




"""simple docstring"""

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        lowercase      (    unittest.TestCase):







				@property
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				List[Any]	   =		UNetaDModel(
					    block_out_channels=(32, 64)      ,     layers_per_block=2      ,     sample_size=32      ,     in_channels=3      ,     out_channels=3      ,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')      ,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')      ,     )
					return model







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		self.dummy_uncond_unet
					A_  :				List[str]	   =		KarrasVeScheduler()

					A_  :				Dict	   =		KarrasVePipeline(unet=_lowerCamelCase      ,     scheduler=_lowerCamelCase       )
					pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Optional[Any]	   =		torch.manual_seed(0       )
					A_  :				str	   =		pipe(num_inference_steps=2      ,     generator=_lowerCamelCase      ,     output_type='''numpy'''       ).images

					A_  :				Any	   =		torch.manual_seed(0       )
					A_  :				List[Any]	   =		pipe(num_inference_steps=2      ,     generator=_lowerCamelCase      ,     output_type='''numpy'''      ,     return_dict=_lowerCamelCase       )[0]

					A_  :				int	   =		image[0, -3:, -3:, -1]
					A_  :				str	   =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					A_  :				Optional[Any]	   =		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2

@slow
@require_torch
class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		'''google/ncsnpp-celebahq-256'''
					A_  :				str	   =		UNetaDModel.from_pretrained(_lowerCamelCase       )
					A_  :				List[str]	   =		KarrasVeScheduler()

					A_  :				Any	   =		KarrasVePipeline(unet=_lowerCamelCase      ,     scheduler=_lowerCamelCase       )
					pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				List[Any]	   =		torch.manual_seed(0       )
					A_  :				Tuple	   =		pipe(num_inference_steps=20      ,     generator=_lowerCamelCase      ,     output_type='''numpy'''       ).images

					A_  :				List[Any]	   =		image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					A_  :				str	   =		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2











"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

	A_  :				int	   =		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  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			Any        =       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  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





