


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE__					:     List[Any]       =     500_000

SCREAMING_SNAKE_CASE__  ,					SCREAMING_SNAKE_CASE__					:     Dict       =     os.path.split(__file__)
SCREAMING_SNAKE_CASE__					:     Dict       =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def    a__    (		snake_case__					:							datasets.Dataset						,   **snake_case__					:							List[str]     ):
  _UpperCAmelCase		:							str							=     dataset.map(**snake_case__     )




@get_duration
def    a__    (		snake_case__					:							datasets.Dataset						,   **snake_case__					:							str     ):
  _UpperCAmelCase		:							Optional[int]							=     dataset.filter(**snake_case__     )




def    a__    (		):
  _UpperCAmelCase		:							Union[str, Any]							=     {"""num examples""": SPEED_TEST_N_EXAMPLES}
  with tempfile.TemporaryDirectory() as tmp_dir:
    _UpperCAmelCase		:							List[Any]							=     datasets.Features({"""text""": datasets.Value("""string"""     ), """numbers""": datasets.Value("""float32"""     )}     )
    _UpperCAmelCase		:							Dict							=     generate_example_dataset(
        os.path.join(snake_case__						,   """dataset.arrow"""     )						,   snake_case__						,   num_examples=snake_case__     )

    _UpperCAmelCase		:							int							=     transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,   use_fast=snake_case__     )

    def tokenize(snake_case__					:							str     ):
      return tokenizer(examples["""text"""]     )

    _UpperCAmelCase		:							Optional[int]							=     map(snake_case__     )

    _UpperCAmelCase		:							Any							=     map(snake_case__						,   batched=snake_case__     )

    _UpperCAmelCase		:							List[str]							=     map(snake_case__						,   function=lambda snake_case__     : None						,   batched=snake_case__     )

    with dataset.formatted_as(type="""numpy"""     ):
      _UpperCAmelCase		:							List[Any]							=     map(snake_case__						,   function=lambda snake_case__     : None						,   batched=snake_case__     )

    with dataset.formatted_as(type="""pandas"""     ):
      _UpperCAmelCase		:							Dict							=     map(snake_case__						,   function=lambda snake_case__     : None						,   batched=snake_case__     )

    with dataset.formatted_as(type="""torch"""						,   columns="""numbers"""     ):
      _UpperCAmelCase		:							Tuple							=     map(snake_case__						,   function=lambda snake_case__     : None						,   batched=snake_case__     )

    with dataset.formatted_as(type="""tensorflow"""						,   columns="""numbers"""     ):
      _UpperCAmelCase		:							int							=     map(snake_case__						,   function=lambda snake_case__     : None						,   batched=snake_case__     )

    _UpperCAmelCase		:							Optional[Any]							=     map(snake_case__						,   function=snake_case__						,   batched=snake_case__     )

    _UpperCAmelCase		:							Any							=     filter(snake_case__     )

    # Activate later when tokenizer support batched inputs
    # with dataset.formatted_as(type='numpy'):
    #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

  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_map_filter()









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     List[str]       =     {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''camembert'''


     def __init__(  self							,					A_=3_05_22							,					A_=7_68							,					A_=12							,					A_=12							,					A_=30_72							,					A_="gelu"							,					A_=0.1							,					A_=0.1							,					A_=5_12							,					A_=2							,					A_=0.0_2							,					A_=1e-12							,					A_=1							,					A_=0							,					A_=2							,					A_="absolute"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					bos_token_id=A_							,					eos_token_id=A_							,					**A_    )

       _UpperCAmelCase		:							Optional[Any]							=     vocab_size
       _UpperCAmelCase		:							int							=     hidden_size
       _UpperCAmelCase		:							Any							=     num_hidden_layers
       _UpperCAmelCase		:							Optional[Any]							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     hidden_act
       _UpperCAmelCase		:							str							=     intermediate_size
       _UpperCAmelCase		:							str							=     hidden_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     max_position_embeddings
       _UpperCAmelCase		:							Union[str, Any]							=     type_vocab_size
       _UpperCAmelCase		:							List[Any]							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							int							=     position_embedding_type
       _UpperCAmelCase		:							Tuple							=     use_cache
       _UpperCAmelCase		:							Any							=     classifier_dropout


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     @property
     def        __snake_case(  self    ):
       if self.task == "multiple-choice":
         _UpperCAmelCase		:							Any							=     {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _UpperCAmelCase		:							List[Any]							=     {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]    )





