





"""simple docstring"""
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Dict			):
 # Load checkpoint
 lowerCAmelCase    				= torch.load(_UpperCAmelCase  ,     map_location='cpu'			)

 lowerCAmelCase    				= chkpt['model']

 # We have the base model one level deeper than the original XLM repository
 lowerCAmelCase    				= {}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   lowerCAmelCase    				= v
  else:
   lowerCAmelCase    				= v

 lowerCAmelCase    				= chkpt['params']
 lowerCAmelCase    				= {n: v for n, v in config.items() if not isinstance(_UpperCAmelCase  ,     (torch.FloatTensor, numpy.ndarray)			)}

 lowerCAmelCase    				= chkpt['dico_word2id']
 lowerCAmelCase    				= {s + '</w>' if s.find('@@'			) == -1 and i > 13 else s.replace('@@'  ,     ''			): i for s, i in vocab.items()}

 # Save pytorch-model
 lowerCAmelCase    				= pytorch_dump_folder_path + '/' + WEIGHTS_NAME
 lowerCAmelCase    				= pytorch_dump_folder_path + '/' + CONFIG_NAME
 lowerCAmelCase    				= pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

 print(F'Save PyTorch model to {pytorch_weights_dump_path}'			)
 torch.save(_UpperCAmelCase  ,     _UpperCAmelCase			)

 print(F'Save configuration file to {pytorch_config_dump_path}'			)
 with open(_UpperCAmelCase  ,     'w'  ,     encoding='utf-8'			) as f:
  f.write(json.dumps(_UpperCAmelCase  ,     indent=2			) + '\n'			)

 print(F'Save vocab file to {pytorch_config_dump_path}'			)
 with open(_UpperCAmelCase  ,     'w'  ,     encoding='utf-8'			) as f:
  f.write(json.dumps(_UpperCAmelCase  ,     indent=2			) + '\n'			)


if __name__ == "__main__":
       __UpperCamelCase     :						str						       =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       __UpperCamelCase     :						List[Any]						       =  parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)












"""simple docstring"""
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       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       IFInpaintingPipeline
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {'''latents'''}



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._get_dummy_components()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

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

  lowerCAmelCase    				= {
      '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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_local()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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





