





'''simple docstring'''




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__       : str         =       logging.get_logger(__name__)

lowercase__       : Union[str, Any]         =       {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''sew-d'''


     def __init__(		self		,      _UpperCAmelCase=32		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase=2		,      _UpperCAmelCase=512		,      _UpperCAmelCase=256		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=("p2c", "c2p")		,      _UpperCAmelCase="layer_norm"		,      _UpperCAmelCase="gelu_python"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-7		,      _UpperCAmelCase=1e-5		,      _UpperCAmelCase="group"		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,      _UpperCAmelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,      _UpperCAmelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,      _UpperCAmelCase=False		,      _UpperCAmelCase=128		,      _UpperCAmelCase=16		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0.05		,      _UpperCAmelCase=10		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.0		,      _UpperCAmelCase=10		,      _UpperCAmelCase=0		,      _UpperCAmelCase="mean"		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=256		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1		,      _UpperCAmelCase=2		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase		,      pad_token_id=_UpperCAmelCase		,      bos_token_id=_UpperCAmelCase		,      eos_token_id=_UpperCAmelCase)
          __A		:					List[Any]						=			hidden_size
          __A		:					Any						=			feat_extract_norm
          __A		:					List[str]						=			feat_extract_activation
          __A		:					Union[str, Any]						=			list(_UpperCAmelCase)
          __A		:					List[str]						=			list(_UpperCAmelCase)
          __A		:					Dict						=			list(_UpperCAmelCase)
          __A		:					Any						=			conv_bias
          __A		:					str						=			num_conv_pos_embeddings
          __A		:					Optional[int]						=			num_conv_pos_embedding_groups
          __A		:					Optional[Any]						=			len(self.conv_dim)
          __A		:					Union[str, Any]						=			num_hidden_layers
          __A		:					int						=			intermediate_size
          __A		:					Union[str, Any]						=			squeeze_factor
          __A		:					Tuple						=			max_position_embeddings
          __A		:					Optional[Any]						=			position_buckets
          __A		:					Union[str, Any]						=			share_att_key
          __A		:					Union[str, Any]						=			relative_attention
          __A		:					Optional[int]						=			norm_rel_ebd
          __A		:					Dict						=			list(_UpperCAmelCase)
          __A		:					Optional[Any]						=			hidden_act
          __A		:					List[str]						=			num_attention_heads
          __A		:					Union[str, Any]						=			hidden_dropout
          __A		:					List[Any]						=			attention_dropout
          __A		:					List[Any]						=			activation_dropout
          __A		:					Dict						=			feat_proj_dropout
          __A		:					str						=			final_dropout
          __A		:					Dict						=			layer_norm_eps
          __A		:					int						=			feature_layer_norm_eps
          __A		:					int						=			initializer_range
          __A		:					str						=			vocab_size

          if (
              (len(self.conv_stride) != self.num_feat_extract_layers)
              or (len(self.conv_kernel) != self.num_feat_extract_layers)
              or (len(self.conv_dim) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   'Configuration for convolutional layers is incorrect.'
                   'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                   F'but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)'
                   F'= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.')

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

          # ctc loss
          __A		:					str						=			ctc_loss_reduction
          __A		:					Any						=			ctc_zero_infinity

          # sequence classification
          __A		:					List[str]						=			use_weighted_layer_sum
          __A		:					List[Any]						=			classifier_proj_size


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return functools.reduce(operator.mul		,      self.conv_stride		,      1)






'''simple docstring'''

import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase				:      int						      =5_0000
__lowerCAmelCase				:      Union[str, Any]						      =5000

__lowerCAmelCase      ,	__lowerCAmelCase				:      Tuple						      =os.path.split(__file__)
__lowerCAmelCase				:      int						      =os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   int			):
    for i in range(_lowerCamelCase			):
        A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Any			):
    for i in range(0		,			len(_lowerCamelCase			)		,			_lowerCamelCase			):
        A__							=     dataset[i : i + batch_size]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   List[str]			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(_lowerCamelCase			):
            A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Dict			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(0		,			_lowerCamelCase		,			_lowerCamelCase			):
            A__							=     dataset[i : i + batch_size]





def 		UpperCamelCase							(				):
    A__							=     {"num examples": SPEED_TEST_N_EXAMPLES}
    A__							=     [
        (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": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (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": 10_00}),
    ]

    A__							=     [
        (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": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (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": 10_00}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"			)
        A__							=     datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"			)			), "numbers": datasets.Value("float32"			)}			)
        A__							=     generate_example_dataset(
            os.path.join(_lowerCamelCase		,			"dataset.arrow"			)		,			_lowerCamelCase		,			num_examples=_lowerCamelCase		,			seq_shapes={"list": (1_00,)}		,			)
        print("first set of iterations"			)
        for func, kwargs in functions:
            print(func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(_lowerCamelCase		,			**_lowerCamelCase			)

        print("shuffling dataset"			)
        A__							=     dataset.shuffle()
        print("Second set of iterations (after shuffling"			)
        for func, kwargs in functions_shuffled:
            print("shuffled "		,			func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(
                _lowerCamelCase		,			**_lowerCamelCase			)

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


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



