

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 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Dict						       =			Dataset.from_dict({'''i''': range(17				)}				)
	lowerCAmelCase__			:		Dict						       =			len(__snake_case				)
	lowerCAmelCase__			:		Any						       =			3
	lowerCAmelCase__			:		int						       =			[
	    split_dataset_by_node(__snake_case		,  rank=__snake_case		,  world_size=__snake_case				) for rank in range(__snake_case				)
	]
	assert sum(len(__snake_case				) 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 						__SCREAMING_SNAKE_CASE						(   ):
	def gen():
		return ({"i": i} for i in range(17				))

	lowerCAmelCase__			:		int						       =			3
	lowerCAmelCase__			:		Optional[int]						       =			IterableDataset.from_generator(__snake_case				)
	lowerCAmelCase__			:		Tuple						       =			len(list(__snake_case				)				)
	lowerCAmelCase__			:		str						       =			[
	    split_dataset_by_node(__snake_case		,  rank=__snake_case		,  world_size=__snake_case				) for rank in range(__snake_case				)
	]
	assert sum(len(list(__snake_case				)				) 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 						__SCREAMING_SNAKE_CASE						(   A_				):
	def gen(A_				):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17				))

	lowerCAmelCase__			:		int						       =			3
	lowerCAmelCase__			:		Optional[Any]						       =			shards_per_node * world_size
	lowerCAmelCase__			:		Dict						       =			{'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(__snake_case				)]}
	lowerCAmelCase__			:		Union[str, Any]						       =			IterableDataset.from_generator(__snake_case		,  gen_kwargs=__snake_case				)
	lowerCAmelCase__			:		List[str]						       =			len(list(__snake_case				)				)
	assert full_ds.n_shards == world_size * shards_per_node
	lowerCAmelCase__			:		List[str]						       =			[
	    split_dataset_by_node(__snake_case		,  rank=__snake_case		,  world_size=__snake_case				) for rank in range(__snake_case				)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(__snake_case				)				) 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 						__SCREAMING_SNAKE_CASE						(   ):
	def gen():
		return ({"i": i} for i in range(17				))

	lowerCAmelCase__			:		Union[str, Any]						       =			2
	lowerCAmelCase__			:		List[str]						       =			IterableDataset.from_generator(__snake_case				)
	lowerCAmelCase__			:		int						       =			len(list(__snake_case				)				)

	lowerCAmelCase__			:		Optional[Any]						       =			split_dataset_by_node(__snake_case		,  rank=0		,  world_size=__snake_case				).shuffle(seed=42				)
	assert len(list(__snake_case				)				) == 1 + full_size // world_size
	with pytest.raises(__snake_case				):
		split_dataset_by_node(__snake_case		,  rank=0		,  world_size=__snake_case				).shuffle()

	lowerCAmelCase__			:		List[str]						       =			split_dataset_by_node(full_ds.shuffle(seed=42				)		,  rank=0		,  world_size=__snake_case				)
	assert len(list(__snake_case				)				) == 1 + full_size // world_size
	with pytest.raises(__snake_case				):
		split_dataset_by_node(full_ds.shuffle()		,  rank=0		,  world_size=__snake_case				)

@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 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		int						       =			2
	lowerCAmelCase__			:		str						       =			get_torch_dist_unique_port()
	lowerCAmelCase__			:		List[str]						       =			Path(__snake_case				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
	lowerCAmelCase__			:		Tuple						       =			f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
	lowerCAmelCase__			:		Tuple						       =			f'\n        --streaming={streaming}\n    '.split()
	lowerCAmelCase__			:		Tuple						       =			[sys.executable] + distributed_args + args
	execute_subprocess_async(__snake_case		,  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 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	lowerCAmelCase__			:		List[Any]						       =			True
	lowerCAmelCase__			:		List[str]						       =			get_torch_dist_unique_port()
	lowerCAmelCase__			:		Dict						       =			Path(__snake_case				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
	lowerCAmelCase__			:		int						       =			f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
	lowerCAmelCase__			:		Optional[int]						       =			f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
	lowerCAmelCase__			:		str						       =			[sys.executable] + distributed_args + args
	execute_subprocess_async(__snake_case		,  env=os.environ.copy()				)







"""simple docstring"""





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCamelCase						:   str							   =	logging.get_logger(__name__)

