






'''simple docstring'''







import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
							import jax
							import jax.numpy as jnp
							from flax.jax_utils import replicate
							from flax.training.common_utils import shard




@slow
@require_flax
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png"""       )
								lowerCamelCase__         =  load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )

								lowerCamelCase__         =  """xvjiarui/stable-diffusion-2-inpainting"""
								lowerCamelCase__       ,			lowerCamelCase__         =  FlaxStableDiffusionInpaintPipeline.from_pretrained(_lowerCAmelCase   ,safety_checker=_lowerCAmelCase       )

								lowerCamelCase__         =  """Face of a yellow cat, high resolution, sitting on a park bench"""

								lowerCamelCase__         =  jax.random.PRNGKey(0       )
								lowerCamelCase__         =  50

								lowerCamelCase__         =  jax.device_count()
								lowerCamelCase__         =  num_samples * [prompt]
								lowerCamelCase__         =  num_samples * [init_image]
								lowerCamelCase__         =  num_samples * [mask_image]
								lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  pipeline.prepare_inputs(_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       )

								# shard inputs and rng
								lowerCamelCase__         =  replicate(_lowerCAmelCase       )
								lowerCamelCase__         =  jax.random.split(_lowerCAmelCase   ,jax.device_count()       )
								lowerCamelCase__         =  shard(_lowerCAmelCase       )
								lowerCamelCase__         =  shard(_lowerCAmelCase       )
								lowerCamelCase__         =  shard(_lowerCAmelCase       )

								lowerCamelCase__         =  pipeline(
								    _lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,jit=_lowerCAmelCase       )

								lowerCamelCase__         =  output.images.reshape(_lowerCAmelCase   ,5_12   ,5_12   ,3       )

								lowerCamelCase__         =  images[0, 2_53:2_56, 2_53:2_56, -1]

								lowerCamelCase__         =  jnp.asarray(jax.device_get(image_slice.flatten()       )       )
								lowerCamelCase__         =  jnp.array(
								    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]       )
								print(F'''output_slice: {output_slice}'''       )
								assert jnp.abs(output_slice - expected_slice       ).max() < 1E-2









'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						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(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Tuple						):
						lowerCamelCase__       ,			lowerCamelCase__         =  emb.weight.shape
						lowerCamelCase__         =  nn.Linear(__lowerCAmelCase   ,    __lowerCAmelCase   ,    bias=__lowerCAmelCase						)
						lowerCamelCase__         =  emb.weight.data
						return lin_layer
def 						A__			(				__lowerCAmelCase		:			Dict						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)
						lowerCamelCase__         =  mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
						lowerCamelCase__         =  mam_aaa["""model"""]
						remove_ignore_keys_(__lowerCAmelCase						)
						lowerCamelCase__         =  state_dict["""encoder.embed_tokens.weight"""].shape[0]

						lowerCamelCase__         =  MaMaaaConfig(
						    vocab_size=__lowerCAmelCase   ,    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(__lowerCAmelCase						)
						model.model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase						)
						lowerCamelCase__         =  make_linear_from_emb(model.model.shared						)

						return model


if __name__ == "__main__":
							UpperCamelCase    :      Optional[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.')
							UpperCamelCase    :      Tuple  			=		parser.parse_args()
							UpperCamelCase    :      List[Any]  			=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)

