






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[Any]    		=       logging.get_logger(__name__)

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

# fmt: off
UpperCamelCase__    :   List[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, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
    1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
    4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
    11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
    17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
    34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
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, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1_350, 1_853, 1_982, 2_460, 2_627,
    3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
    7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
    14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
    22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
    42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]







class 					lowerCamelCase_  (						a_  ):
							SCREAMING_SNAKE_CASE_      =      'whisper'
							SCREAMING_SNAKE_CASE_      =      ['past_key_values']
							SCREAMING_SNAKE_CASE_      =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
							def __init__(    self		:       Dict				,__lowerCamelCase		:       Union[str, Any]=5_18_65				,__lowerCamelCase		:       Optional[Any]=80				,__lowerCamelCase		:       Dict=6				,__lowerCamelCase		:       Any=4				,__lowerCamelCase		:       Dict=6				,__lowerCamelCase		:       Union[str, Any]=4				,__lowerCamelCase		:       List[Any]=15_36				,__lowerCamelCase		:       List[Any]=15_36				,__lowerCamelCase		:       Optional[int]=0.0				,__lowerCamelCase		:       Optional[int]=0.0				,__lowerCamelCase		:       Optional[int]=5_02_57				,__lowerCamelCase		:       int=True				,__lowerCamelCase		:       Union[str, Any]=True				,__lowerCamelCase		:       List[str]="gelu"				,__lowerCamelCase		:       str=2_56				,__lowerCamelCase		:       Union[str, Any]=0.0				,__lowerCamelCase		:       Dict=0.0				,__lowerCamelCase		:       Any=0.0				,__lowerCamelCase		:       Union[str, Any]=0.02				,__lowerCamelCase		:       Any=False				,__lowerCamelCase		:       Union[str, Any]=15_00				,__lowerCamelCase		:       Optional[Any]=4_48				,__lowerCamelCase		:       Optional[Any]=5_02_56				,__lowerCamelCase		:       List[Any]=5_02_56				,__lowerCamelCase		:       Union[str, Any]=5_02_56				,__lowerCamelCase		:       List[Any]=None				,__lowerCamelCase		:       Dict=[2_20, 5_02_56]				,__lowerCamelCase		:       List[str]=False				,__lowerCamelCase		:       List[str]=2_56				,__lowerCamelCase		:       Optional[int]=False				,__lowerCamelCase		:       Union[str, Any]=0.05				,__lowerCamelCase		:       Tuple=10				,__lowerCamelCase		:       Tuple=2				,__lowerCamelCase		:       Tuple=0.0				,__lowerCamelCase		:       Dict=10				,__lowerCamelCase		:       Dict=0				,__lowerCamelCase		:       Optional[Any]=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 					lowerCamelCase_  (						a_  ):
							@property
							def 			SCREAMING_SNAKE_CASE_			(    self		:       Union[str, Any]       ):





								'''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 			SCREAMING_SNAKE_CASE_			(    self		:       Optional[Any]				,__lowerCamelCase		:       Union[str, Any]				,__lowerCamelCase		:       Optional[int] = -1				,__lowerCamelCase		:       Union[str, Any] = -1				,__lowerCamelCase		:       List[Any] = False				,__lowerCamelCase		:       Optional[Any] = None				,__lowerCamelCase		:       str = 2_20_50				,__lowerCamelCase		:       Optional[int] = 5.0				,__lowerCamelCase		:       int = 2_20				,):





								'''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 			SCREAMING_SNAKE_CASE_			(    self		:       List[Any]       ):





								'''simple docstring'''



								return 1e-3






"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  List[Any]				    =      'philschmid/bart-large-cnn-samsum'
      snake_case__     :  int				    =      (
          'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
          'and returns a summary of the text.'
      )
      snake_case__     :  Tuple				    =      'summarizer'
      snake_case__     :  int				    =      AutoTokenizer
      snake_case__     :  Any				    =      AutoModelForSeqaSeqLM

      snake_case__     :  Optional[int]				    =      ['text']
      snake_case__     :  Optional[int]				    =      ['text']

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor(__lowerCAmelCase						,			return_tensors="""pt"""						,			truncation=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.model.generate(**__lowerCAmelCase		)[0]







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.pre_processor.decode(__lowerCAmelCase						,			skip_special_tokens=__lowerCAmelCase						,			clean_up_tokenization_spaces=__lowerCAmelCase		)



