



from __future__ import annotations

from typing import TypedDict
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str
				UpperCamelCase__			:   int



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)

			return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE_					)					)]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)
			if not s:
						raise ValueError("The parameter s must not be empty."					)

			lowercase__   					=			all_rotations(SCREAMING_SNAKE_CASE_					)
			rotations.sort()  # sort the list of rotations in alphabetically order
			# make a string composed of the last char of each rotation
			lowercase__   					=			{
			    "bwt_string": "".join([word[-1] for word in rotations]					),
			    "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE_					),
			}
			return response



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter bwt_string type must be str."					)
			if not bwt_string:
						raise ValueError("The parameter bwt_string must not be empty."					)
			try:
						lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			except ValueError:
						raise TypeError(
						    "The parameter idx_original_string type must be int or passive"
						    " of cast to int."					)
			if idx_original_string < 0:
						raise ValueError("The parameter idx_original_string must not be lower than 0."					)
			if idx_original_string >= len(SCREAMING_SNAKE_CASE_					):
						raise ValueError(
						    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

			lowercase__   					=			[""] * len(SCREAMING_SNAKE_CASE_					)
			for _ in range(len(SCREAMING_SNAKE_CASE_					)					):
						for i in range(len(SCREAMING_SNAKE_CASE_					)					):
									lowercase__   					=			bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
							lowercase_						=       """Provide a string that I will generate its BWT transform: """
							lowercase_						=       input(entry_msg).strip()
							lowercase_						=       bwt_transform(s)
							print(
							    F'Burrows Wheeler transform for string \'{s}\' results '
							    F'in \'{result["bwt_string"]}\''
							)
							lowercase_						=       reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
							print(
							    F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' '
							    F'we get original string \'{original_string}\''
							)









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

lowercase_						=       logging.get_logger(__name__)

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

# fmt: off
lowercase_						=       [
    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, 1_0563, 1_0786,
    1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
    1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
    3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
lowercase_						=       [
    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, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
    1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
    2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
    4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[int]       						="""whisper"""
				UpperCamelCase__			:   Optional[int]       						=["""past_key_values"""]
				UpperCamelCase__			:   Optional[Any]       						={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self   :  List[Any],   __lowercase   :  List[str]=5_1865,   __lowercase   :  Dict=80,   __lowercase   :  List[Any]=6,   __lowercase   :  Union[str, Any]=4,   __lowercase   :  Tuple=6,   __lowercase   :  Dict=4,   __lowercase   :  List[Any]=1536,   __lowercase   :  Tuple=1536,   __lowercase   :  Any=0.0,   __lowercase   :  List[str]=0.0,   __lowercase   :  List[Any]=5_0257,   __lowercase   :  List[str]=True,   __lowercase   :  str=True,   __lowercase   :  int="gelu",   __lowercase   :  Tuple=256,   __lowercase   :  Tuple=0.0,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=0.0,   __lowercase   :  List[Any]=0.02,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  str=1500,   __lowercase   :  Optional[int]=448,   __lowercase   :  Optional[Any]=5_0256,   __lowercase   :  Tuple=5_0256,   __lowercase   :  Any=5_0256,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Any=[220, 5_0256],   __lowercase   :  List[Any]=False,   __lowercase   :  int=256,   __lowercase   :  int=False,   __lowercase   :  Tuple=0.05,   __lowercase   :  int=10,   __lowercase   :  Dict=2,   __lowercase   :  List[Any]=0.0,   __lowercase   :  Optional[int]=10,   __lowercase   :  Union[str, Any]=0,   __lowercase   :  Tuple=7,   **__lowercase   :  Union[str, Any],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			num_mel_bins
							lowercase__   					=			d_model
							lowercase__   					=			encoder_layers
							lowercase__   					=			encoder_attention_heads
							lowercase__   					=			decoder_layers
							lowercase__   					=			decoder_attention_heads
							lowercase__   					=			decoder_ffn_dim
							lowercase__   					=			encoder_ffn_dim
							lowercase__   					=			dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			activation_dropout
							lowercase__   					=			activation_function
							lowercase__   					=			init_std
							lowercase__   					=			encoder_layerdrop
							lowercase__   					=			decoder_layerdrop
							lowercase__   					=			use_cache
							lowercase__   					=			encoder_layers
							lowercase__   					=			scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__   					=			max_source_positions
							lowercase__   					=			max_target_positions

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

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

							lowercase__   					=			median_filter_width

							super().__init__(
							    pad_token_id=__lowercase,   bos_token_id=__lowercase,   eos_token_id=__lowercase,   is_encoder_decoder=__lowercase,   decoder_start_token_id=__lowercase,   suppress_tokens=__lowercase,   begin_suppress_tokens=__lowercase,   **__lowercase,   )
class 			_snake_case     (   lowercase__):




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

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

							return common_inputs




				def 							A__ (  self   :  int,   __lowercase   :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional["TensorType"] = None,   __lowercase   :  int = 2_2050,   __lowercase   :  float = 5.0,   __lowercase   :  int = 220,   ):
							lowercase__   					=			OrderedDict()
							lowercase__   					=			OnnxConfig.generate_dummy_inputs(
							    self,   preprocessor=preprocessor.feature_extractor,   batch_size=__lowercase,   framework=__lowercase,   sampling_rate=__lowercase,   time_duration=__lowercase,   frequency=__lowercase,   )
							lowercase__   					=			encoder_inputs["input_features"].shape[2]
							lowercase__   					=			encoder_sequence_length // 2 if self.use_past else seq_length

							lowercase__   					=			super().generate_dummy_inputs(
							    preprocessor.tokenizer,   __lowercase,   __lowercase,   __lowercase,   __lowercase  )

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

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

							return dummy_inputs




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




