


"""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

A     :   str			    =			logging.get_logger(__name__)

A     :   str			    =			{
    "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json",
}

# fmt: off
A     :   Tuple			    =			[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
    7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
    1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
    4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
    1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
    1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
    3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
A     :   Optional[int]			    =			[
    1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
    2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
    6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
    8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
    3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
    7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
    1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
    2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
    4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Dict				      ="""whisper"""
							__UpperCAmelCase						:    str				      =["""past_key_values"""]
							__UpperCAmelCase						:    List[str]				      ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

							def __init__(     self    ,   __a=5_18_65    ,   __a=80    ,   __a=6    ,   __a=4    ,   __a=6    ,   __a=4    ,   __a=15_36    ,   __a=15_36    ,   __a=0.0    ,   __a=0.0    ,   __a=5_02_57    ,   __a=True    ,   __a=True    ,   __a="gelu"    ,   __a=2_56    ,   __a=0.0    ,   __a=0.0    ,   __a=0.0    ,   __a=0.0_2    ,   __a=False    ,   __a=15_00    ,   __a=4_48    ,   __a=5_02_56    ,   __a=5_02_56    ,   __a=5_02_56    ,   __a=None    ,   __a=[2_20, 5_02_56]    ,   __a=False    ,   __a=2_56    ,   __a=False    ,   __a=0.0_5    ,   __a=10    ,   __a=2    ,   __a=0.0    ,   __a=10    ,   __a=0    ,   __a=7    ,   **__a    ,   ):
										__lowerCAmelCase				    =			vocab_size
										__lowerCAmelCase				    =			num_mel_bins
										__lowerCAmelCase				    =			d_model
										__lowerCAmelCase				    =			encoder_layers
										__lowerCAmelCase				    =			encoder_attention_heads
										__lowerCAmelCase				    =			decoder_layers
										__lowerCAmelCase				    =			decoder_attention_heads
										__lowerCAmelCase				    =			decoder_ffn_dim
										__lowerCAmelCase				    =			encoder_ffn_dim
										__lowerCAmelCase				    =			dropout
										__lowerCAmelCase				    =			attention_dropout
										__lowerCAmelCase				    =			activation_dropout
										__lowerCAmelCase				    =			activation_function
										__lowerCAmelCase				    =			init_std
										__lowerCAmelCase				    =			encoder_layerdrop
										__lowerCAmelCase				    =			decoder_layerdrop
										__lowerCAmelCase				    =			use_cache
										__lowerCAmelCase				    =			encoder_layers
										__lowerCAmelCase				    =			scale_embedding  # scale factor will be sqrt(d_model) if True
										__lowerCAmelCase				    =			max_source_positions
										__lowerCAmelCase				    =			max_target_positions

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

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

										__lowerCAmelCase				    =			median_filter_width

										super().__init__(
										    pad_token_id=__a    ,   bos_token_id=__a    ,   eos_token_id=__a    ,   is_encoder_decoder=__a    ,   decoder_start_token_id=__a    ,   suppress_tokens=__a    ,   begin_suppress_tokens=__a    ,   **__a    ,   )
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			OrderedDict(
										    [
										        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
										    ]	)
										if self.use_past:
													__lowerCAmelCase				    =			{0: "batch"}
										else:
													__lowerCAmelCase				    =			{0: "batch", 1: "decoder_sequence"}

										if self.use_past:
													self.fill_with_past_key_values_(__a    ,   direction="inputs"	)

										return common_inputs

							def 			snake_case				(     self    ,   __a    ,   __a = -1    ,   __a = -1    ,   __a = False    ,   __a = None    ,   __a = 2_20_50    ,   __a = 5.0    ,   __a = 2_20    ,   ):
										__lowerCAmelCase				    =			OrderedDict()
										__lowerCAmelCase				    =			OnnxConfig.generate_dummy_inputs(
										    self    ,   preprocessor=preprocessor.feature_extractor    ,   batch_size=__a    ,   framework=__a    ,   sampling_rate=__a    ,   time_duration=__a    ,   frequency=__a    ,   )
										__lowerCAmelCase				    =			encoder_inputs["input_features"].shape[2]
										__lowerCAmelCase				    =			encoder_sequence_length // 2 if self.use_past else seq_length

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

										__lowerCAmelCase				    =			encoder_inputs.pop("input_features"	)
										__lowerCAmelCase				    =			decoder_inputs.pop("decoder_input_ids"	)

										if "past_key_values" in decoder_inputs:
													__lowerCAmelCase				    =			decoder_inputs.pop("past_key_values"	)

										return dummy_inputs



							@property
							def 			snake_case				(     self	):
										return 1e-3








"""simple docstring"""


A     :   int			    =			{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}




