
'''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    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}

# fmt: off
UpperCAmelCase    					=   [
    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    					=   [
    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 						a   (				__lowercase    ):
				_snake_case		    = '''whisper'''
				_snake_case		    = ['''past_key_values''']
				_snake_case		    = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




				def __init__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				int=5_18_65,    SCREAMING_SNAKE_CASE_	:				str=80,    SCREAMING_SNAKE_CASE_	:				int=6,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=4,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=6,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=4,    SCREAMING_SNAKE_CASE_	:				str=15_36,    SCREAMING_SNAKE_CASE_	:				Any=15_36,    SCREAMING_SNAKE_CASE_	:				int=0.0,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=0.0,    SCREAMING_SNAKE_CASE_	:				Dict=5_02_57,    SCREAMING_SNAKE_CASE_	:				str=True,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=True,    SCREAMING_SNAKE_CASE_	:				List[Any]="gelu",    SCREAMING_SNAKE_CASE_	:				Optional[int]=2_56,    SCREAMING_SNAKE_CASE_	:				Tuple=0.0,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=0.0,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=0.0,    SCREAMING_SNAKE_CASE_	:				str=0.02,    SCREAMING_SNAKE_CASE_	:				Tuple=False,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=15_00,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=4_48,    SCREAMING_SNAKE_CASE_	:				List[str]=5_02_56,    SCREAMING_SNAKE_CASE_	:				int=5_02_56,    SCREAMING_SNAKE_CASE_	:				Tuple=5_02_56,    SCREAMING_SNAKE_CASE_	:				str=None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=[2_20, 5_02_56],    SCREAMING_SNAKE_CASE_	:				Optional[int]=False,    SCREAMING_SNAKE_CASE_	:				str=2_56,    SCREAMING_SNAKE_CASE_	:				str=False,    SCREAMING_SNAKE_CASE_	:				Tuple=0.05,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=10,    SCREAMING_SNAKE_CASE_	:				Tuple=2,    SCREAMING_SNAKE_CASE_	:				Optional[int]=0.0,    SCREAMING_SNAKE_CASE_	:				List[Any]=10,    SCREAMING_SNAKE_CASE_	:				Any=0,    SCREAMING_SNAKE_CASE_	:				Optional[int]=7,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										snake_case       :     Optional[int] =    vocab_size
										snake_case       :     List[str] =    num_mel_bins
										snake_case       :     Dict =    d_model
										snake_case       :     int =    encoder_layers
										snake_case       :     Union[str, Any] =    encoder_attention_heads
										snake_case       :     Any =    decoder_layers
										snake_case       :     Optional[Any] =    decoder_attention_heads
										snake_case       :     Dict =    decoder_ffn_dim
										snake_case       :     List[Any] =    encoder_ffn_dim
										snake_case       :     str =    dropout
										snake_case       :     Tuple =    attention_dropout
										snake_case       :     Tuple =    activation_dropout
										snake_case       :     List[Any] =    activation_function
										snake_case       :     List[str] =    init_std
										snake_case       :     Union[str, Any] =    encoder_layerdrop
										snake_case       :     List[Any] =    decoder_layerdrop
										snake_case       :     List[str] =    use_cache
										snake_case       :     int =    encoder_layers
										snake_case       :     Any =    scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case       :     Dict =    max_source_positions
										snake_case       :     Any =    max_target_positions

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

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										snake_case       :     Any =    apply_spec_augment
										snake_case       :     int =    mask_time_prob
										snake_case       :     Optional[Any] =    mask_time_length
										snake_case       :     Dict =    mask_time_min_masks
										snake_case       :     Any =    mask_feature_prob
										snake_case       :     Dict =    mask_feature_length
										snake_case       :     List[str] =    mask_feature_min_masks

										snake_case       :     int =    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 						a   (				__lowercase    ):




				@property
				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     Tuple =    OrderedDict(
										    [
										        ('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
										    ]      )
										if self.use_past:
																snake_case       :     List[str] =    {0: """batch"""}
										else:
																snake_case       :     Tuple =    {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	:				int,    SCREAMING_SNAKE_CASE_	:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],    SCREAMING_SNAKE_CASE_	:				int = -1,    SCREAMING_SNAKE_CASE_	:				int = -1,    SCREAMING_SNAKE_CASE_	:				bool = False,    SCREAMING_SNAKE_CASE_	:				Optional["TensorType"] = None,    SCREAMING_SNAKE_CASE_	:				int = 2_20_50,    SCREAMING_SNAKE_CASE_	:				float = 5.0,    SCREAMING_SNAKE_CASE_	:				int = 2_20,    ):
										snake_case       :     Dict =    OrderedDict()
										snake_case       :     List[str] =    OnnxConfig.generate_dummy_inputs(
										    self,    preprocessor=preprocessor.feature_extractor,    batch_size=__a,    framework=__a,    sampling_rate=__a,    time_duration=__a,    frequency=__a,    )
										snake_case       :     Any =    encoder_inputs["""input_features"""].shape[2]
										snake_case       :     str =    encoder_sequence_length // 2 if self.use_past else seq_length

										snake_case       :     List[str] =    super().generate_dummy_inputs(
										    preprocessor.tokenizer,    __a,    __a,    __a,    __a      )

										snake_case       :     Union[str, Any] =    encoder_inputs.pop('''input_features'''      )
										snake_case       :     List[Any] =    decoder_inputs.pop('''decoder_input_ids'''      )

										if "past_key_values" in decoder_inputs:
																snake_case       :     List[Any] =    decoder_inputs.pop('''past_key_values'''      )

										return dummy_inputs




				@property
				def 	__snake_case							(							self	:				List[str]      ):
										return 1e-3



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase    					=   {
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						UpperCAmelCase    					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

