import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard



class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    {
													    "model_details": {
													        "Organization": "testing",
													        "Model date": "today",
													        "Model version": "v2.1, Developed by Test Corp in 2019.",
													        "Architecture": "Convolutional Neural Network.",
													    },
													    "metrics": "BLEU and ROUGE-1",
													    "evaluation_data": {
													        "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
													        "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
													    },
													    "training_data": {
													        "Dataset": "English Wikipedia dump dated 2018-12-01",
													        "Preprocessing": (
													            "Using SentencePiece vocabulary of size 52k tokens. See details on"
													            " https://arxiv.org/pdf/1810.03993.pdf"
													        ),
													    },
													    "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
													}



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    ModelCard.from_dict(self.inputs_dict				)
													self.assertTrue(hasattr(lowercase_      ,				"model_details"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"intended_use"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"factors"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"metrics"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"evaluation_data"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"training_data"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"quantitative_analyses"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"ethical_considerations"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"caveats_and_recommendations"				)				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    ModelCard.from_dict(self.inputs_dict				)
													_snake_case						:     Dict				=    json.loads(modelcard.to_json_string()				)
													for key, value in self.inputs_dict.items():
																			self.assertEqual(obj[key]      ,				lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    ModelCard.from_dict(self.inputs_dict				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																			_snake_case						:     Optional[Any]				=    os.path.join(lowercase_      ,				"modelcard.json"				)
																			model_card_first.to_json_file(lowercase_				)
																			_snake_case						:     Union[str, Any]				=    ModelCard.from_json_file(lowercase_				)

													self.assertEqual(model_card_second.to_dict()      ,				model_card_first.to_dict()				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    ModelCard.from_dict(self.inputs_dict				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																			model_card_first.save_pretrained(lowercase_				)
																			_snake_case						:     int				=    ModelCard.from_pretrained(lowercase_				)

													self.assertEqual(model_card_second.to_dict()      ,				model_card_first.to_dict()				)
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 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    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 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



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

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    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 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



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

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    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 					snake_case						()       ->       Dict:

						'''simple docstring'''



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

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    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()

						_snake_case						:     Optional[int]				=    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 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [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 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



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