'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 							snake_case__				(     _A:   Dict					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    [
		    """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(_A ,  _A					)






def 							snake_case__				(     _A:   List[str]					)       ->      List[Any]:

		'''simple docstring'''


		lowerCAmelCase   ,	lowerCAmelCase     =    emb.weight.shape
		lowerCAmelCase     =    nn.Linear(_A ,  _A ,  bias=_A					)
		lowerCAmelCase     =    emb.weight.data
		return lin_layer






def 							snake_case__				(     _A:   str					)       ->      List[Any]:

		'''simple docstring'''


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

		lowerCAmelCase     =    MaMaaaConfig(
		    vocab_size=_A ,  max_position_embeddings=1024 ,  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""" ,  )

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

		return model


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





'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint






@require_torch
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 4], [1, 2, 3, 4]]
					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)
					self.assertTrue(isinstance(dc.token_ids			,       __lowerCAmelCase))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2], [1, 2, 3, 4]]

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(__lowerCAmelCase)  # fails here



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(3)
					lowerCAmelCase     =    stepped is True and completed is True and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 3])







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(4)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2, 4])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 4, 5])

					dc.reset()

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 3)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 2)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.remaining() == 0)
					self.assertTrue(dc.current_seq == [1, 2, 5])




