




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase	:		Any							       =   500000

_lowerCamelCase       ,			_lowerCamelCase	:		List[Any]							       =   os.path.split(__file__)
_lowerCamelCase	:		Tuple							       =   os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				**lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    dataset.map(**lowercase_						)







@get_duration
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				**lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    dataset.filter(**lowercase_						)







def SCREAMING_SNAKE_CASE				(       )   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    {'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              A__       =    datasets.Features({'''text''': datasets.Value('''string'''						), '''numbers''': datasets.Value('''float32'''						)}						)
              A__       =    generate_example_dataset(
                  os.path.join(lowercase_ ,				'''dataset.arrow'''						) ,				lowercase_ ,				num_examples=lowercase_						)

              A__       =    transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' ,				use_fast=lowercase_						)

              def tokenize(lowercase_						):
                     return tokenizer(examples['''text''']						)

              A__       =    map(lowercase_						)

              A__       =    map(lowercase_ ,				batched=lowercase_						)

              A__       =    map(lowercase_ ,				function=lambda lowercase_						: None ,				batched=lowercase_						)

              with dataset.formatted_as(type='''numpy'''						):
                     A__       =    map(lowercase_ ,				function=lambda lowercase_						: None ,				batched=lowercase_						)

              with dataset.formatted_as(type='''pandas'''						):
                     A__       =    map(lowercase_ ,				function=lambda lowercase_						: None ,				batched=lowercase_						)

              with dataset.formatted_as(type='''torch''' ,				columns='''numbers'''						):
                     A__       =    map(lowercase_ ,				function=lambda lowercase_						: None ,				batched=lowercase_						)

              with dataset.formatted_as(type='''tensorflow''' ,				columns='''numbers'''						):
                     A__       =    map(lowercase_ ,				function=lambda lowercase_						: None ,				batched=lowercase_						)

              A__       =    map(lowercase_ ,				function=lowercase_ ,				batched=lowercase_						)

              A__       =    filter(lowercase_						)

              # 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(lowercase_ ,				'''wb'''						) as f:
              f.write(json.dumps(lowercase_						).encode('''utf-8'''						)						)


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








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[Any]:





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


