


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCAmelCase__      		= 5_00_00
lowerCAmelCase__      		= 50_00

lowerCAmelCase__		,lowerCAmelCase__      		= os.path.split(__file__)
lowerCAmelCase__      		= os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  Optional[Any]						):
       for i in range(UpperCamelCase__						):
              _A      :       Optional[int]				    = dataset[i]
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Dict						):
       for i in range(0				,							len(UpperCamelCase__						)				,							UpperCamelCase__						):
              _A      :       int				    = dataset[i : i + batch_size]
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  int						):
       with dataset.formatted_as(type=UpperCamelCase__						):
              for i in range(UpperCamelCase__						):
                     _A      :       Optional[int]				    = dataset[i]
@get_duration
def 							_UpperCAmelCase				(UpperCamelCase__     :  datasets.Dataset				,							UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  List[Any]						):
       with dataset.formatted_as(type=UpperCamelCase__						):
              for i in range(0				,							UpperCamelCase__				,							UpperCamelCase__						):
                     _A      :       Dict				    = dataset[i : i + batch_size]
def 							_UpperCAmelCase				():
       _A      :       Optional[int]				    = {"num examples": SPEED_TEST_N_EXAMPLES}
       _A      :       List[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": 1000}),
           (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": 1000}),
       ]

       _A      :       str				    = [
           (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": 1000}),
           (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": 1000}),
       ]
       with tempfile.TemporaryDirectory() as tmp_dir:
              print("generating dataset"						)
              _A      :       List[Any]				    = datasets.Features(
                  {"list": datasets.Sequence(datasets.Value("float32"						)						), "numbers": datasets.Value("float32"						)}						)
              _A      :       Dict				    = generate_example_dataset(
                  os.path.join(UpperCamelCase__				,							"dataset.arrow"						)				,							UpperCamelCase__				,							num_examples=UpperCamelCase__				,							seq_shapes={"list": (100,)}				,							)
              print("first set of iterations"						)
              for func, kwargs in functions:
                     print(func.__name__				,							str(UpperCamelCase__						)						)
                     _A      :       List[str]				    = func(UpperCamelCase__				,							**UpperCamelCase__						)

              print("shuffling dataset"						)
              _A      :       Union[str, Any]				    = dataset.shuffle()
              print("Second set of iterations (after shuffling"						)
              for func, kwargs in functions_shuffled:
                     print("shuffled "				,							func.__name__				,							str(UpperCamelCase__						)						)
                     _A      :       Dict				    = func(
                         UpperCamelCase__				,							**UpperCamelCase__						)

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


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








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


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

lowerCamelCase       :       List[str]        =				{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''canine'''



					def __init__(     self :		List[str]						,   __a :		Optional[int]=768						,   __a :		Any=12						,   __a :		Any=12						,   __a :		Dict=3072						,   __a :		Dict="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		Tuple=16384						,   __a :		List[Any]=16						,   __a :		List[Any]=0.02						,   __a :		Optional[Any]=1E-12						,   __a :		Dict=0						,   __a :		List[Any]=0xe_0_0_0						,   __a :		Optional[int]=0xe_0_0_1						,   __a :		Any=4						,   __a :		Dict=4						,   __a :		Optional[int]=8						,   __a :		Any=16384						,   __a :		Optional[Any]=128						,   **__a :		List[str]						,   )	->     Union[str, Any]:


												"""simple docstring"""

												super().__init__(pad_token_id=__a						,   bos_token_id=__a						,   eos_token_id=__a						,   **__a							)

												__lowercase					:							int			=   max_position_embeddings
												__lowercase					:							List[str]			=   hidden_size
												__lowercase					:							List[Any]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							Tuple			=   hidden_dropout_prob
												__lowercase					:							Union[str, Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   initializer_range
												__lowercase					:							Any			=   type_vocab_size
												__lowercase					:							int			=   layer_norm_eps

												# Character config:
												__lowercase					:							int			=   downsampling_rate
												__lowercase					:							str			=   upsampling_kernel_size
												__lowercase					:							Union[str, Any]			=   num_hash_functions
												__lowercase					:							Optional[Any]			=   num_hash_buckets
												__lowercase					:							Optional[int]			=   local_transformer_stride