

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase__ :     List[Any]				    =    500_000

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






@get_duration
def 					__lowercase      (     _A						,    **_A			)     ->						Optional[int]:
      SCREAMING_SNAKE_CASE :      List[str]       =   dataset.map(**_A			)






@get_duration
def 					__lowercase      (     _A						,    **_A			)     ->						List[Any]:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   dataset.filter(**_A			)






def 					__lowercase      (     )     ->						Tuple:
      SCREAMING_SNAKE_CASE :      List[str]       =   {"""num examples""": SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   datasets.Features({"""text""": datasets.Value("""string"""			), """numbers""": datasets.Value("""float32"""			)}			)
            SCREAMING_SNAKE_CASE :      List[Any]       =   generate_example_dataset(
                os.path.join(_A						,    """dataset.arrow"""			)						,    _A						,    num_examples=_A			)

            SCREAMING_SNAKE_CASE :      List[str]       =   transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,    use_fast=_A			)

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

            SCREAMING_SNAKE_CASE :      List[Any]       =   map(_A			)

            SCREAMING_SNAKE_CASE :      List[Any]       =   map(_A						,    batched=_A			)

            SCREAMING_SNAKE_CASE :      Optional[int]       =   map(_A						,    function=lambda _A			: None						,    batched=_A			)

            with dataset.formatted_as(type="""numpy"""			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   map(_A						,    function=lambda _A			: None						,    batched=_A			)

            with dataset.formatted_as(type="""pandas"""			):
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   map(_A						,    function=lambda _A			: None						,    batched=_A			)

            with dataset.formatted_as(type="""torch"""						,    columns="""numbers"""			):
                  SCREAMING_SNAKE_CASE :      Any       =   map(_A						,    function=lambda _A			: None						,    batched=_A			)

            with dataset.formatted_as(type="""tensorflow"""						,    columns="""numbers"""			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   map(_A						,    function=lambda _A			: None						,    batched=_A			)

            SCREAMING_SNAKE_CASE :      Tuple       =   map(_A						,    function=_A						,    batched=_A			)

            SCREAMING_SNAKE_CASE :      Optional[Any]       =   filter(_A			)

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


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







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


UpperCAmelCase__ :     List[Any]				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       ="""lxmert"""
  UpperCAmelCase__      : str			       ={}


  def __init__(			self    : int				,				UpperCAmelCase__    : Dict=3_0_5_2_2				,				UpperCAmelCase__    : int=7_6_8				,				UpperCAmelCase__    : Union[str, Any]=1_2				,				UpperCAmelCase__    : int=9_5_0_0				,				UpperCAmelCase__    : Union[str, Any]=1_6_0_0				,				UpperCAmelCase__    : Optional[Any]=4_0_0				,				UpperCAmelCase__    : List[str]=3_0_7_2				,				UpperCAmelCase__    : List[Any]="gelu"				,				UpperCAmelCase__    : int=0.1				,				UpperCAmelCase__    : Optional[Any]=0.1				,				UpperCAmelCase__    : Union[str, Any]=5_1_2				,				UpperCAmelCase__    : int=2				,				UpperCAmelCase__    : List[str]=0.02				,				UpperCAmelCase__    : str=1e-12				,				UpperCAmelCase__    : int=9				,				UpperCAmelCase__    : Optional[int]=5				,				UpperCAmelCase__    : int=5				,				UpperCAmelCase__    : List[str]=2_0_4_8				,				UpperCAmelCase__    : Tuple=4				,				UpperCAmelCase__    : List[str]=6.67				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[Any]=True				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : List[Any]=True				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[Any]=True				,				**UpperCAmelCase__    : Tuple				,				)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_size
        SCREAMING_SNAKE_CASE :      Any       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      str       =   hidden_act
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   intermediate_size
        SCREAMING_SNAKE_CASE :      List[str]       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      str       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      Dict       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Any       =   type_vocab_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[int]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      Dict       =   num_qa_labels
        SCREAMING_SNAKE_CASE :      str       =   num_object_labels
        SCREAMING_SNAKE_CASE :      str       =   num_attr_labels
        SCREAMING_SNAKE_CASE :      List[str]       =   l_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   x_layers
        SCREAMING_SNAKE_CASE :      List[str]       =   r_layers
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   visual_feat_dim
        SCREAMING_SNAKE_CASE :      Dict       =   visual_pos_dim
        SCREAMING_SNAKE_CASE :      List[Any]       =   visual_loss_normalizer
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   task_matched
        SCREAMING_SNAKE_CASE :      int       =   task_mask_lm
        SCREAMING_SNAKE_CASE :      int       =   task_obj_predict
        SCREAMING_SNAKE_CASE :      Optional[int]       =   task_qa
        SCREAMING_SNAKE_CASE :      Any       =   visual_obj_loss
        SCREAMING_SNAKE_CASE :      str       =   visual_attr_loss
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   visual_feat_loss
        SCREAMING_SNAKE_CASE :      List[Any]       =   {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
        super().__init__(**UpperCAmelCase__		)




