






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A_       :Optional[int]       =						50000
A_       :Dict       =						5000

A_       :Union[str, Any]       =						os.path.split(__file__)
A_       :Any       =						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))






@get_duration
def 							A     (       a_							,a_					)     ->						Dict:
					for i in range(__snake_case					):
										__UpperCamelCase      :					Union[str, Any]       		=dataset[i]






@get_duration
def 							A     (       a_							,a_							,a_					)     ->						Dict:
					for i in range(0							,len(__snake_case					)							,__snake_case					):
										__UpperCamelCase      :					List[str]       		=dataset[i : i + batch_size]






@get_duration
def 							A     (       a_							,a_							,a_					)     ->						Dict:
					with dataset.formatted_as(type=__snake_case					):
										for i in range(__snake_case					):
															__UpperCamelCase      :					Union[str, Any]       		=dataset[i]






@get_duration
def 							A     (       a_							,a_							,a_							,a_					)     ->						str:
					with dataset.formatted_as(type=__snake_case					):
										for i in range(0							,__snake_case							,__snake_case					):
															__UpperCamelCase      :					Optional[int]       		=dataset[i : i + batch_size]






def 							A     (       )     ->						Union[str, Any]:
					__UpperCamelCase      :					Optional[int]       		={"num examples": SPEED_TEST_N_EXAMPLES}
					__UpperCamelCase      :					Optional[int]       		=[
					    (read, {"length": SMALL_TEST}),
					    (read, {"length": SPEED_TEST_N_EXAMPLES}),
					    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
					    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
					    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
					    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
					    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
					    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
					    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
					    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
					    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
					]

					__UpperCamelCase      :					Any       		=[
					    (read, {"length": SMALL_TEST}),
					    (read, {"length": SPEED_TEST_N_EXAMPLES}),
					    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
					    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
					    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
					    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
					    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
					    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
					]
					with tempfile.TemporaryDirectory() as tmp_dir:
										print('generating dataset'					)
										__UpperCamelCase      :					int       		=datasets.Features(
										    {'list': datasets.Sequence(datasets.Value('float32'					)					), 'numbers': datasets.Value('float32'					)}					)
										__UpperCamelCase      :					str       		=generate_example_dataset(
										    os.path.join(__snake_case							,'dataset.arrow'					)							,__snake_case							,num_examples=__snake_case							,seq_shapes={'list': (100,)}							,)
										print('first set of iterations'					)
										for func, kwargs in functions:
															print(func.__name__							,str(__snake_case					)					)
															__UpperCamelCase      :					str       		=func(__snake_case							,**__snake_case					)

										print('shuffling dataset'					)
										__UpperCamelCase      :					Optional[int]       		=dataset.shuffle()
										print('Second set of iterations (after shuffling'					)
										for func, kwargs in functions_shuffled:
															print('shuffled '							,func.__name__							,str(__snake_case					)					)
															__UpperCamelCase      :					List[str]       		=func(
															    __snake_case							,**__snake_case					)

					with open(__snake_case							,'wb'					) as f:
										f.write(json.dumps(__snake_case					).encode('utf-8'					)					)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_iterating()






