





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE_						       =	5_0_0_0_0
SCREAMING_SNAKE_CASE_						       =	5_0_0_0

SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_						       =	os.path.split(__file__)
SCREAMING_SNAKE_CASE_						       =	os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



@get_duration
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       List[Any]:


						'''simple docstring'''


						for i in range(_a     ):
												SCREAMING_SNAKE_CASE		=						dataset[i]



@get_duration
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Optional[int]:


						'''simple docstring'''


						for i in range(0			,						len(_a     )			,						_a     ):
												SCREAMING_SNAKE_CASE		=						dataset[i : i + batch_size]



@get_duration
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       List[Any]:


						'''simple docstring'''


						with dataset.formatted_as(type=_a     ):
												for i in range(_a     ):
																		SCREAMING_SNAKE_CASE		=						dataset[i]



@get_duration
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Optional[Any]:


						'''simple docstring'''


						with dataset.formatted_as(type=_a     ):
												for i in range(0			,						_a			,						_a     ):
																		SCREAMING_SNAKE_CASE		=						dataset[i : i + batch_size]



def        __lowercase       (				)			->       List[str]:


						'''simple docstring'''


						SCREAMING_SNAKE_CASE		=						{"""num examples""": SPEED_TEST_N_EXAMPLES}
						SCREAMING_SNAKE_CASE		=						[
						    (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}),
						]

						SCREAMING_SNAKE_CASE		=						[
						    (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"""     )
												SCREAMING_SNAKE_CASE		=						datasets.Features(
												    {"""list""": datasets.Sequence(datasets.Value("""float32"""     )     ), """numbers""": datasets.Value("""float32"""     )}     )
												SCREAMING_SNAKE_CASE		=						generate_example_dataset(
												    os.path.join(_a			,						"""dataset.arrow"""     )			,						_a			,						num_examples=_a			,						seq_shapes={"""list""": (1_00,)}			,						)
												print("""first set of iterations"""     )
												for func, kwargs in functions:
																		print(func.__name__			,						str(_a     )     )
																		SCREAMING_SNAKE_CASE		=						func(_a			,						**_a     )

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

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


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









import json
import sys



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


      with open(_SCREAMING_SNAKE_CASE			,						encoding="""utf-8"""     ) as f:
            SCREAMING_SNAKE_CASE		=						json.load(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=						["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

      for benchmark_name in sorted(_SCREAMING_SNAKE_CASE     ):
            SCREAMING_SNAKE_CASE		=						results[benchmark_name]

            SCREAMING_SNAKE_CASE		=						benchmark_name.split("""/"""     )[-1]
            output_md.append(F"""### Benchmark: {benchmark_file_name}"""     )

            SCREAMING_SNAKE_CASE		=						"""| metric |"""
            SCREAMING_SNAKE_CASE		=						"""|--------|"""
            SCREAMING_SNAKE_CASE		=						"""| new / old (diff) |"""
            for metric_name in sorted(_SCREAMING_SNAKE_CASE     ):
                  SCREAMING_SNAKE_CASE		=						benchmark_res[metric_name]
                  SCREAMING_SNAKE_CASE		=						metric_vals["""new"""]
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""old"""			,						_SCREAMING_SNAKE_CASE     )
                  SCREAMING_SNAKE_CASE		=						metric_vals.get("""diff"""			,						_SCREAMING_SNAKE_CASE     )

                  SCREAMING_SNAKE_CASE		=						F""" {new_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else """None"""

                  if old_val is not None:
                        val_str += F""" / {old_val:f}""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else "None"
                  if dif_val is not None:
                        val_str += F""" ({dif_val:f})""" if isinstance(_SCREAMING_SNAKE_CASE			,						(int, float)     ) else "None"

                  title += " " + metric_name + " |"
                  lines += "---|"
                  value += val_str + " |"

            output_md += [title, lines, value, " "]

      output_md.append("""</details>"""     )

      with open(_SCREAMING_SNAKE_CASE			,						"""w"""			,						encoding="""utf-8"""     ) as f:
            f.writelines("""\n""".join(_SCREAMING_SNAKE_CASE     )     )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	sys.argv[1]
      SCREAMING_SNAKE_CASE_						       =	sys.argv[2]

      format_json_to_md(input_json_file, output_md_file)


