


"""simple docstring"""



import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


UpperCAmelCase      		=			logging.get_logger(__name__)

enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      int        =   UNetaDModel
       __A			:      Any        =   "sample"






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				4
              lowercase		:Union[str, Any]       							=				3
              lowercase		:Dict       							=				(3_2, 3_2)

              lowercase		:Dict       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)
              lowercase		:Dict       							=				torch.tensor([1_0]				).to(snake_case__				)

              return {"sample": noise, "timestep": time_step}






       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				{
                  '''block_out_channels''': (3_2, 6_4),
                  '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
                  '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
                  '''attention_head_dim''': 3,
                  '''out_channels''': 3,
                  '''in_channels''': 3,
                  '''layers_per_block''': 2,
                  '''sample_size''': 3_2,
              }
              lowercase		:Any       							=				self.dummy_input
              return init_dict, inputs_dict







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   UNetaDModel
       __A			:      List[Any]        =   "sample"






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				4
              lowercase		:Dict       							=				4
              lowercase		:Optional[int]       							=				(3_2, 3_2)

              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)
              lowercase		:Optional[int]       							=				torch.tensor([1_0]				).to(snake_case__				)

              return {"sample": noise, "timestep": time_step}






       @property
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              return (4, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              return (4, 3_2, 3_2)






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Any       							=				{
                  '''sample_size''': 3_2,
                  '''in_channels''': 4,
                  '''out_channels''': 4,
                  '''layers_per_block''': 2,
                  '''block_out_channels''': (3_2, 6_4),
                  '''attention_head_dim''': 3_2,
                  '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
                  '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,     output_loading_info=snake_case__				)

              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:Optional[Any]       							=				model(**self.dummy_input				).sample

              assert image is not None, "Make sure output is not None"






       @unittest.skipIf(torch_device != '''cuda'''  ,     '''This test is supposed to run on GPU'''				)
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[Any]       							=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,     output_loading_info=snake_case__				)
              model.to(snake_case__				)
              lowercase		:Optional[int]       							=				model(**self.dummy_input				).sample

              assert image is not None, "Make sure output is not None"






       @unittest.skipIf(torch_device != '''cuda'''  ,     '''This test is supposed to run on GPU'''				)
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:str       							=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,     output_loading_info=snake_case__				)
              model_accelerate.to(snake_case__				)
              model_accelerate.eval()

              lowercase		:str       							=				torch.randn(
                  1  ,     model_accelerate.config.in_channels  ,     model_accelerate.config.sample_size  ,     model_accelerate.config.sample_size  ,     generator=torch.manual_seed(0				)  ,     )
              lowercase		:List[str]       							=				noise.to(snake_case__				)
              lowercase		:int       							=				torch.tensor([1_0] * noise.shape[0]				).to(snake_case__				)

              lowercase		:int       							=				model_accelerate(snake_case__  ,     snake_case__				)['''sample''']

              # two models don't need to stay in the device at the same time
              del model_accelerate
              torch.cuda.empty_cache()
              gc.collect()

              lowercase							,  lowercase		:List[str]       							=				UNetaDModel.from_pretrained(
                  '''fusing/unet-ldm-dummy-update'''  ,     output_loading_info=snake_case__  ,     low_cpu_mem_usage=snake_case__				)
              model_normal_load.to(snake_case__				)
              model_normal_load.eval()
              lowercase		:int       							=				model_normal_load(snake_case__  ,     snake_case__				)['''sample''']

              assert torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-3				)







       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:str       							=				UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''				)
              model.eval()
              model.to(snake_case__				)

              lowercase		:List[Any]       							=				torch.randn(
                  1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size  ,     generator=torch.manual_seed(0				)  ,     )
              lowercase		:Any       							=				noise.to(snake_case__				)
              lowercase		:Tuple       							=				torch.tensor([1_0] * noise.shape[0]				).to(snake_case__				)

              with torch.no_grad():
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample

              lowercase		:Optional[Any]       							=				output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              lowercase		:Optional[int]       							=				torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]				)
              # fmt: on

              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-3				)				)







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   UNetaDModel
       __A			:      str        =   "sample"






