



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 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		str ):
		# Load checkpoint
		lowerCAmelCase_			:     Union[str, Any]									= torch.load(__UpperCamelCase     ,map_location='''cpu''' )

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

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

		lowerCAmelCase_			:     Tuple									= chkpt['''params''']
		lowerCAmelCase_			:     Optional[Any]									= {n: v for n, v in config.items() if not isinstance(__UpperCamelCase     ,(torch.FloatTensor, numpy.ndarray) )}

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

		# Save pytorch-model
		lowerCAmelCase_			:     Optional[int]									= pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
		lowerCAmelCase_			:     Optional[Any]									= pytorch_dump_folder_path + '''/''' + CONFIG_NAME
		lowerCAmelCase_			:     Optional[Any]									= 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__":
						A__  :  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.'''
						)
						A__  :  Tuple								=					parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)










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 				__snake_case						(     unittest.TestCase						):




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







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png''')
									lowerCAmelCase_			:     str									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

									lowerCAmelCase_			:     List[Any]									= '''xvjiarui/stable-diffusion-2-inpainting'''
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= FlaxStableDiffusionInpaintPipeline.from_pretrained(A_				,				safety_checker=A_)

									lowerCAmelCase_			:     List[str]									= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowerCAmelCase_			:     List[Any]									= jax.random.PRNGKey(0)
									lowerCAmelCase_			:     str									= 5_0

									lowerCAmelCase_			:     List[Any]									= jax.device_count()
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [prompt]
									lowerCAmelCase_			:     str									= num_samples * [init_image]
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [mask_image]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pipeline.prepare_inputs(A_				,				A_				,				A_)

									# shard inputs and rng
									lowerCAmelCase_			:     str									= replicate(A_)
									lowerCAmelCase_			:     str									= jax.random.split(A_				,				jax.device_count())
									lowerCAmelCase_			:     List[Any]									= shard(A_)
									lowerCAmelCase_			:     str									= shard(A_)
									lowerCAmelCase_			:     Tuple									= shard(A_)

									lowerCAmelCase_			:     int									= pipeline(
									    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				jit=A_)

									lowerCAmelCase_			:     Optional[int]									= output.images.reshape(A_				,				5_1_2				,				5_1_2				,				3)

									lowerCAmelCase_			:     List[str]									= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									lowerCAmelCase_			:     int									= jnp.asarray(jax.device_get(image_slice.flatten()))
									lowerCAmelCase_			:     str									= 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





