


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase	: Optional[Any]  				=5_0_0_0_0_0

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


@get_duration
def        _UpperCamelCase							(      lowercase__    ,       **lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[str]					=		dataset.map(**lowercase__      )


@get_duration
def        _UpperCamelCase							(      lowercase__    ,       **lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		dataset.filter(**lowercase__      )


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		datasets.Features({'''text''': datasets.Value('''string'''      ), '''numbers''': datasets.Value('''float32'''      )}      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		generate_example_dataset(
												    os.path.join(lowercase__    ,       '''dataset.arrow'''      )    ,       lowercase__    ,       num_examples=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     Any					=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''    ,       use_fast=lowercase__      )

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

												__SCREAMING_SNAKE_CASE						:     List[str]					=		map(lowercase__      )

												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		map(lowercase__    ,       batched=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     str					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''numpy'''      ):
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''pandas'''      ):
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''torch'''    ,       columns='''numbers'''      ):
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												with dataset.formatted_as(type='''tensorflow'''    ,       columns='''numbers'''      ):
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		map(lowercase__    ,       function=lambda lowercase__      : None    ,       batched=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     Tuple					=		map(lowercase__    ,       function=lowercase__    ,       batched=lowercase__      )

												__SCREAMING_SNAKE_CASE						:     int					=		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()


'''simple docstring'''







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__lowerCAmelCase         =    HUGGINGFACE_HUB_CACHE


__lowerCAmelCase         =    """config.json"""
__lowerCAmelCase         =    """diffusion_pytorch_model.bin"""
__lowerCAmelCase         =    """diffusion_flax_model.msgpack"""
__lowerCAmelCase         =    """model.onnx"""
__lowerCAmelCase         =    """diffusion_pytorch_model.safetensors"""
__lowerCAmelCase         =    """weights.pb"""
__lowerCAmelCase         =    """https://huggingface.co"""
__lowerCAmelCase         =    default_cache_path
__lowerCAmelCase         =    """diffusers_modules"""
__lowerCAmelCase         =    os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
__lowerCAmelCase         =    ["""fp16""", """non-ema"""]
__lowerCAmelCase         =    """.self_attn"""



