import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		test_file.split(os.path.sep       )
			if components[0:2] != ["tests", "models"]:
						raise ValueError(
						    '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
						    f'{test_file} instead.'       )
			lowercase							=		components[-1]
			if not test_fn.endswith('''py'''       ):
						raise ValueError(f'`test_file` should be a python file. Got {test_fn} instead.'       )
			if not test_fn.startswith('''test_modeling_'''       ):
						raise ValueError(
						    f'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'       )

			lowercase							=		components[:-1] + [test_fn.replace('''.py''',		''''''       )]
			lowercase							=		'''.'''.join(UpperCAmelCase       )

			return test_module_path






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[Any]:



			"""simple docstring"""







			lowercase							=		get_module_path(UpperCAmelCase       )
			lowercase							=		importlib.import_module(UpperCAmelCase       )

			return test_module






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		[]
			lowercase							=		get_test_module(UpperCAmelCase       )
			for attr in dir(UpperCAmelCase       ):
						if attr.endswith('''ModelTester'''       ):
									tester_classes.append(getattr(UpperCAmelCase,		UpperCAmelCase       )       )

    # sort with class names
			return sorted(UpperCAmelCase,		key=lambda UpperCAmelCase       : x.__name__       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		[]
			lowercase							=		get_test_module(UpperCAmelCase       )
			for attr in dir(UpperCAmelCase       ):
						lowercase							=		getattr(UpperCAmelCase,		UpperCAmelCase       )
						# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
						# `all_model_classes` is not empty (which also excludes other special classes).
						lowercase							=		getattr(UpperCAmelCase,		'''all_model_classes''',		[]       )
						if len(UpperCAmelCase       ) > 0:
									test_classes.append(UpperCAmelCase       )

    # sort with class names
			return sorted(UpperCAmelCase,		key=lambda UpperCAmelCase       : x.__name__       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Any:



			"""simple docstring"""







			lowercase							=		get_test_classes(UpperCAmelCase       )
			lowercase							=		set()
			for test_class in test_classes:
						model_classes.update(test_class.all_model_classes       )

			# sort with class names
			return sorted(UpperCAmelCase,		key=lambda UpperCAmelCase       : x.__name__       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		test_class()
			if hasattr(UpperCAmelCase,		'''setUp'''       ):
						test.setUp()

			lowercase							=		None
			if hasattr(UpperCAmelCase,		'''model_tester'''       ):
						# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
						if test.model_tester is not None:
									lowercase							=		test.model_tester.__class__

			return model_tester






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Optional[Any]:



			"""simple docstring"""







			lowercase							=		get_test_classes(UpperCAmelCase       )

			lowercase							=		[]
			for test_class in test_classes:
						if model_class in test_class.all_model_classes:
									target_test_classes.append(UpperCAmelCase       )

    # sort with class names
			return sorted(UpperCAmelCase,		key=lambda UpperCAmelCase       : x.__name__       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		get_test_classes_for_model(UpperCAmelCase,		UpperCAmelCase       )

			lowercase							=		[]
			for test_class in test_classes:
						lowercase							=		get_model_tester_from_test_class(UpperCAmelCase       )
						if tester_class is not None:
									tester_classes.append(UpperCAmelCase       )

    # sort with class names
			return sorted(UpperCAmelCase,		key=lambda UpperCAmelCase       : x.__name__       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			lowercase							=		get_test_classes(UpperCAmelCase       )
			lowercase							=		{test_class: get_model_tester_from_test_class(UpperCAmelCase       ) for test_class in test_classes}
			return test_tester_mapping






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		get_model_classes(UpperCAmelCase       )
			lowercase							=		{
			    model_class: get_test_classes_for_model(UpperCAmelCase,		UpperCAmelCase       ) for model_class in model_classes
			}
			return model_test_mapping






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Any:



			"""simple docstring"""







			lowercase							=		get_model_classes(UpperCAmelCase       )
			lowercase							=		{
			    model_class: get_tester_classes_for_model(UpperCAmelCase,		UpperCAmelCase       ) for model_class in model_classes
			}
			return model_to_tester_mapping






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Any:



			"""simple docstring"""







			if isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						return o
			elif isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						return o.__name__
			elif isinstance(UpperCAmelCase,		(list, tuple)       ):
						return [to_json(UpperCAmelCase       ) for x in o]
			elif isinstance(UpperCAmelCase,		UpperCAmelCase       ):
						return {to_json(UpperCAmelCase       ): to_json(UpperCAmelCase       ) for k, v in o.items()}
			else:
						return o






import argparse
from pathlib import Path

import torch

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


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






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )
			if "model" in sd.keys():
						lowercase							=		torch.load(UpperCAmelCase,		map_location='''cpu'''       )['''model''']

			# pop unnecessary weights
			lowercase							=		[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(UpperCAmelCase       )

			lowercase							=		{
			    '''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:
									lowercase							=		sd.pop(UpperCAmelCase       )

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

									lowercase							=		key.replace('''.qkv_proj.''',		'''.q_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.k_proj.'''       )
									lowercase							=		key.replace('''.qkv_proj.''',		'''.v_proj.'''       )

									lowercase							=		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
									lowercase		,lowercase		,lowercase							=		torch.split(UpperCAmelCase,		depth // 3,		dim=0       )

									lowercase							=		q
									lowercase							=		k
									lowercase							=		v
									del sd[key]

			return sd






@torch.no_grad()
def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None       )->       int:



			"""simple docstring"""







			lowercase							=		load_checkpoint(UpperCAmelCase       )

			if config is not None:
						lowercase							=		OPTConfig.from_pretrained(UpperCAmelCase       )
			else:
						lowercase							=		OPTConfig()

			lowercase							=		OPTModel(UpperCAmelCase       ).half().eval()
			model.load_state_dict(UpperCAmelCase       )

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


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





