'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    [
           "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(_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    Union[str, Any]:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any    )    ->    int:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    torch.load(_UpperCAmelCase			,	map_location="cpu"    )
       _UpperCAmelCase     :  Any							      =    mam_aaa["args"] or mam_aaa["cfg"]["model"]
       _UpperCAmelCase     :  List[Any]							      =    mam_aaa["model"]
       remove_ignore_keys_(_UpperCAmelCase    )
       _UpperCAmelCase     :  int							      =    state_dict["encoder.embed_tokens.weight"].shape[0]

       _UpperCAmelCase     :  Tuple							      =    MaMaaaConfig(
           vocab_size=_UpperCAmelCase			,	max_position_embeddings=1_024			,	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"			,	)

       _UpperCAmelCase     :  Union[str, Any]							      =    state_dict["decoder.embed_tokens.weight"]
       _UpperCAmelCase     :  Union[str, Any]							      =    MaMaaaForConditionalGeneration(_UpperCAmelCase    )
       model.model.load_state_dict(_UpperCAmelCase			,	strict=_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[Any]							      =    make_linear_from_emb(model.model.shared    )

       return model


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Union[str, 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.""")
  __SCREAMING_SNAKE_CASE       :     int     =  parser.parse_args()
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE_							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'				,				revision='onnx'				,				safety_checker=_lowerCAmelCase				,				feature_extractor=_lowerCAmelCase				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A red cat sitting on a park bench'

       SCREAMING_SNAKE_CASE_							=			np.random.RandomState(0						)
       SCREAMING_SNAKE_CASE_							=			pipe(
           prompt=_lowerCAmelCase				,				image=_lowerCAmelCase				,				mask_image=_lowerCAmelCase				,				strength=0.75				,				guidance_scale=7.5				,				num_inference_steps=15				,				generator=_lowerCAmelCase				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2