




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

						SCREAMING_SNAKE_CASE:     int									= True
except (ImportError, AttributeError):
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= object


						def 					_a							(					*lowerCAmelCase    ,						**lowerCAmelCase )->	Dict:
								pass




						SCREAMING_SNAKE_CASE:     Tuple									= False


SCREAMING_SNAKE_CASE:     Any									= logging.get_logger('''transformers-cli/serving''')


def 					_a							(					lowerCAmelCase )->	Optional[int]:
		SCREAMING_SNAKE_CASE_					      =							pipeline(
		    task=args.task    ,						model=args.model if args.model else None    ,						config=args.config    ,						tokenizer=args.tokenizer    ,						device=args.device    ,						)
		return ServeCommand(SCREAMING_SNAKE_CASE__    ,						args.host    ,						args.port    ,						args.workers )



class    lowercase_		(_lowerCAmelCase ):
						lowerCAmelCase__			      =42



class    lowercase_		(_lowerCAmelCase ):
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =42



class    lowercase_		(_lowerCAmelCase ):
						lowerCAmelCase__			      =42



class    lowercase_		(_lowerCAmelCase ):
						lowerCAmelCase__			      =42



class    lowercase_		(_lowerCAmelCase ):



						@staticmethod
						def 	__a					(			snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							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	:   Tuple					,							snake_case__	:   Any					,							snake_case__	:   Optional[Any]					,							snake_case__	:   Dict					,							snake_case__	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							pipeline

								SCREAMING_SNAKE_CASE_					      =							host
								SCREAMING_SNAKE_CASE_					      =							port
								SCREAMING_SNAKE_CASE_					      =							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}'''  )
										SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								run(self._app					,							host=self.host					,							port=self.port					,							workers=self.workers  )



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Tuple					,							snake_case__	:   Tuple = Body(snake_case__					,							embed=snake_case__  )					,							snake_case__	:   Dict = Body(snake_case__					,							embed=snake_case__  )  ):

								"""simple docstring"""

								try:
										SCREAMING_SNAKE_CASE_					      =							self._pipeline.tokenizer.tokenize(snake_case__  )

										if return_ids:
												SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   List[Any]					,							snake_case__	:   Any = Body(snake_case__					,							embed=snake_case__  )					,							snake_case__	:   int = Body(snake_case__					,							embed=snake_case__  )					,							snake_case__	:   int = Body(snake_case__					,							embed=snake_case__  )					,							):

								"""simple docstring"""

								try:
										SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   List[Any]					,							snake_case__	:   int=Body(snake_case__					,							embed=snake_case__  )  ):

								"""simple docstring"""

								if len(snake_case__  ) == 0:
										return ServeForwardResult(output=[]					,							attention=[]  )

								try:
										# Forward through the model
										SCREAMING_SNAKE_CASE_					      =							self._pipeline(snake_case__  )
										return ServeForwardResult(output=snake_case__  )
								except Exception as e:
										raise HTTPException(5_00					,							{'error': str(snake_case__  )}  )







'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
 from ...feature_extraction_utils import FeatureExtractionMixin
 from ...tokenization_utils_base import PreTrainedTokenizerBase
 from ...utils import TensorType

__UpperCamelCase   :							List[str]         =   logging.get_logger(__name__)

__UpperCamelCase   :							Optional[int]         =   {
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
__UpperCamelCase   :							Optional[Any]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
__UpperCamelCase   :							List[str]         =   [
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="whisper"
      __a	       =["past_key_values"]
      __a	       ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




      def __init__(	self			, lowerCamelCase=5_1865			, lowerCamelCase=80			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=6			, lowerCamelCase=4			, lowerCamelCase=1536			, lowerCamelCase=1536			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=5_0257			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase="gelu"			, lowerCamelCase=256			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.02			, lowerCamelCase=False			, lowerCamelCase=1500			, lowerCamelCase=448			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=None			, lowerCamelCase=[220, 5_0256]			, lowerCamelCase=False			, lowerCamelCase=256			, lowerCamelCase=False			, lowerCamelCase=0.05			, lowerCamelCase=10			, lowerCamelCase=2			, lowerCamelCase=0.0			, lowerCamelCase=10			, lowerCamelCase=0			, lowerCamelCase=7			, **lowerCamelCase			, )				->Any:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     num_mel_bins
         __a					  =     d_model
         __a					  =     encoder_layers
         __a					  =     encoder_attention_heads
         __a					  =     decoder_layers
         __a					  =     decoder_attention_heads
         __a					  =     decoder_ffn_dim
         __a					  =     encoder_ffn_dim
         __a					  =     dropout
         __a					  =     attention_dropout
         __a					  =     activation_dropout
         __a					  =     activation_function
         __a					  =     init_std
         __a					  =     encoder_layerdrop
         __a					  =     decoder_layerdrop
         __a					  =     use_cache
         __a					  =     encoder_layers
         __a					  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         __a					  =     max_source_positions
         __a					  =     max_target_positions

         # Audio Classification-specific parameters. Feel free to ignore for other classes.
         __a					  =     classifier_proj_size
         __a					  =     use_weighted_layer_sum

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __a					  =     apply_spec_augment
         __a					  =     mask_time_prob
         __a					  =     mask_time_length
         __a					  =     mask_time_min_masks
         __a					  =     mask_feature_prob
         __a					  =     mask_feature_length
         __a					  =     mask_feature_min_masks

         __a					  =     median_filter_width

         super().__init__(
             pad_token_id=lowerCamelCase			, bos_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, is_encoder_decoder=lowerCamelCase			, decoder_start_token_id=lowerCamelCase			, suppress_tokens=lowerCamelCase			, begin_suppress_tokens=lowerCamelCase			, **lowerCamelCase			, )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      @property
      def 			__UpperCamelCase				(	self   )				->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a					  =     OrderedDict(
             [
                 ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
             ]   )
         if self.use_past:
            __a					  =     {0: 'batch'}
         else:
            __a					  =     {0: 'batch', 1: 'decoder_sequence'}

         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase			, direction='inputs'   )

         return common_inputs




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = -1			, lowerCamelCase = -1			, lowerCamelCase = False			, lowerCamelCase = None			, lowerCamelCase = 2_2050			, lowerCamelCase = 5.0			, lowerCamelCase = 220			, )				->Mapping[str, Any]:



         '''simple docstring'''
         __a					  =     OrderedDict()
         __a					  =     OnnxConfig.generate_dummy_inputs(
             self			, preprocessor=preprocessor.feature_extractor			, batch_size=lowerCamelCase			, framework=lowerCamelCase			, sampling_rate=lowerCamelCase			, time_duration=lowerCamelCase			, frequency=lowerCamelCase			, )
         __a					  =     encoder_inputs['input_features'].shape[2]
         __a					  =     encoder_sequence_length // 2 if self.use_past else seq_length

         __a					  =     super().generate_dummy_inputs(
             preprocessor.tokenizer			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )

         __a					  =     encoder_inputs.pop('input_features'   )
         __a					  =     decoder_inputs.pop('decoder_input_ids'   )

         if "past_key_values" in decoder_inputs:
            __a					  =     decoder_inputs.pop('past_key_values'   )

         return dummy_inputs






      @property
      def 			__UpperCamelCase				(	self   )				->float:



         '''simple docstring'''
         return 1e-3