

"""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 	lowercase_						(     _lowercase							:		str				,   _lowercase							:		Dict						):




					'''simple docstring'''






					UpperCAmelCase      : List[Any]   =					torch.load(_lowercase				,   map_location="cpu"						)

					UpperCAmelCase      : List[str]   =					chkpt['''model''']

					# We have the base model one level deeper than the original XLM repository
					UpperCAmelCase      : List[Any]   =					{}
					for k, v in state_dict.items():
										if "pred_layer" in k:
															UpperCAmelCase      : Union[str, Any]   =					v
										else:
															UpperCAmelCase      : Tuple   =					v

					UpperCAmelCase      : Union[str, Any]   =					chkpt['''params''']
					UpperCAmelCase      : List[Any]   =					{n: v for n, v in config.items() if not isinstance(_lowercase				,   (torch.FloatTensor, numpy.ndarray)						)}

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

					# Save pytorch-model
					UpperCAmelCase      : Optional[Any]   =					pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					UpperCAmelCase      : int   =					pytorch_dump_folder_path + '''/''' + CONFIG_NAME
					UpperCAmelCase      : Union[str, Any]   =					pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

					print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""						)
					torch.save(_lowercase				,   _lowercase						)

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

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


if __name__ == "__main__":
	snake_case_ : Any   =							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."""
	)
	snake_case_ : Union[str, Any]   =							parser.parse_args()
	convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = VQModel
   lowerCAmelCase   :     List[str]	      = '''sample'''



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Tuple=(32, 32)				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			4
    _UpperCamelCase:  Tuple    						=			3

    _UpperCamelCase:  List[str]    						=			floats_tensor((batch_size, num_channels) + sizes				).to(_lowercase				)

    return {"sample": image}



   @property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return (3, 32, 32)



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    return (3, 32, 32)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			{
        '''block_out_channels''': [32, 64],
        '''in_channels''': 3,
        '''out_channels''': 3,
        '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
        '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
        '''latent_channels''': 3,
    }
    _UpperCamelCase:  Optional[Any]    						=			self.dummy_input
    return init_dict, inputs_dict



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  int    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''		,	output_loading_info=_lowercase				)
    self.assertIsNotNone(_lowercase				)
    self.assertEqual(len(loading_info['''missing_keys''']				)		,	0				)

    model.to(_lowercase				)
    _UpperCamelCase:  Tuple    						=			model(**self.dummy_input				)

    assert image is not None, "Make sure output is not None"



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
    model.to(_lowercase				).eval()

    torch.manual_seed(0				)
    if torch.cuda.is_available():
     torch.cuda.manual_seed_all(0				)

    _UpperCamelCase:  int    						=			torch.randn(1		,	model.config.in_channels		,	model.config.sample_size		,	model.config.sample_size				)
    _UpperCamelCase:  Tuple    						=			image.to(_lowercase				)
    with torch.no_grad():
     _UpperCamelCase:  Optional[int]    						=			model(_lowercase				).sample

    _UpperCamelCase:  List[Any]    						=			output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _UpperCamelCase:  Any    						=			torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]				)
    # fmt: on
    self.assertTrue(torch.allclose(_lowercase		,	_lowercase		,	atol=1E-3				)				)