






"""simple docstring"""
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Dict	    =   [
	    """decoder.version""",
	    """decoder.output_projection.weight""",
	    """_float_tensor""",
	    """decoder.embed_positions._float_tensor""",
	]
	for k in ignore_keys:
		state_dict.pop(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__					):
	A_    ,     A_		:						Optional[int]	    =   emb.weight.shape
	A_		:						Optional[Any]	    =   nn.Linear(snake_case__   ,   snake_case__   ,   bias=snake_case__					)
	A_		:						Optional[int]	    =   emb.weight.data
	return lin_layer






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Any	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)
	A_		:						int	    =   Namespace(**checkpoint["""cfg"""]["""model"""]					)
	A_		:						Optional[int]	    =   checkpoint["""model"""]
	remove_ignore_keys_(snake_case__					)
	A_		:						Optional[int]	    =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

	A_		:						List[Any]	    =   {key.replace("""decoder"""   ,   """model"""					): val for key, val in state_dict.items()}

	A_		:						int	    =   XGLMConfig(
	    vocab_size=snake_case__   ,   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   ,   )

	A_		:						Optional[int]	    =   XGLMForCausalLM(snake_case__					)
	A_		:						List[str]	    =   model.load_state_dict(snake_case__   ,   strict=snake_case__					)
	print(snake_case__					)
	A_		:						Optional[int]	    =   make_linear_from_emb(model.model.embed_tokens					)

	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_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""
import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation



class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   get_activation("""swish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   get_activation("""silu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   get_activation("""mish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.Mish							)

		self.assertEqual(act(torch.tensor(-200     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   get_activation("""gelu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.GELU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)

