

'''simple docstring'''

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


lowercase__            =     logging.get_logger(__name__)

lowercase__            =     {
    """google/reformer-crime-and-punishment""": (
        """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"""
    ),
    """google/reformer-enwik8""": """https://huggingface.co/google/reformer-enwik8/resolve/main/config.json""",
}




class 			A_					(							_lowerCamelCase				):






     '''simple docstring'''



     UpperCAmelCase_		:      Optional[int]    						=    """reformer"""
     UpperCAmelCase_		:      Tuple    						=    ["""past_buckets_states"""]
     UpperCAmelCase_		:      Optional[int]    						=    {}


     def __init__(						self					:	Optional[int]	,     lowercase_					:	List[str]=64	,     lowercase_					:	Dict=["local", "lsh", "local", "lsh", "local", "lsh"]	,     lowercase_					:	List[str]=1.0	,     lowercase_					:	Dict=True	,     lowercase_					:	List[Any]=[64, 64]	,     lowercase_					:	Optional[int]=[64, 192]	,     lowercase_					:	int=0	,     lowercase_					:	Union[str, Any]=2	,     lowercase_					:	int=512	,     lowercase_					:	Dict=None	,     lowercase_					:	Dict="relu"	,     lowercase_					:	Optional[Any]=0.05	,     lowercase_					:	str=256	,     lowercase_					:	Any=0.02	,     lowercase_					:	List[Any]=False	,     lowercase_					:	List[str]=1E-12	,     lowercase_					:	Any=1	,     lowercase_					:	Any=0	,     lowercase_					:	Any=0.05	,     lowercase_					:	List[Any]=64	,     lowercase_					:	List[str]=64	,     lowercase_					:	Tuple=0.0	,     lowercase_					:	List[Any]=1	,     lowercase_					:	int=0	,     lowercase_					:	Tuple=4_096	,     lowercase_					:	Dict=12	,     lowercase_					:	List[str]=None	,     lowercase_					:	Optional[int]=1	,     lowercase_					:	str=0	,     lowercase_					:	str=320	,     lowercase_					:	str=False	,     lowercase_					:	Optional[int]=True	,     lowercase_					:	int=None	,     **lowercase_					:	Union[str, Any]	,     )		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					hash_seed
      UpperCAmelCase			:      Any		   =					vocab_size
      UpperCAmelCase			:      int		   =					attention_head_size
      UpperCAmelCase			:      Union[str, Any]		   =					hidden_size
      UpperCAmelCase			:      Dict		   =					num_attention_heads
      UpperCAmelCase			:      Union[str, Any]		   =					num_hashes
      UpperCAmelCase			:      List[Any]		   =					len(__lowerCamelCase			)
      UpperCAmelCase			:      Union[str, Any]		   =					tuple(__lowerCamelCase			) if isinstance(__lowerCamelCase	,     __lowerCamelCase			) else num_buckets
      UpperCAmelCase			:      List[str]		   =					lsh_attn_chunk_length
      UpperCAmelCase			:      Tuple		   =					local_attn_chunk_length
      UpperCAmelCase			:      int		   =					lsh_num_chunks_after
      UpperCAmelCase			:      Tuple		   =					lsh_num_chunks_before
      UpperCAmelCase			:      Any		   =					local_num_chunks_after
      UpperCAmelCase			:      Optional[int]		   =					local_num_chunks_before
      UpperCAmelCase			:      Optional[Any]		   =					hidden_act
      UpperCAmelCase			:      Dict		   =					feed_forward_size
      UpperCAmelCase			:      Optional[Any]		   =					hidden_dropout_prob
      UpperCAmelCase			:      Dict		   =					lsh_attention_probs_dropout_prob
      UpperCAmelCase			:      Any		   =					local_attention_probs_dropout_prob
      UpperCAmelCase			:      Optional[Any]		   =					max_position_embeddings
      UpperCAmelCase			:      List[str]		   =					initializer_range
      UpperCAmelCase			:      Optional[Any]		   =					layer_norm_eps
      UpperCAmelCase			:      List[str]		   =					axial_pos_embds
      UpperCAmelCase			:      List[str]		   =					tuple(__lowerCamelCase			)
      UpperCAmelCase			:      Optional[Any]		   =					tuple(__lowerCamelCase			)
      UpperCAmelCase			:      Tuple		   =					axial_norm_std
      UpperCAmelCase			:      List[str]		   =					chunk_size_lm_head
      UpperCAmelCase			:      int		   =					attn_layers
      UpperCAmelCase			:      Tuple		   =					use_cache
      UpperCAmelCase			:      Optional[int]		   =					classifier_dropout
      super().__init__(
          pad_token_id=__lowerCamelCase	,     eos_token_id=__lowerCamelCase	,     is_decoder=__lowerCamelCase	,     tie_word_embeddings=__lowerCamelCase	,     **__lowerCamelCase	,     )












'''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  lowerCamelCase_  (     ):
 lowerCamelCase__	=  Dataset.from_dict({"i": range(17)})
 lowerCamelCase__	=  len(lowercase__)
 lowerCamelCase__	=  3
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(lowercase__) 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  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert sum(len(list(lowercase__)) 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  lowerCamelCase_  (     lowercase__):
 def gen(lowercase__):
  for shard in shards:
   yield from ({"i": i, "shard": shard} for i in range(17))

 lowerCamelCase__	=  3
 lowerCamelCase__	=  shards_per_node * world_size
 lowerCamelCase__	=  {"shards": [F'''shard_{shard_idx}.txt''' for shard_idx in range(lowercase__)]}
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__					,  gen_kwargs=lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))
 assert full_ds.n_shards == world_size * shards_per_node
 lowerCamelCase__	=  [
     split_dataset_by_node(lowercase__					,  rank=lowercase__					,  world_size=lowercase__) for rank in range(lowercase__)
 ]
 assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
 assert sum(len(list(lowercase__)) 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  lowerCamelCase_  (     ):
 def gen():
  return ({"i": i} for i in range(17))

 lowerCamelCase__	=  2
 lowerCamelCase__	=  IterableDataset.from_generator(lowercase__)
 lowerCamelCase__	=  len(list(lowercase__))

 lowerCamelCase__	=  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle(seed=42)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(lowercase__					,  rank=0					,  world_size=lowercase__).shuffle()

 lowerCamelCase__	=  split_dataset_by_node(full_ds.shuffle(seed=42)					,  rank=0					,  world_size=lowercase__)
 assert len(list(lowercase__)) == 1 + full_size // world_size
 with pytest.raises(lowercase__):
  split_dataset_by_node(full_ds.shuffle()					,  rank=0					,  world_size=lowercase__)






@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  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  2
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  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  lowerCamelCase_  (     lowercase__					,  lowercase__):
 lowerCamelCase__	=  True
 lowerCamelCase__	=  get_torch_dist_unique_port()
 lowerCamelCase__	=  Path(lowercase__).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
 lowerCamelCase__	=  F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
 lowerCamelCase__	=  F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
 lowerCamelCase__	=  [sys.executable] + distributed_args + args
 execute_subprocess_async(lowercase__					,  env=os.environ.copy())

