
import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 	A__				(	unittest.TestCase      ):
				lowercase            =    EsmTokenizer






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							super().setUp()
							lowerCAmelCase__ : Any   =						tempfile.mkdtemp()
							# fmt: off
							lowerCAmelCase__ : List[str]   =						["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
							# fmt: on
							lowerCAmelCase__ : List[str]   =						os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']  )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'  ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]  )  )






				def 				_lowerCamelCase						(		self   : Optional[Any]       ,	**a   : str  ):






							'''simple docstring'''







							return [self.get_tokenizer(**lowerCamelCase__  )]






				def 				_lowerCamelCase						(		self   : int       ,	**a   : Optional[int]  ):






							'''simple docstring'''







							return self.tokenizer_class.from_pretrained(self.tmpdirname       ,	**lowerCamelCase__  )






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						self.tokenizer_class(self.vocab_file  )

							lowerCAmelCase__ : Optional[Any]   =						tokenizer.tokenize('LAGVS'  )
							self.assertListEqual(lowerCamelCase__       ,	['L', 'A', 'G', 'V', 'S']  )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__  )       ,	[4, 5, 6, 7, 8]  )






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						self.tokenizer_class(self.vocab_file  )

							lowerCAmelCase__ : List[str]   =						"LAGVS"
							self.assertListEqual(tokenizer.encode(lowerCamelCase__  )       ,	[0, 4, 5, 6, 7, 8, 2]  )






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						self.tokenizer_class(self.vocab_file  )

							lowerCAmelCase__ : Union[str, Any]   =						["LAGVS", "WCB"]
							lowerCAmelCase__ : Tuple   =						tokenizer(lowerCamelCase__       ,	padding=lowerCamelCase__  )["input_ids"]

							self.assertListEqual(lowerCamelCase__       ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]  )






				def 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						self.tokenizer_class(self.vocab_file  )

							lowerCAmelCase__ : List[Any]   =						["LAGVS", "WCB"]
							lowerCAmelCase__ : List[Any]   =						tokenizer(lowerCamelCase__       ,	padding=lowerCamelCase__  )["input_ids"]

							self.assertListEqual(lowerCamelCase__       ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]  )






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						self.get_tokenizers(fast=lowerCamelCase__  )
							for tokenizer in tokenizers:
										with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
													lowerCAmelCase__ : int   =						"<unk>"
													lowerCAmelCase__ : Any   =						"<mask>"

													lowerCAmelCase__ : Optional[Any]   =						tokenizer.tokenize(lowerCamelCase__  )
													lowerCAmelCase__ : Optional[Any]   =						tokenizer.tokenize(lowerCamelCase__  )

													self.assertEqual(len(lowerCamelCase__  )       ,	1  )
													self.assertEqual(len(lowerCamelCase__  )       ,	1  )
													self.assertEqual(token_a[0]       ,	lowerCamelCase__  )
													self.assertEqual(token_a[0]       ,	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			():
		_UpperCAmelCase		:							Optional[int]             =   Dataset.from_dict({"i": range(17 )} )
		_UpperCAmelCase		:							int             =   len(__lowerCAmelCase )
		_UpperCAmelCase		:							Dict             =   3
		_UpperCAmelCase		:							str             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert sum(len(__lowerCAmelCase ) 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 ))

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

		_UpperCAmelCase		:							Tuple             =   3
		_UpperCAmelCase		:							Tuple             =   shards_per_node * world_size
		_UpperCAmelCase		:							Optional[int]             =   {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowerCAmelCase )]}
		_UpperCAmelCase		:							int             =   IterableDataset.from_generator(__lowerCAmelCase   ,			gen_kwargs=__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   len(list(__lowerCAmelCase ) )
		assert full_ds.n_shards == world_size * shards_per_node
		_UpperCAmelCase		:							Optional[Any]             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
		assert sum(len(list(__lowerCAmelCase ) ) 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 ))

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

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

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






@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			(__lowerCAmelCase ):
		_UpperCAmelCase		:							Union[str, Any]             =   2
		_UpperCAmelCase		:							Dict             =   get_torch_dist_unique_port()
		_UpperCAmelCase		:							int             =   Path(__lowerCAmelCase ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		_UpperCAmelCase		:							List[Any]             =   F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
		_UpperCAmelCase		:							List[str]             =   F"""
        --streaming={streaming}
    """.split()
		_UpperCAmelCase		:							Tuple             =   [sys.executable] + distributed_args + args
		execute_subprocess_async(__lowerCAmelCase   ,			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			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Tuple             =   True
		_UpperCAmelCase		:							int             =   get_torch_dist_unique_port()
		_UpperCAmelCase		:							Optional[int]             =   Path(__lowerCAmelCase ).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
		_UpperCAmelCase		:							List[str]             =   F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
		_UpperCAmelCase		:							int             =   F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
		_UpperCAmelCase		:							Optional[int]             =   [sys.executable] + distributed_args + args
		execute_subprocess_async(__lowerCAmelCase   ,			env=os.environ.copy() )