__UpperCamelCase						:   Union[str, Any]							   =	{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= "gpt2"
					lowercase__       				= ["past_key_values"]
					lowercase__       				= {
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__( self    :       List[str]     ,lowercase_    :       Dict=5_0_2_5_7     ,lowercase_    :       Dict=1_0_2_4     ,lowercase_    :       Any=7_6_8     ,lowercase_    :       str=1_2     ,lowercase_    :       int=1_2     ,lowercase_    :       List[Any]=None     ,lowercase_    :       Dict="gelu_new"     ,lowercase_    :       Any=0.1     ,lowercase_    :       Optional[Any]=0.1     ,lowercase_    :       Tuple=0.1     ,lowercase_    :       Dict=1E-5     ,lowercase_    :       List[Any]=0.02     ,lowercase_    :       Tuple="cls_index"     ,lowercase_    :       Optional[int]=True     ,lowercase_    :       Union[str, Any]=None     ,lowercase_    :       Optional[Any]=True     ,lowercase_    :       List[Any]=0.1     ,lowercase_    :       List[str]=True     ,lowercase_    :       int=True     ,lowercase_    :       Optional[Any]=5_0_2_5_6     ,lowercase_    :       Dict=5_0_2_5_6     ,lowercase_    :       Union[str, Any]=False     ,lowercase_    :       Optional[Any]=False     ,**lowercase_    :       Dict     ,):
						lowerCAmelCase__			:		Tuple						       =			vocab_size
						lowerCAmelCase__			:		List[Any]						       =			n_positions
						lowerCAmelCase__			:		Optional[int]						       =			n_embd
						lowerCAmelCase__			:		Tuple						       =			n_layer
						lowerCAmelCase__			:		Optional[Any]						       =			n_head
						lowerCAmelCase__			:		List[Any]						       =			n_inner
						lowerCAmelCase__			:		Any						       =			activation_function
						lowerCAmelCase__			:		Optional[int]						       =			resid_pdrop
						lowerCAmelCase__			:		str						       =			embd_pdrop
						lowerCAmelCase__			:		Tuple						       =			attn_pdrop
						lowerCAmelCase__			:		List[str]						       =			layer_norm_epsilon
						lowerCAmelCase__			:		Union[str, Any]						       =			initializer_range
						lowerCAmelCase__			:		Optional[int]						       =			summary_type
						lowerCAmelCase__			:		Optional[Any]						       =			summary_use_proj
						lowerCAmelCase__			:		List[str]						       =			summary_activation
						lowerCAmelCase__			:		Optional[int]						       =			summary_first_dropout
						lowerCAmelCase__			:		Dict						       =			summary_proj_to_labels
						lowerCAmelCase__			:		Dict						       =			scale_attn_weights
						lowerCAmelCase__			:		Dict						       =			use_cache
						lowerCAmelCase__			:		List[Any]						       =			scale_attn_by_inverse_layer_idx
						lowerCAmelCase__			:		int						       =			reorder_and_upcast_attn

						lowerCAmelCase__			:		List[str]						       =			bos_token_id
						lowerCAmelCase__			:		List[str]						       =			eos_token_id

						super().__init__(bos_token_id=lowercase_     ,eos_token_id=lowercase_     ,**lowercase_	)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       Optional[int]     ,lowercase_    :       PretrainedConfig     ,lowercase_    :       str = "default"     ,lowercase_    :       List[PatchingSpec] = None     ,lowercase_    :       bool = False     ,):
						super().__init__(lowercase_     ,task=lowercase_     ,patching_specs=lowercase_     ,use_past=lowercase_	)
						if not getattr(self._config     ,'''pad_token_id'''     ,lowercase_	):
							# TODO: how to do that better?
							lowerCAmelCase__			:		int						       =			0







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
						if self.use_past:
							self.fill_with_past_key_values_(lowercase_     ,direction='''inputs'''	)
							lowerCAmelCase__			:		str						       =			{0: '''batch''', 1: '''past_sequence + sequence'''}
						else:
							lowerCAmelCase__			:		List[str]						       =			{0: '''batch''', 1: '''sequence'''}

						return common_inputs







					@property
					def  __lowerCAmelCase				( self    :       Any	):
						return self._config.n_layer







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						return self._config.n_head







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       PreTrainedTokenizer     ,lowercase_    :       int = -1     ,lowercase_    :       int = -1     ,lowercase_    :       bool = False     ,lowercase_    :       Optional[TensorType] = None     ,):
						lowerCAmelCase__			:		Any						       =			super(lowercase_     ,self	).generate_dummy_inputs(
						    lowercase_     ,batch_size=lowercase_     ,seq_length=lowercase_     ,is_pair=lowercase_     ,framework=lowercase_	)

						# We need to order the input in the way they appears in the forward()
						lowerCAmelCase__			:		List[Any]						       =			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
							else:
								import torch

								lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								lowerCAmelCase__			:		Dict						       =			seqlen + 2
								lowerCAmelCase__			:		Optional[Any]						       =			(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								lowerCAmelCase__			:		Tuple						       =			[
								    (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(self.num_layers	)
								]

						lowerCAmelCase__			:		Any						       =			common_inputs['''attention_mask''']
						if self.use_past:
							lowerCAmelCase__			:		Optional[Any]						       =			ordered_inputs['''attention_mask'''].dtype
							lowerCAmelCase__			:		Union[str, Any]						       =			torch.cat(
							    [ordered_inputs['''attention_mask'''], torch.ones(lowercase_     ,lowercase_     ,dtype=lowercase_	)]     ,dim=1	)

						return ordered_inputs







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return 1_3