       @property
       def  __snake_case						(   self  :    int  ,     snake_case__  :    List[Any]=(3_2, 3_2)				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				4
              lowercase		:Union[str, Any]       							=				3

              lowercase		:int       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)
              lowercase		:Dict       							=				torch.tensor(batch_size * [1_0]				).to(dtype=torch.intaa  ,     device=snake_case__				)

              return {"sample": noise, "timestep": time_step}






       @property
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Dict       							=				{
                  '''block_out_channels''': [3_2, 6_4, 6_4, 6_4],
                  '''in_channels''': 3,
                  '''layers_per_block''': 1,
                  '''out_channels''': 3,
                  '''time_embedding_type''': '''fourier''',
                  '''norm_eps''': 1e-6,
                  '''mid_block_scale_factor''': math.sqrt(2.0				),
                  '''norm_num_groups''': None,
                  '''down_block_types''': [
                      '''SkipDownBlock2D''',
                      '''AttnSkipDownBlock2D''',
                      '''SkipDownBlock2D''',
                      '''SkipDownBlock2D''',
                  ],
                  '''up_block_types''': [
                      '''SkipUpBlock2D''',
                      '''SkipUpBlock2D''',
                      '''AttnSkipUpBlock2D''',
                      '''SkipUpBlock2D''',
                  ],
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       @slow
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase							,  lowercase		:Dict       							=				UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:Optional[Any]       							=				self.dummy_input
              lowercase		:List[Any]       							=				floats_tensor((4, 3) + (2_5_6, 2_5_6)				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				noise
              lowercase		:int       							=				model(**snake_case__				)

              assert image is not None, "Make sure output is not None"






       @slow
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''				)
              model.to(snake_case__				)

              lowercase		:Any       							=				4
              lowercase		:Tuple       							=				3
              lowercase		:Optional[int]       							=				(2_5_6, 2_5_6)

              lowercase		:Dict       							=				torch.ones((batch_size, num_channels) + sizes				).to(snake_case__				)
              lowercase		:int       							=				torch.tensor(batch_size * [1e-4]				).to(snake_case__				)

              with torch.no_grad():
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample

              lowercase		:Dict       							=				output[0, -3:, -3:, -1].flatten().cpu()
              # fmt: off
              lowercase		:List[Any]       							=				torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]				)
              # fmt: on

              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''				)
              model.to(snake_case__				)

              lowercase		:List[str]       							=				4
              lowercase		:Optional[Any]       							=				3
              lowercase		:List[str]       							=				(3_2, 3_2)

              lowercase		:Optional[Any]       							=				torch.ones((batch_size, num_channels) + sizes				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				torch.tensor(batch_size * [1e-4]				).to(snake_case__				)

              with torch.no_grad():
                     lowercase		:Dict       							=				model(snake_case__  ,     snake_case__				).sample

              lowercase		:List[str]       							=				output[0, -3:, -3:, -1].flatten().cpu()
              # fmt: off
              lowercase		:Any       							=				torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]				)
              # fmt: on

              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)







       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              pass





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def       lowerCamelCase					(a_						:Dict)     ->							Dict:
       lowercase		:Tuple       							=				[
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(a_				,				a_)






def       lowerCamelCase					(a_						:Union[str, Any])     ->							str:
       lowercase							,  lowercase		:Tuple       							=				emb.weight.shape
       lowercase		:List[str]       							=				nn.Linear(a_				,				a_				,				bias=a_)
       lowercase		:List[str]       							=				emb.weight.data
       return lin_layer






def       lowerCamelCase					(a_						:int				,				a_						:Union[str, Any]="facebook/mbart-large-en-ro"				,				a_						:Union[str, Any]=False				,				a_						:List[Any]=False)     ->							List[Any]:
       lowercase		:List[Any]       							=				torch.load(a_				,				map_location='''cpu''')['''model''']
       remove_ignore_keys_(a_)
       lowercase		:Dict       							=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase		:Tuple       							=				MBartConfig.from_pretrained(a_				,				vocab_size=a_)
       if mbart_aa and finetuned:
              lowercase		:List[Any]       							=				'''relu'''

       lowercase		:Optional[int]       							=				state_dict['''decoder.embed_tokens.weight''']
       lowercase		:Union[str, Any]       							=				MBartForConditionalGeneration(a_)
       model.model.load_state_dict(a_)

       if finetuned:
              lowercase		:Dict       							=				make_linear_from_emb(model.model.shared)

       return model


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a 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.''')
 parser.add_argument(
     '''--hf_config''',
     default='''facebook/mbart-large-cc25''',
     type=str,
     help='''Which huggingface architecture to use: mbart-large''',
 )
 parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
 parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
 UpperCAmelCase      		=			parser.parse_args()
 UpperCAmelCase      		=			convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)

