






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, 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
]
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, 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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					'''whisper'''
	lowerCAmelCase						=					['''past_key_values''']
	lowerCAmelCase						=					{'''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=0.02			,     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=0.05			,     a=10			,     a=2			,     a=0.0			,     a=10			,     a=0			,     a=7			,     **a			,     )			->       Any:
								snake_case_           =				vocab_size
								snake_case_           =				num_mel_bins
								snake_case_           =				d_model
								snake_case_           =				encoder_layers
								snake_case_           =				encoder_attention_heads
								snake_case_           =				decoder_layers
								snake_case_           =				decoder_attention_heads
								snake_case_           =				decoder_ffn_dim
								snake_case_           =				encoder_ffn_dim
								snake_case_           =				dropout
								snake_case_           =				attention_dropout
								snake_case_           =				activation_dropout
								snake_case_           =				activation_function
								snake_case_           =				init_std
								snake_case_           =				encoder_layerdrop
								snake_case_           =				decoder_layerdrop
								snake_case_           =				use_cache
								snake_case_           =				encoder_layers
								snake_case_           =				scale_embedding  # scale factor will be sqrt(d_model) if True
								snake_case_           =				max_source_positions
								snake_case_           =				max_target_positions

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

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

								snake_case_           =				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_ (     snake_case_							):



	'''simple docstring'''




	@property
	def 				_UpperCamelCase     (			self   )			->       Mapping[str, Mapping[int, str]]:
								snake_case_           =				OrderedDict(
								    [
								        ('input_features', {0: 'batch', 1: 'feature_size', 2: 'encoder_sequence'}),
								    ]   )
								if self.use_past:
															snake_case_           =				{0: 'batch'}
								else:
															snake_case_           =				{0: 'batch', 1: 'decoder_sequence'}

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

								return common_inputs




	def 				_UpperCamelCase     (			self			,     a			,     a = -1			,     a = -1			,     a = False			,     a = None			,     a = 2_20_50			,     a = 5.0			,     a = 2_20			,     )			->       Mapping[str, Any]:
								snake_case_           =				OrderedDict()
								snake_case_           =				OnnxConfig.generate_dummy_inputs(
								    self			,     preprocessor=preprocessor.feature_extractor			,     batch_size=a			,     framework=a			,     sampling_rate=a			,     time_duration=a			,     frequency=a			,     )
								snake_case_           =				encoder_inputs['input_features'].shape[2]
								snake_case_           =				encoder_sequence_length // 2 if self.use_past else seq_length

								snake_case_           =				super().generate_dummy_inputs(
								    preprocessor.tokenizer			,     a			,     a			,     a			,     a   )

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

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

								return dummy_inputs






	@property
	def 				_UpperCamelCase     (			self   )			->       float:
								return 1E-3













import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





