






'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 		_UpperCAmelCase   (							__A							:   Any      ):

				'''simple docstring'''





				a_		:  Optional[Any]											=      [
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''encoder.embed_positions._float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(__A						,		__A      )





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

				'''simple docstring'''





				a_		:  str											=      emb.weight.shape
				a_		:  Any											=      nn.Linear(__A						,		__A						,		bias=__A      )
				a_		:  List[Any]											=      emb.weight.data
				return lin_layer





def 		_UpperCAmelCase   (							__A							:   List[Any]      ):

				'''simple docstring'''





				a_		:  int											=      torch.load(__A						,		map_location='''cpu'''      )
				a_		:  Tuple											=      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				a_		:  List[str]											=      mam_aaa['''model''']
				remove_ignore_keys_(__A      )
				a_		:  Union[str, Any]											=      state_dict['''encoder.embed_tokens.weight'''].shape[0]

				a_		:  List[str]											=      MaMaaaConfig(
				    vocab_size=__A						,		max_position_embeddings=10_24						,		encoder_layers=args.encoder_layers						,		decoder_layers=args.decoder_layers						,		encoder_attention_heads=args.encoder_attention_heads						,		decoder_attention_heads=args.decoder_attention_heads						,		encoder_ffn_dim=args.encoder_ffn_embed_dim						,		decoder_ffn_dim=args.decoder_ffn_embed_dim						,		d_model=args.encoder_embed_dim						,		encoder_layerdrop=args.encoder_layerdrop						,		decoder_layerdrop=args.decoder_layerdrop						,		dropout=args.dropout						,		attention_dropout=args.attention_dropout						,		activation_dropout=args.activation_dropout						,		activation_function='''relu'''						,		)

				a_		:  List[Any]											=      state_dict['''decoder.embed_tokens.weight''']
				a_		:  List[str]											=      MaMaaaForConditionalGeneration(__A      )
				model.model.load_state_dict(__A						,		strict=__A      )
				a_		:  Tuple											=      make_linear_from_emb(model.model.shared      )

				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_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)












'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				IFInpaintingSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=0					)			->			List[Any]:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Optional[int]											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''original_image''': original_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}

							return inputs






			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()  ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''  ,						)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda'''  ,						reason='''float16 requires CUDA'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[int]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Optional[Any]:
							self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[Any]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2  ,						)




