



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Dict													=					[
				    "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(lowerCamelCase__					,			lowerCamelCase__						)


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   Tuple													=					emb.weight.shape
				lowercase__					:   Dict													=					nn.Linear(lowerCamelCase__					,			lowerCamelCase__					,			bias=lowerCamelCase__						)
				lowercase__					:   Optional[Any]													=					emb.weight.data
				return lin_layer


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					torch.load(lowerCamelCase__					,			map_location="cpu"						)
				lowercase__					:   Union[str, Any]													=					mam_aaa["args"] or mam_aaa["cfg"]["model"]
				lowercase__					:   Optional[Any]													=					mam_aaa["model"]
				remove_ignore_keys_(lowerCamelCase__						)
				lowercase__					:   Optional[Any]													=					state_dict["encoder.embed_tokens.weight"].shape[0]

				lowercase__					:   Any													=					MaMaaaConfig(
				    vocab_size=lowerCamelCase__					,			max_position_embeddings=1_024					,			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"					,			)

				lowercase__					:   Tuple													=					state_dict["decoder.embed_tokens.weight"]
				lowercase__					:   Union[str, Any]													=					MaMaaaForConditionalGeneration(lowerCamelCase__						)
				model.model.load_state_dict(lowerCamelCase__					,			strict=lowerCamelCase__						)
				lowercase__					:   Any													=					make_linear_from_emb(model.model.shared						)

				return model


if __name__ == "__main__":
				lowerCAmelCase__					  =  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.''')
				lowerCAmelCase__					  =  parser.parse_args()
				lowerCAmelCase__					  =  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@slow
					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Dict													=					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"					)
									lowercase__					:   Dict													=					AutoTokenizer.from_pretrained("google/mt5-small"					)

									lowercase__					:   Optional[Any]													=					tokenizer("Hello there"				,       return_tensors="np"					).input_ids
									lowercase__					:   Optional[Any]													=					tokenizer("Hi I am"				,       return_tensors="np"					).input_ids

									lowercase__					:   int													=					shift_tokens_right(SCREAMING_SNAKE_CASE				,       model.config.pad_token_id				,       model.config.decoder_start_token_id					)

									lowercase__					:   Tuple													=					model(SCREAMING_SNAKE_CASE				,       decoder_input_ids=SCREAMING_SNAKE_CASE					).logits
									lowercase__					:   Dict													=					optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE				,       onehot(SCREAMING_SNAKE_CASE				,       logits.shape[-1]					)					).mean()

									lowercase__					:   Union[str, Any]													=					-(labels.shape[-1] * loss.item())

									lowercase__					:   Union[str, Any]													=					-84.9_127
									self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