"""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

__snake_case							:   Optional[Any]      =    logging.get_logger(__name__)

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

# fmt: off
__snake_case							:   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
]
__snake_case							:   str      =    [
    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__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	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:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Any=5_1865       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=80       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=6       ,			_SCREAMING_SNAKE_CASE:		Any=4       ,			_SCREAMING_SNAKE_CASE:		Dict=6       ,			_SCREAMING_SNAKE_CASE:		Dict=4       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=1536       ,			_SCREAMING_SNAKE_CASE:		List[str]=1536       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.0       ,			_SCREAMING_SNAKE_CASE:		str=0.0       ,			_SCREAMING_SNAKE_CASE:		List[str]=5_0257       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		List[str]=True       ,			_SCREAMING_SNAKE_CASE:		Optional[int]="gelu"       ,			_SCREAMING_SNAKE_CASE:		Tuple=256       ,			_SCREAMING_SNAKE_CASE:		str=0.0       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.0       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=0.0       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1500       ,			_SCREAMING_SNAKE_CASE:		str=448       ,			_SCREAMING_SNAKE_CASE:		Any=5_0256       ,			_SCREAMING_SNAKE_CASE:		Any=5_0256       ,			_SCREAMING_SNAKE_CASE:		List[str]=5_0256       ,			_SCREAMING_SNAKE_CASE:		Dict=None       ,			_SCREAMING_SNAKE_CASE:		List[Any]=[220, 5_0256]       ,			_SCREAMING_SNAKE_CASE:		Dict=False       ,			_SCREAMING_SNAKE_CASE:		str=256       ,			_SCREAMING_SNAKE_CASE:		List[str]=False       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.05       ,			_SCREAMING_SNAKE_CASE:		List[str]=10       ,			_SCREAMING_SNAKE_CASE:		str=2       ,			_SCREAMING_SNAKE_CASE:		Any=0.0       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=10       ,			_SCREAMING_SNAKE_CASE:		int=0       ,			_SCREAMING_SNAKE_CASE:		Any=7       ,			**_SCREAMING_SNAKE_CASE:		List[str]       ,			)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       vocab_size
				__lowerCAmelCase      :  Optional[Any]												=       num_mel_bins
				__lowerCAmelCase      :  int												=       d_model
				__lowerCAmelCase      :  List[Any]												=       encoder_layers
				__lowerCAmelCase      :  List[Any]												=       encoder_attention_heads
				__lowerCAmelCase      :  List[str]												=       decoder_layers
				__lowerCAmelCase      :  Tuple												=       decoder_attention_heads
				__lowerCAmelCase      :  Any												=       decoder_ffn_dim
				__lowerCAmelCase      :  Tuple												=       encoder_ffn_dim
				__lowerCAmelCase      :  List[str]												=       dropout
				__lowerCAmelCase      :  Union[str, Any]												=       attention_dropout
				__lowerCAmelCase      :  Union[str, Any]												=       activation_dropout
				__lowerCAmelCase      :  Dict												=       activation_function
				__lowerCAmelCase      :  Tuple												=       init_std
				__lowerCAmelCase      :  str												=       encoder_layerdrop
				__lowerCAmelCase      :  int												=       decoder_layerdrop
				__lowerCAmelCase      :  Optional[int]												=       use_cache
				__lowerCAmelCase      :  Union[str, Any]												=       encoder_layers
				__lowerCAmelCase      :  List[str]												=       scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase      :  int												=       max_source_positions
				__lowerCAmelCase      :  Any												=       max_target_positions

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

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

				__lowerCAmelCase      :  Union[str, Any]												=       median_filter_width

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE       ,			bos_token_id=_SCREAMING_SNAKE_CASE       ,			eos_token_id=_SCREAMING_SNAKE_CASE       ,			is_encoder_decoder=_SCREAMING_SNAKE_CASE       ,			decoder_start_token_id=_SCREAMING_SNAKE_CASE       ,			suppress_tokens=_SCREAMING_SNAKE_CASE       ,			begin_suppress_tokens=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)






class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       OrderedDict(
				    [
				        ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
				    ])
				if self.use_past:
							__lowerCAmelCase      :  Tuple												=       {0: "batch"}
				else:
							__lowerCAmelCase      :  Union[str, Any]												=       {0: "batch", 1: "decoder_sequence"}

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

				return common_inputs

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_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_2050       ,			_SCREAMING_SNAKE_CASE:		float = 5.0       ,			_SCREAMING_SNAKE_CASE:		int = 220       ,			)				->       Mapping[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       OrderedDict()
				__lowerCAmelCase      :  Optional[Any]												=       OnnxConfig.generate_dummy_inputs(
				    self       ,			preprocessor=preprocessor.feature_extractor       ,			batch_size=_SCREAMING_SNAKE_CASE       ,			framework=_SCREAMING_SNAKE_CASE       ,			sampling_rate=_SCREAMING_SNAKE_CASE       ,			time_duration=_SCREAMING_SNAKE_CASE       ,			frequency=_SCREAMING_SNAKE_CASE       ,			)
				__lowerCAmelCase      :  List[str]												=       encoder_inputs["input_features"].shape[2]
				__lowerCAmelCase      :  List[str]												=       encoder_sequence_length // 2 if self.use_past else seq_length

				__lowerCAmelCase      :  List[Any]												=       super().generate_dummy_inputs(
				    preprocessor.tokenizer       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[str]												=       encoder_inputs.pop("input_features")
				__lowerCAmelCase      :  List[Any]												=       decoder_inputs.pop("decoder_input_ids")

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

				return dummy_inputs







	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       float:

				"""simple docstring"""

				return 1e-3