




'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case   :   List[str]     							=	logging.get_logger(__name__)

_snake_case   :   Optional[int]     							=	[
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]
def 	snake_case_					(UpperCamelCase :						List[str]   ):





							'''simple docstring'''


							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														_a			  =       k.replace(UpperCamelCase				,		UpperCamelCase   )

							if k.startswith('''encoder'''   ):
														_a			  =       k.replace('''.attn'''				,		'''.self_attn'''   )
														_a			  =       k.replace('''norm1'''				,		'''self_attn_layer_norm'''   )
														_a			  =       k.replace('''norm2'''				,		'''final_layer_norm'''   )
							elif k.startswith('''decoder'''   ):
														_a			  =       k.replace('''norm1'''				,		'''self_attn_layer_norm'''   )
														_a			  =       k.replace('''norm2'''				,		'''encoder_attn_layer_norm'''   )
														_a			  =       k.replace('''norm3'''				,		'''final_layer_norm'''   )
							return k
def 	snake_case_					(UpperCamelCase :						Optional[Any]   ):





							'''simple docstring'''


							_a			  =       [
							    '''model.encoder.layernorm_embedding.weight''',
							    '''model.encoder.layernorm_embedding.bias''',
							    '''model.decoder.layernorm_embedding.weight''',
							    '''model.decoder.layernorm_embedding.bias''',
							]
							for k in keys:
														_a			  =       sd.pop(UpperCamelCase   )
														_a			  =       k.replace('''layernorm_embedding'''				,		'''layer_norm'''   )
														assert new_k not in sd
														_a			  =       v


_snake_case   :   List[str]     							=	['START']
@torch.no_grad()
def 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						int				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							_a			  =       torch.load(UpperCamelCase				,		map_location='''cpu'''   )
							_a			  =       model['''model''']
							_a			  =       BlenderbotConfig.from_json_file(UpperCamelCase   )
							_a			  =       BlenderbotForConditionalGeneration(UpperCamelCase   )
							_a			  =       m.model.state_dict().keys()
							_a			  =       []
							_a			  =       {}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

														_a			  =       rename_state_dict_key(UpperCamelCase   )
														if new_k not in valid_keys:
																					failures.append([k, new_k]   )
														else:
																					_a			  =       v
							if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
														rename_layernorm_keys(UpperCamelCase   )
							m.model.load_state_dict(UpperCamelCase				,		strict=UpperCamelCase   )
							m.half()
							m.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
				_snake_case   :   int     							=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
				parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
				parser.add_argument(
				    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
				)
				_snake_case   :   Optional[int]     							=	parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







'''simple docstring'''


import os

import pytest

from transformers.dynamic_module_utils import get_imports


_snake_case   :   List[str]     							=	'\nimport os\n'

_snake_case   :   Dict     							=	'\ndef foo():\n    import os\n    return False\n'

_snake_case   :   List[Any]     							=	'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

_snake_case   :   List[str]     							=	'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

_snake_case   :   Union[str, Any]     							=	[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case'''				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       os.path.join(UpperCamelCase				,		'''test_file.py'''   )
							with open(UpperCamelCase				,		'''w'''   ) as _tmp_file:
														_tmp_file.write(UpperCamelCase   )

							_a			  =       get_imports(UpperCamelCase   )
							assert parsed_imports == ["os"]

