



'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__a  			=						5_0000
__a  			=						5000

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





@get_duration
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Dict:
      for i in range(_lowerCAmelCase						):
            snake_case__    :       Dict       		= dataset[i]





@get_duration
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					List[str]:
      for i in range(0	,  len(_lowerCAmelCase						)	,  _lowerCAmelCase						):
            snake_case__    :       Union[str, Any]       		= dataset[i : i + batch_size]





@get_duration
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Optional[int]:
      with dataset.formatted_as(type=_lowerCAmelCase						):
            for i in range(_lowerCAmelCase						):
                  snake_case__    :       Union[str, Any]       		= dataset[i]





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





def 		__snake_case(							)						->					Optional[Any]:
      snake_case__    :       List[Any]       		= {"""num examples""": SPEED_TEST_N_EXAMPLES}
      snake_case__    :       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, {"""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}),
      ]

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

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





'''simple docstring'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__a  			=						"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
       import torch

       print("Torch version:", torch.__version__)
       print("Cuda available:", torch.cuda.is_available())
       print("Cuda version:", torch.version.cuda)
       print("CuDNN version:", torch.backends.cudnn.version())
       print("Number of GPUs available:", torch.cuda.device_count())
       print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
       print("Torch version:", None)

try:
       import deepspeed

       print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
       print("DeepSpeed version:", None)

try:
       import tensorflow as tf

       print("TensorFlow version:", tf.__version__)
       print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
       print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
       print("TensorFlow version:", None)
