
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase  :					Union[str, Any]						    =							50000
lowercase  :					Optional[int]						    =							5000

lowercase    ,  lowercase  :					List[str]						    =							os.path.split(__file__)
lowercase  :					int						    =							os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       for i in range(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Optional[int]       	=      dataset[i]







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       for i in range(0					,	len(SCREAMING_SNAKE_CASE__				)					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     str       	=      dataset[i : i + batch_size]







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__				):
              for i in range(SCREAMING_SNAKE_CASE__				):
                     lowercase							:     Any       	=      dataset[i]







@get_duration
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       with dataset.formatted_as(type=SCREAMING_SNAKE_CASE__				):
              for i in range(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     lowercase							:     List[Any]       	=      dataset[i : i + batch_size]







def 						_snake_case(  ) ->    str:
       lowercase							:     Any       	=      {"""num examples""": SPEED_TEST_N_EXAMPLES}
       lowercase							:     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}),
       ]

       lowercase							:     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""": 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"""				)
              lowercase							:     int       	=      datasets.Features(
                  {"""list""": datasets.Sequence(datasets.Value("""float32"""				)				), """numbers""": datasets.Value("""float32"""				)}				)
              lowercase							:     List[Any]       	=      generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE__					,	"""dataset.arrow"""				)					,	SCREAMING_SNAKE_CASE__					,	num_examples=SCREAMING_SNAKE_CASE__					,	seq_shapes={"""list""": (100,)}					,	)
              print("""first set of iterations"""				)
              for func, kwargs in functions:
                     print(func.__name__					,	str(SCREAMING_SNAKE_CASE__				)				)
                     lowercase							:     Any       	=      func(SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				)

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

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


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








"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{
    '''vocab_file''': {
        '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''unc-nlp/lxmert-base-uncased''': (
            '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE					:				Any					    =						{
    '''unc-nlp/lxmert-base-uncased''': 512,
}

_SCREAMING_SNAKE_CASE					:				int					    =						{
    '''unc-nlp/lxmert-base-uncased''': {'''do_lower_case''': True},
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     int						= VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= LxmertTokenizer






    def __init__(       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							str=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[UNK]"       ,						__SCREAMING_SNAKE_CASE     :							int="[SEP]"       ,						__SCREAMING_SNAKE_CASE     :							int="[PAD]"       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[CLS]"       ,						__SCREAMING_SNAKE_CASE     :							Any="[MASK]"       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							List[str]=None       ,						**__SCREAMING_SNAKE_CASE     :							Any       ,						)     ->		Any:
      super().__init__(
          __SCREAMING_SNAKE_CASE       ,						tokenizer_file=__SCREAMING_SNAKE_CASE       ,						do_lower_case=__SCREAMING_SNAKE_CASE       ,						unk_token=__SCREAMING_SNAKE_CASE       ,						sep_token=__SCREAMING_SNAKE_CASE       ,						pad_token=__SCREAMING_SNAKE_CASE       ,						cls_token=__SCREAMING_SNAKE_CASE       ,						mask_token=__SCREAMING_SNAKE_CASE       ,						tokenize_chinese_chars=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      lowerCamelCase_         =						json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
      if (
          normalizer_state.get('lowercase'       ,						__SCREAMING_SNAKE_CASE					) != do_lower_case
          or normalizer_state.get('strip_accents'       ,						__SCREAMING_SNAKE_CASE					) != strip_accents
          or normalizer_state.get('handle_chinese_chars'       ,						__SCREAMING_SNAKE_CASE					) != tokenize_chinese_chars
      ):
        lowerCamelCase_         =						getattr(__SCREAMING_SNAKE_CASE       ,						normalizer_state.pop('type'					)					)
        lowerCamelCase_         =						do_lower_case
        lowerCamelCase_         =						strip_accents
        lowerCamelCase_         =						tokenize_chinese_chars
        lowerCamelCase_         =						normalizer_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						do_lower_case






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Tuple=None					)     ->		Dict:
      lowerCamelCase_         =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							List[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[int]] = None					)     ->		List[int]:
      lowerCamelCase_         =						[self.sep_token_id]
      lowerCamelCase_         =						[self.cls_token_id]
      if token_ids_a is None:
        return len(cls + token_ids_a + sep					) * [0]
      return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None					)     ->		Tuple[str]:
      lowerCamelCase_         =						self._tokenizer.model.save(__SCREAMING_SNAKE_CASE       ,						name=__SCREAMING_SNAKE_CASE					)
      return tuple(__SCREAMING_SNAKE_CASE					)




