

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
A_ :				int						 =    logging.get_logger(__name__)

def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   str:
	A__							:					Dict       =	torch.load(lowercase_					,   map_location="""cpu"""      )
	if "model" in sd.keys():
		A__							:					List[Any]       =	torch.load(lowercase_					,   map_location="""cpu"""      )["""model"""]

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

	A__							:					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:
			A__							:					List[Any]       =	sd.pop(lowercase_      )

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

			A__							:					List[str]       =	key.replace(""".qkv_proj."""					,   """.q_proj."""      )
			A__							:					Optional[int]       =	key.replace(""".qkv_proj."""					,   """.k_proj."""      )
			A__							:					Any       =	key.replace(""".qkv_proj."""					,   """.v_proj."""      )

			A__							:					List[Any]       =	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
			A__     ,     A__     ,     A__							:					int       =	torch.split(lowercase_					,   depth // 3					,   dim=0      )

			A__							:					Any       =	q
			A__							:					Optional[int]       =	k
			A__							:					List[Any]       =	v
			del sd[key]

	return sd

@torch.no_grad()
def 		UpperCamelCase      (lowercase_:				Union[str, Any]					,   lowercase_:				List[str]					,   lowercase_:				Optional[Any]=None      )  ->   Any:
	A__							:					Union[str, Any]       =	load_checkpoint(lowercase_      )

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

	A__							:					Optional[Any]       =	OPTModel(lowercase_      ).half().eval()
	model.load_state_dict(lowercase_      )

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


if __name__ == "__main__":
	A_ :				int						 =    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.')
	A_ :				Dict						 =    parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam



class 					_a		(datasets.BeamBasedBuilder       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						return datasets.DatasetInfo(
						    features=datasets.Features({"""content""": datasets.Value("""string"""    )}    )					, supervised_keys=A__					, )






					def 						__A    (				self					, A__					, A__    ):
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={"""examples""": get_test_dummy_examples()}    )]






					def 						__A    (				self					, A__					, A__    ):
						import apache_beam as beam

						return pipeline | "Load Examples" >> beam.Create(A__    )



class 					_a		(datasets.BeamBasedBuilder       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						return datasets.DatasetInfo(
						    features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""    )}    )}    )					, supervised_keys=A__					, )






					def 						__A    (				self					, A__					, A__    ):
						return [
						    datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={"""examples""": get_test_nested_examples()}    )
						]






					def 						__A    (				self					, A__					, A__    ):
						import apache_beam as beam

						return pipeline | "Load Examples" >> beam.Create(A__    )



def 		UpperCamelCase      ()  ->   Dict:
	return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]      )]

def 		UpperCamelCase      ()  ->   Tuple:
	return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]      )]



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					@require_beam
					def 						__A    (				self    ):
						A__							:					Dict       =	len(get_test_dummy_examples()    )
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					int       =	DummyBeamDataset(cache_dir=A__					, beam_runner="""DirectRunner"""    )
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train.arrow"""    )    )    )
							self.assertDictEqual(builder.info.features					, datasets.Features({"""content""": datasets.Value("""string"""    )}    )    )
							A__							:					int       =	builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows					, A__    )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					, A__    )
							self.assertDictEqual(dset["""train"""][0]					, get_test_dummy_examples()[0][1]    )
							self.assertDictEqual(
							    dset["""train"""][expected_num_examples - 1]					, get_test_dummy_examples()[expected_num_examples - 1][1]    )
							self.assertTrue(
							    os.path.exists(os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, """dataset_info.json"""    )    )    )
							del dset






					@require_beam
					def 						__A    (				self    ):
						import apache_beam as beam

						A__							:					int       =	beam.io.parquetio.WriteToParquet

						A__							:					List[str]       =	len(get_test_dummy_examples()    )
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					str       =	DummyBeamDataset(cache_dir=A__					, beam_runner="""DirectRunner"""    )
							with patch("""apache_beam.io.parquetio.WriteToParquet"""    ) as write_parquet_mock:
								A__							:					Optional[Any]       =	partial(A__					, num_shards=2    )
								builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train-00000-of-00002.arrow"""    )    )    )
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train-00000-of-00002.arrow"""    )    )    )
							self.assertDictEqual(builder.info.features					, datasets.Features({"""content""": datasets.Value("""string"""    )}    )    )
							A__							:					Optional[int]       =	builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows					, A__    )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					, A__    )
							# Order is not preserved when sharding, so we just check that all the elements are there
							self.assertListEqual(sorted(dset["""train"""]["""content"""]    )					, sorted(["""foo""", """bar""", """foobar"""]    )    )
							self.assertTrue(
							    os.path.exists(os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, """dataset_info.json"""    )    )    )
							del dset






					@require_beam
					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					int       =	DummyBeamDataset(cache_dir=A__    )
							self.assertRaises(datasets.builder.MissingBeamOptions					, builder.download_and_prepare    )






					@require_beam
					def 						__A    (				self    ):
						A__							:					List[Any]       =	len(get_test_nested_examples()    )
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					Optional[int]       =	NestedBeamDataset(cache_dir=A__					, beam_runner="""DirectRunner"""    )
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train.arrow"""    )    )    )
							self.assertDictEqual(
							    builder.info.features					, datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""    )}    )}    )    )
							A__							:					Optional[int]       =	builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows					, A__    )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					, A__    )
							self.assertDictEqual(dset["""train"""][0]					, get_test_nested_examples()[0][1]    )
							self.assertDictEqual(
							    dset["""train"""][expected_num_examples - 1]					, get_test_nested_examples()[expected_num_examples - 1][1]    )
							self.assertTrue(
							    os.path.exists(os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, """dataset_info.json"""    )    )    )
							del dset



