
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		_UpperCAmelCase   (	UpperCamelCase:     Any  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[
	    "decoder.version",
	    "decoder.output_projection.weight",
	    "_float_tensor",
	    "decoder.embed_positions._float_tensor",
	]
	for k in ignore_keys:
		state_dict.pop(UpperCamelCase			,							UpperCamelCase  )






def 		_UpperCAmelCase   (	UpperCamelCase:     Dict  ):






	"""simple docstring"""


	__lowerCAmelCase   ,						__lowerCAmelCase         =							emb.weight.shape
	__lowerCAmelCase         =							nn.Linear(UpperCamelCase			,							UpperCamelCase			,							bias=UpperCamelCase  )
	__lowerCAmelCase         =							emb.weight.data
	return lin_layer






def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							torch.load(UpperCamelCase			,							map_location="cpu"  )
	__lowerCAmelCase         =							Namespace(**checkpoint["cfg"]["model"]  )
	__lowerCAmelCase         =							checkpoint["model"]
	remove_ignore_keys_(UpperCamelCase  )
	__lowerCAmelCase         =							state_dict["decoder.embed_tokens.weight"].shape[0]

	__lowerCAmelCase         =							{key.replace("decoder"			,							"model"  ): val for key, val in state_dict.items()}

	__lowerCAmelCase         =							XGLMConfig(
	    vocab_size=UpperCamelCase			,							max_position_embeddings=args.max_target_positions			,							num_layers=args.decoder_layers			,							attention_heads=args.decoder_attention_heads			,							ffn_dim=args.decoder_ffn_embed_dim			,							d_model=args.decoder_embed_dim			,							layerdrop=args.decoder_layerdrop			,							dropout=args.dropout			,							attention_dropout=args.attention_dropout			,							activation_dropout=args.activation_dropout			,							activation_function="gelu"			,							scale_embedding=not args.no_scale_embedding			,							tie_word_embeddings=args.share_decoder_input_output_embed			,							)

	__lowerCAmelCase         =							XGLMForCausalLM(UpperCamelCase  )
	__lowerCAmelCase         =							model.load_state_dict(UpperCamelCase			,							strict=UpperCamelCase  )
	print(UpperCamelCase  )
	__lowerCAmelCase         =							make_linear_from_emb(model.model.embed_tokens  )

	return model


if __name__ == "__main__":
					UpperCamelCase_   	=	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.")
					UpperCamelCase_   	=	parser.parse_args()
					UpperCamelCase_   	=	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)








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					):







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







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

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

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







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







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

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







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

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







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







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

								__lowerCAmelCase         =							DisjunctiveConstraint(snake_case__ )

								__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] )






