import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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  __magic_name__  (		__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[int]  			=   IFInpaintingPipeline
   lowerCAmelCase      :    List[str]  			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
   lowerCAmelCase      :    Optional[Any]  			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase      :    List[str]  			=   PipelineTesterMixin.required_optional_params - {'''latents'''}



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    return self._get_dummy_components()



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  List[Any]		,  _lowercase					:  Optional[int]=0							):



    """simple docstring"""



    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     int							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     int							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)

    _UpperCamelCase:     Union[str, Any]							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)
    _UpperCamelCase:     Tuple							= floats_tensor((1, 3, 32, 32)		,  rng=random.Random(_lowercase							)							).to(_lowercase							)

    _UpperCamelCase:     Dict							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''image''': 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   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda'''		,  reason='''float16 requires CUDA'''							)
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    super().test_save_load_floataa(expected_max_diff=1E-1							)



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    self._test_save_load_local()



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    self._test_inference_batch_single_identical(
        expected_max_diff=1E-2		,  )
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= [
     '''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(lowercase  ,					lowercase		)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= emb.weight.shape
 _UpperCamelCase:     int							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Any							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							Optional[Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     Any							= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 _UpperCamelCase:     List[Any]							= mam_aaa['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Tuple							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     List[Any]							= MaMaaaConfig(
     vocab_size=lowercase  ,					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:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MaMaaaForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 _UpperCamelCase:     Optional[Any]							= 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)