




'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					UpperCamelCase_(     snake_case							:	int					):

							'''simple docstring'''






							snake_case_      =				[
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(_UpperCamelCase							,    _UpperCamelCase					)


def 					UpperCamelCase_(     snake_case							:	Any					):

							'''simple docstring'''






							snake_case_ ,				snake_case_      =				emb.weight.shape
							snake_case_      =				nn.Linear(_UpperCamelCase							,    _UpperCamelCase							,    bias=_UpperCamelCase					)
							snake_case_      =				emb.weight.data
							return lin_layer


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]					):

							'''simple docstring'''






							snake_case_      =				torch.load(_UpperCamelCase							,    map_location="cpu"					)
							snake_case_      =				mam_aaa["args"] or mam_aaa["cfg"]["model"]
							snake_case_      =				mam_aaa["model"]
							remove_ignore_keys_(_UpperCamelCase					)
							snake_case_      =				state_dict["encoder.embed_tokens.weight"].shape[0]

							snake_case_      =				MaMaaaConfig(
							    vocab_size=_UpperCamelCase							,    max_position_embeddings=1_0_2_4							,    encoder_layers=args.encoder_layers							,    decoder_layers=args.decoder_layers							,    encoder_attention_heads=args.encoder_attention_heads							,    decoder_attention_heads=args.decoder_attention_heads							,    encoder_ffn_dim=args.encoder_ffn_embed_dim							,    decoder_ffn_dim=args.decoder_ffn_embed_dim							,    d_model=args.encoder_embed_dim							,    encoder_layerdrop=args.encoder_layerdrop							,    decoder_layerdrop=args.decoder_layerdrop							,    dropout=args.dropout							,    attention_dropout=args.attention_dropout							,    activation_dropout=args.activation_dropout							,    activation_function="relu"							,    )

							snake_case_      =				state_dict["decoder.embed_tokens.weight"]
							snake_case_      =				MaMaaaForConditionalGeneration(_UpperCamelCase					)
							model.model.load_state_dict(_UpperCamelCase							,    strict=_UpperCamelCase					)
							snake_case_      =				make_linear_from_emb(model.model.shared					)

							return model


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[str]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		_SCREAMING_SNAKE_CASE							:       List[Any]						=			parser.parse_args()
		_SCREAMING_SNAKE_CASE							:       Any						=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)










'''simple docstring'''





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 			_snake_case		(     unittest.TestCase						,						lowercase_							):




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				load_tool("text-classification"							)
										self.tool.setup()
										snake_case_      =				load_tool("text-classification"    ,							remote=a__							)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				self.tool("That's quite cool"    ,							["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				self.remote_tool("That's quite cool"    ,							["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self.tool(text="That's quite cool"    ,							labels=["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.remote_tool(text="That's quite cool"    ,							labels=["positive", "negative"]							)
										self.assertEqual(a__    ,							"positive"							)




