StructArray.from_arrays([bytes_array, path_array],   ["""bytes""", """path"""],   mask=storage.is_null()  )
									return array_cast(snake_case__,   self.pa_type  )






		def 						snake_case__				(							self,   snake_case__  )       -> pa.StructArray:






									"""simple docstring"""

									@no_op_if_value_is_null
									def path_to_bytes(snake_case__  ):
																with xopen(snake_case__,   """rb"""  ) as f:
																							lowercase_	:   Optional[int]  				=     f.read()
																return bytes_

									lowercase_	:   Tuple  				=     pa.array(
									    [
									        (path_to_bytes(x["""path"""]  ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
									        for x in storage.to_pylist()
									    ],   type=pa.binary(),   )
									lowercase_	:   Optional[Any]  				=     pa.array(
									    [os.path.basename(snake_case__  ) if path is not None else None for path in storage.field("""path"""  ).to_pylist()],   type=pa.string(),   )
									lowercase_	:   Any  				=     pa.StructArray.from_arrays([bytes_array, path_array],   ["""bytes""", """path"""],   mask=bytes_array.is_null()  )
									return array_cast(snake_case__,   self.pa_type  )






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__  )}  )