
"""simple docstring"""
import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    int            = Dataset.from_dict({"i": range(17		)}		)
  _snake_case  :    Tuple            = len(a		)
  _snake_case  :    int            = 3
  _snake_case  :    Any            = [
      split_dataset_by_node(a							,  rank=a							,  world_size=a		) for rank in range(a		)
  ]
  assert sum(len(a		) for ds in datasets_per_rank		) == full_size
  assert len({tuple(x.values()		) for ds in datasets_per_rank for x in ds}		) == full_size
def 					a__ ( ):

  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(17		))

  _snake_case  :    Union[str, Any]            = 3
  _snake_case  :    Optional[int]            = IterableDataset.from_generator(a		)
  _snake_case  :    Optional[int]            = len(list(a		)		)
  _snake_case  :    Optional[int]            = [
      split_dataset_by_node(a							,  rank=a							,  world_size=a		) for rank in range(a		)
  ]
  assert sum(len(list(a		)		) for ds in datasets_per_rank		) == full_size
  assert len({tuple(x.values()		) for ds in datasets_per_rank for x in ds}		) == full_size
@pytest.mark.parametrize("shards_per_node"							,  [1, 2, 3]		)
def 					a__ ( a :					int		):

  """simple docstring"""







  def gen(a :					int		):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17		))

  _snake_case  :    str            = 3
  _snake_case  :    List[Any]            = shards_per_node * world_size
  _snake_case  :    int            = {"shards": [f'shard_{shard_idx}.txt' for shard_idx in range(a		)]}
  _snake_case  :    List[str]            = IterableDataset.from_generator(a							,  gen_kwargs=a		)
  _snake_case  :    Dict            = len(list(a		)		)
  assert full_ds.n_shards == world_size * shards_per_node
  _snake_case  :    Dict            = [
      split_dataset_by_node(a							,  rank=a							,  world_size=a		) for rank in range(a		)
  ]
  assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  assert sum(len(list(a		)		) for ds in datasets_per_rank		) == full_size
  assert len({tuple(x.values()		) for ds in datasets_per_rank for x in ds}		) == full_size
def 					a__ ( ):

  """simple docstring"""







  def gen():
    return ({"i": i} for i in range(17		))

  _snake_case  :    Tuple            = 2
  _snake_case  :    str            = IterableDataset.from_generator(a		)
  _snake_case  :    List[Any]            = len(list(a		)		)

  _snake_case  :    Union[str, Any]            = split_dataset_by_node(a							,  rank=0							,  world_size=a		).shuffle(seed=42		)
  assert len(list(a		)		) == 1 + full_size // world_size
  with pytest.raises(a		):
    split_dataset_by_node(a							,  rank=0							,  world_size=a		).shuffle()

  _snake_case  :    str            = split_dataset_by_node(full_ds.shuffle(seed=42		)							,  rank=0							,  world_size=a		)
  assert len(list(a		)		) == 1 + full_size // world_size
  with pytest.raises(a		):
    split_dataset_by_node(full_ds.shuffle()							,  rank=0							,  world_size=a		)
@pytest.mark.parametrize("streaming"							,  [False, True]		)
@require_torch
@pytest.mark.skipif(os.name == "nt"							,  reason="execute_subprocess_async doesn't support windows"		)
@pytest.mark.integration
def 					a__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Union[str, Any]            = 2
  _snake_case  :    Union[str, Any]            = get_torch_dist_unique_port()
  _snake_case  :    Union[str, Any]            = Path(a		).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
  _snake_case  :    Tuple            = f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
  _snake_case  :    Tuple            = f'\n        --streaming={streaming}\n    '.split()
  _snake_case  :    str            = [sys.executable] + distributed_args + args
  execute_subprocess_async(a							,  env=os.environ.copy()		)



@pytest.mark.parametrize(
    "nproc_per_node, num_workers"							,  [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]							,  )
@require_torch
@pytest.mark.skipif(os.name == "nt"							,  reason="execute_subprocess_async doesn't support windows"		)
@pytest.mark.integration
def 					a__ ( a :					Optional[int]							,  a :					int		):

  """simple docstring"""







  _snake_case  :    Tuple            = True
  _snake_case  :    Union[str, Any]            = get_torch_dist_unique_port()
  _snake_case  :    List[str]            = Path(a		).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
  _snake_case  :    Union[str, Any]            = f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
  _snake_case  :    Tuple            = f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
  _snake_case  :    List[str]            = [sys.executable] + distributed_args + args
  execute_subprocess_async(a							,  env=os.environ.copy()		)









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   UpperCamelCase__     	=							"""squeezebert"""





   def __init__(       self					, UpperCAmelCase_=30_522					, UpperCAmelCase_=768					, UpperCAmelCase_=12					, UpperCAmelCase_=12					, UpperCAmelCase_=3_072					, UpperCAmelCase_="gelu"					, UpperCAmelCase_=0.1					, UpperCAmelCase_=0.1					, UpperCAmelCase_=512					, UpperCAmelCase_=2					, UpperCAmelCase_=0.02					, UpperCAmelCase_=1E-1_2					, UpperCAmelCase_=0					, UpperCAmelCase_=768					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=1					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, **UpperCAmelCase_					, )			->   int:




          '''simple docstring'''




          super().__init__(pad_token_id=UpperCAmelCase_					, **UpperCAmelCase_)

          lowercase__:							Union[str, Any]         =     vocab_size
          lowercase__:							int         =     hidden_size
          lowercase__:							int         =     num_hidden_layers
          lowercase__:							List[str]         =     num_attention_heads
          lowercase__:							Any         =     hidden_act
          lowercase__:							List[str]         =     intermediate_size
          lowercase__:							Any         =     hidden_dropout_prob
          lowercase__:							List[Any]         =     attention_probs_dropout_prob
          lowercase__:							Optional[int]         =     max_position_embeddings
          lowercase__:							List[Any]         =     type_vocab_size
          lowercase__:							Union[str, Any]         =     initializer_range
          lowercase__:							str         =     layer_norm_eps
          lowercase__:							Optional[Any]         =     embedding_size
          lowercase__:							Optional[int]         =     q_groups
          lowercase__:							List[str]         =     k_groups
          lowercase__:							Union[str, Any]         =     v_groups
          lowercase__:							str         =     post_attention_groups
          lowercase__:							Optional[int]         =     intermediate_groups
          lowercase__:							List[Any]         =     output_groups







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @property
   def __lowercase							(       self)			->   Mapping[str, Mapping[int, str]]:




          '''simple docstring'''




          if self.task == "multiple-choice":
                 lowercase__:							Optional[int]         =     {0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase__:							Optional[Any]         =     {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ])






