
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase_  :	Any 				=					5_00_00
lowerCamelCase_  :	Any 				=					50_00

lowerCamelCase_  :	Tuple 				=					os.path.split(__file__)
lowerCamelCase_  :	int 				=					os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(lowerCamelCase  ):
								UpperCamelCase_:							Dict		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				for i in range(0       ,      len(lowerCamelCase  )       ,      lowerCamelCase  ):
								UpperCamelCase_:							List[Any]		=  dataset[i : i + batch_size]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Any:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(lowerCamelCase  ):
												UpperCamelCase_:							List[str]		=  dataset[i]







@get_duration
def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Optional[int]:
				with dataset.formatted_as(type=lowerCamelCase  ):
								for i in range(0       ,      lowerCamelCase       ,      lowerCamelCase  ):
												UpperCamelCase_:							Union[str, Any]		=  dataset[i : i + batch_size]







def     A__   (  )					->		Tuple:
				UpperCamelCase_:							int		=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
				UpperCamelCase_:							Union[str, 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""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (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""": 10_00}),
				]

				UpperCamelCase_:							Tuple		=  [
				    (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""": 1_00}),
				    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10_00}),
				    (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""": 10_00}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print("""generating dataset"""  )
								UpperCamelCase_:							int		=  datasets.Features(
								    {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
								UpperCamelCase_:							Optional[int]		=  generate_example_dataset(
								    os.path.join(lowerCamelCase       ,      """dataset.arrow"""  )       ,      lowerCamelCase       ,      num_examples=lowerCamelCase       ,      seq_shapes={"""list""": (1_00,)}       ,      )
								print("""first set of iterations"""  )
								for func, kwargs in functions:
												print(func.__name__       ,      str(lowerCamelCase  )  )
												UpperCamelCase_:							List[Any]		=  func(lowerCamelCase       ,      **lowerCamelCase  )

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

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


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








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


lowerCamelCase_  :	Tuple 				=					logging.get_logger(__name__)

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

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Any    =	"""lxmert"""
			__UpperCamelCase :  Optional[Any]    =	{}

			def __init__(    self	:    Dict  ,		snake_case_	:    List[str]=3_0522  ,		snake_case_	:    int=768  ,		snake_case_	:    str=12  ,		snake_case_	:    Tuple=9500  ,		snake_case_	:    Optional[Any]=1600  ,		snake_case_	:    str=400  ,		snake_case_	:    List[str]=3072  ,		snake_case_	:    Dict="gelu"  ,		snake_case_	:    Optional[Any]=0.1  ,		snake_case_	:    int=0.1  ,		snake_case_	:    Optional[int]=512  ,		snake_case_	:    Tuple=2  ,		snake_case_	:    Optional[int]=0.02  ,		snake_case_	:    Union[str, Any]=1e-12  ,		snake_case_	:    Tuple=9  ,		snake_case_	:    Tuple=5  ,		snake_case_	:    Union[str, Any]=5  ,		snake_case_	:    List[str]=2048  ,		snake_case_	:    Any=4  ,		snake_case_	:    int=6.67  ,		snake_case_	:    Dict=True  ,		snake_case_	:    Tuple=True  ,		snake_case_	:    int=True  ,		snake_case_	:    str=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[Any]=True  ,		snake_case_	:    List[str]=True  ,		**snake_case_	:    Optional[int]  ,		):
							UpperCamelCase_:							int		=  vocab_size
							UpperCamelCase_:							Tuple		=  hidden_size
							UpperCamelCase_:							Optional[Any]		=  num_attention_heads
							UpperCamelCase_:							List[str]		=  hidden_act
							UpperCamelCase_:							int		=  intermediate_size
							UpperCamelCase_:							Any		=  hidden_dropout_prob
							UpperCamelCase_:							List[Any]		=  attention_probs_dropout_prob
							UpperCamelCase_:							List[str]		=  max_position_embeddings
							UpperCamelCase_:							Optional[Any]		=  type_vocab_size
							UpperCamelCase_:							Optional[Any]		=  initializer_range
							UpperCamelCase_:							str		=  layer_norm_eps
							UpperCamelCase_:							List[str]		=  num_qa_labels
							UpperCamelCase_:							List[str]		=  num_object_labels
							UpperCamelCase_:							Tuple		=  num_attr_labels
							UpperCamelCase_:							List[Any]		=  l_layers
							UpperCamelCase_:							List[Any]		=  x_layers
							UpperCamelCase_:							Any		=  r_layers
							UpperCamelCase_:							str		=  visual_feat_dim
							UpperCamelCase_:							Union[str, Any]		=  visual_pos_dim
							UpperCamelCase_:							str		=  visual_loss_normalizer
							UpperCamelCase_:							Dict		=  task_matched
							UpperCamelCase_:							Tuple		=  task_mask_lm
							UpperCamelCase_:							List[str]		=  task_obj_predict
							UpperCamelCase_:							Any		=  task_qa
							UpperCamelCase_:							Dict		=  visual_obj_loss
							UpperCamelCase_:							Optional[Any]		=  visual_attr_loss
							UpperCamelCase_:							List[Any]		=  visual_feat_loss
							UpperCamelCase_:							Tuple		=  {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
							super().__init__(**snake_case_ )






