



"""simple docstring"""
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_       			=   logging.get_logger(__name__)

def        snake_case   (     A__		):
			UpperCAmelCase_  :				str      =      torch.load(A__			,map_location="cpu"		)
			if "model" in sd.keys():
						UpperCAmelCase_  :				Tuple      =      torch.load(A__			,map_location="cpu"		)["""model"""]

			# pop unnecessary weights
			UpperCAmelCase_  :				Optional[int]      =      [
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(A__		)

			UpperCAmelCase_  :				Optional[Any]      =      {
			    """decoder.project_in_dim.weight""": """decoder.project_in.weight""",
			    """decoder.project_out_dim.weight""": """decoder.project_out.weight""",
			    """decoder.layer_norm.weight""": """decoder.final_layer_norm.weight""",
			    """decoder.layer_norm.bias""": """decoder.final_layer_norm.bias""",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									UpperCAmelCase_  :				Dict      =      sd.pop(A__		)

			UpperCAmelCase_  :				str      =      list(sd.keys()		)
			for key in keys:
						if ".qkv_proj." in key:
									UpperCAmelCase_  :				Any      =      sd[key]
									# We split QKV in separate Q,K,V

									UpperCAmelCase_  :				Dict      =      key.replace(".qkv_proj."			,".q_proj."		)
									UpperCAmelCase_  :				List[str]      =      key.replace(".qkv_proj."			,".k_proj."		)
									UpperCAmelCase_  :				List[str]      =      key.replace(".qkv_proj."			,".v_proj."		)

									UpperCAmelCase_  :				List[str]      =      value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									UpperCAmelCase_  :				Dict      =      torch.split(A__			,depth // 3			,dim=0		)

									UpperCAmelCase_  :				Tuple      =      q
									UpperCAmelCase_  :				Optional[int]      =      k
									UpperCAmelCase_  :				Dict      =      v
									del sd[key]

			return sd

@torch.no_grad()
def        snake_case   (     A__			,A__			,A__=None		):
			UpperCAmelCase_  :				List[Any]      =      load_checkpoint(A__		)

			if config is not None:
						UpperCAmelCase_  :				List[Any]      =      OPTConfig.from_pretrained(A__		)
			else:
						UpperCAmelCase_  :				Dict      =      OPTConfig()

			UpperCAmelCase_  :				List[Any]      =      OPTModel(A__		).half().eval()
			model.load_state_dict(A__		)

			# Check results
			Path(A__		).mkdir(exist_ok=A__		)
			model.save_pretrained(A__		)


if __name__ == "__main__":
			lowerCamelCase_       			=   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--fairseq_path''',
			    type=str,
			    help=(
			        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
			        ''' https://huggingface.co/models?other=opt_metasq'''
			    ),
			)
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
			lowerCamelCase_       			=   parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)











"""simple docstring"""
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCamelCase_       			=   pytest.mark.integration

@pytest.mark.parametrize("path"			,["paws", "csv"]		)
def        snake_case   (     A__			,A__		):
			inspect_dataset(A__			,A__		)
			UpperCAmelCase_  :				Tuple      =      path + ".py"
			assert script_name in os.listdir(A__		)
			assert "__pycache__" not in os.listdir(A__		)

@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"		)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"		)
@pytest.mark.parametrize("path"			,["accuracy"]		)
def        snake_case   (     A__			,A__		):
			inspect_metric(A__			,A__		)
			UpperCAmelCase_  :				List[str]      =      path + ".py"
			assert script_name in os.listdir(A__		)
			assert "__pycache__" not in os.listdir(A__		)

@pytest.mark.parametrize(
    "path, config_name, expected_splits"			,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      get_dataset_config_info(A__			,config_name=A__		)
			assert info.config_name == config_name
			assert list(info.splits.keys()		) == expected_splits

@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,[
        ("paws", None, ValueError),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			with pytest.raises(A__		):
						get_dataset_config_info(A__			,config_name=A__		)

@pytest.mark.parametrize(
    "path, expected"			,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]			,)
def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				str      =      get_dataset_config_names(A__		)
			assert expected in config_names

@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"			,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      get_dataset_infos(A__		)
			assert list(infos.keys()		) == expected_configs
			UpperCAmelCase_  :				List[str]      =      expected_configs[0]
			assert expected_config in infos
			UpperCAmelCase_  :				int      =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()		) == expected_splits_in_first_config

@pytest.mark.parametrize(
    "path, expected_config, expected_splits"			,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Optional[Any]      =      get_dataset_infos(A__		)
			assert expected_config in infos
			UpperCAmelCase_  :				Union[str, Any]      =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()		) == expected_splits




@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,[
        ("paws", None, ValueError),
    ]			,)
def        snake_case   (     A__			,A__			,A__		):
			with pytest.raises(A__		):
						get_dataset_split_names(A__			,config_name=A__		)



