



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__snake_case       :	Any            =False
__snake_case       :	Tuple            =True
__snake_case       :	Optional[int]            =False


if __name__ == "__main__":
			__snake_case       :	Tuple            =argparse.ArgumentParser()

			parser.add_argument(
			    '--repo_path',
			    default=None,
			    type=str,
			    required=True,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

			__snake_case       :	Tuple            =parser.parse_args()

			__snake_case       :	List[str]            ={
			    'image_size': 'sample_size',
			    'num_res_blocks': 'layers_per_block',
			    'block_channels': 'block_out_channels',
			    'down_blocks': 'down_block_types',
			    'up_blocks': 'up_block_types',
			    'downscale_freq_shift': 'freq_shift',
			    'resnet_num_groups': 'norm_num_groups',
			    'resnet_act_fn': 'act_fn',
			    'resnet_eps': 'norm_eps',
			    'num_head_channels': 'attention_head_dim',
			}

			__snake_case       :	int            ={
			    'time_steps': 'time_proj',
			    'mid': 'mid_block',
			    'downsample_blocks': 'down_blocks',
			    'upsample_blocks': 'up_blocks',
			}

			__snake_case       :	str            ='' if has_file(args.repo_path, 'config.json') else 'unet'

			with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
						__snake_case       :	Tuple            =reader.read()
						__snake_case       :	Dict            =json.loads(text)

			if do_only_config:
						for key in config_parameters_to_change.keys():
									config.pop(key, None)

			if has_file(args.repo_path, 'config.json'):
						__snake_case       :	Optional[Any]            =UNetaDModel(**config)
			else:
						__snake_case       :	Union[str, Any]            =UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
						__snake_case       :	Any            =class_name(**config)

			if do_only_config:
						model.save_config(os.path.join(args.repo_path, subfolder))

			__snake_case       :	List[str]            =dict(model.config)

			if do_only_renaming:
						for key, value in config_parameters_to_change.items():
									if key in config:
												__snake_case       :	int            =config[key]
												del config[key]

						__snake_case       :	int            =[k.replace('UNetRes', '') for k in config['down_block_types']]
						__snake_case       :	Tuple            =[k.replace('UNetRes', '') for k in config['up_block_types']]

			if do_only_weights:
						__snake_case       :	Tuple            =torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

						__snake_case       :	int            ={}
						for param_key, param_value in state_dict.items():
									if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
												continue
									__snake_case       :	Any            =False
									for key, new_key in key_parameters_to_change.items():
												if not has_changed and param_key.split('.')[0] == key:
															__snake_case       :	Tuple            =param_value
															__snake_case       :	str            =True
									if not has_changed:
												__snake_case       :	int            =param_value

						model.load_state_dict(new_state_dict)
						model.save_pretrained(os.path.join(args.repo_path, subfolder))






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

