




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =			[
							    '''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(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =			emb.weight.shape
							lowerCAmelCase   =			nn.Linear(_snake_case						, _snake_case						, bias=_snake_case							)
							lowerCAmelCase   =			emb.weight.data
							return lin_layer



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =			torch.load(_snake_case						, map_location='''cpu'''							)
							lowerCAmelCase   =			mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
							lowerCAmelCase   =			mam_aaa['''model''']
							remove_ignore_keys_(_snake_case							)
							lowerCAmelCase   =			state_dict['''encoder.embed_tokens.weight'''].shape[0]

							lowerCAmelCase   =			MaMaaaConfig(
							    vocab_size=_snake_case						, max_position_embeddings=1024						, 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'''						, )

							lowerCAmelCase   =			state_dict['''decoder.embed_tokens.weight''']
							lowerCAmelCase   =			MaMaaaForConditionalGeneration(_snake_case							)
							model.model.load_state_dict(_snake_case						, strict=_snake_case							)
							lowerCAmelCase   =			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="""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.""")
		UpperCAmelCase_						=parser.parse_args()
		UpperCAmelCase_						=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)









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 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Tuple										=IFInpaintingSuperResolutionPipeline
				__a							: Dict										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__a							: int										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}  )
				__a							: Union[str, Any]										=PipelineTesterMixin.required_optional_params - {"""latents"""}



				def       __snake_case		( self						):
											return self._get_superresolution_dummy_components()



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
											if str(UpperCAmelCase_						).startswith('''mps'''						):
																		lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											else:
																		lowerCAmelCase   =		torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)

											lowerCAmelCase   =		floats_tensor((1, 3, 16, 16)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

											lowerCAmelCase   =		{
											    '''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       __snake_case		( self						):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



				def       __snake_case		( self						):
											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != '''cuda'''       , reason='''float16 requires CUDA'''						)
				def       __snake_case		( self						):
											# 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       __snake_case		( self						):
											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



				def       __snake_case		( self						):
											self._test_save_load_local()






				def       __snake_case		( self						):
											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2       , )



