
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	@property
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      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     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.dummy_uncond_unet
				_snake_case          =      PNDMScheduler()

				_snake_case          =      PNDMPipeline(unet=lowercase      ,  scheduler=lowercase     )
				pndm.to(lowercase     )
				pndm.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pndm(generator=lowercase      ,  num_inference_steps=20      ,  output_type='numpy'     ).images

				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pndm(generator=lowercase      ,  num_inference_steps=20      ,  output_type='numpy'      ,  return_dict=lowercase     )[0]

				_snake_case          =      image[0, -3:, -3:, -1]
				_snake_case          =      image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				_snake_case          =      np.array([1.0, 1.0, 0.0, 1.0, 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        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      'google/ddpm-cifar10-32'

				_snake_case          =      UNetaDModel.from_pretrained(lowercase     )
				_snake_case          =      PNDMScheduler()

				_snake_case          =      PNDMPipeline(unet=lowercase      ,  scheduler=lowercase     )
				pndm.to(lowercase     )
				pndm.set_progress_bar_config(disable=lowercase     )
				_snake_case          =      torch.manual_seed(0     )
				_snake_case          =      pndm(generator=lowercase      ,  output_type='numpy'     ).images

				_snake_case          =      image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				_snake_case          =      np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]     )

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

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






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






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






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

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      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''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      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)