



'''simple docstring'''

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
a							   =				logging.getLogger()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Dict      							='\n'.join(__UpperCAmelCase					)
			Path(__UpperCAmelCase					).open('w'					).writelines(__UpperCAmelCase					)


a							   =				'patrickvonplaten/t5-tiny-random'
a							   =				'sshleifer/bart-tiny-random'
a							   =				'sshleifer/tiny-mbart'

a							   =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 						a_		(     snake_case    ):


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						List[Any]			)			->					str:
										snake_case:    Tuple      							=Path(self.get_auto_remove_tmp_dir()			) / 'utest_input.source'
										snake_case:    Tuple      							=input_file_name.parent / 'utest_output.txt'
										assert not output_file_name.exists()
										snake_case:    Tuple      							=[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
										_dump_articles(a_      ,						a_			)

										snake_case:    int      							=str(Path(self.get_auto_remove_tmp_dir()			) / 'scores.json'			)
										snake_case:    Optional[Any]      							='translation_en_to_de' if model == T5_TINY else 'summarization'
										snake_case:    Tuple      							=F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

										with patch.object(a_      ,						'argv'      ,						a_			):
													run_generate()
													assert Path(a_			).exists()
													# os.remove(Path(output_file_name))


							def    UpperCamelCase     (				self   :						Dict			)			->					List[Any]:
										self.run_eval_tester(a_			)


							@parameterized.expand([BART_TINY, MBART_TINY]			)
							@slow
							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						List[str]			)			->					Tuple:
										self.run_eval_tester(a_			)




							@parameterized.expand([T5_TINY, MBART_TINY]			)
							@slow
							def    UpperCamelCase     (				self   :						Any      ,						a_   :						List[Any]			)			->					Optional[Any]:
										snake_case:    List[str]      							=Path(self.get_auto_remove_tmp_dir()			) / 'utest_input.source'
										snake_case:    Dict      							=input_file_name.parent / 'utest_output.txt'
										assert not output_file_name.exists()

										snake_case:    str      							={
										    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
										    'de': [
										        'Maschinelles Lernen ist großartig, oder?',
										        'Ich esse gerne Bananen',
										        'Morgen ist wieder ein toller Tag!',
										    ],
										}

										snake_case:    Optional[int]      							=Path(self.get_auto_remove_tmp_dir()			)
										snake_case:    Optional[int]      							=str(tmp_dir / 'scores.json'			)
										snake_case:    Union[str, Any]      							=str(tmp_dir / 'val.target'			)
										_dump_articles(a_      ,						text['en']			)
										_dump_articles(a_      ,						text['de']			)
										snake_case:    Any      							='translation_en_to_de' if model == T5_TINY else 'summarization'
										snake_case:    List[Any]      							=F'''
            run_eval_search.py
            {model}
            {str(a_			)}
            {str(a_			)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
										testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']			)

										with patch.object(a_      ,						'argv'      ,						a_			):
													with CaptureStdout() as cs:
																run_search()
													snake_case:    Dict      							=[' num_beams | length_penalty', model, 'Best score args']
													snake_case:    int      							=['Info']
													if "translation" in task:
																expected_strings.append('bleu'			)
													else:
																expected_strings.extend(a_			)
													for w in expected_strings:
																assert w in cs.out
													for w in un_expected_strings:
																assert w not in cs.out
													assert Path(a_			).exists()
													os.remove(Path(a_			)			)









'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


a							   =				logging.get_logger(__name__)
logging.set_verbosity_info()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "xprophetnet" in prophetnet_checkpoint_path:
						snake_case:    Optional[Any]      							=XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    str      							=XLMProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)
			else:
						snake_case:    Optional[int]      							=ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    List[str]      							=ProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    'self_attn': 'ngram_self_attn',
			    'cross_attn': 'encoder_attn',
			    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
			    'feed_forward_layer_norm': 'final_layer_norm',
			    'feed_forward': '',
			    'intermediate': 'fc1',
			    'output': 'fc2',
			    'key_proj': 'k_proj',
			    'query_proj': 'q_proj',
			    'value_proj': 'v_proj',
			    'word_embeddings': 'embed_tokens',
			    'embeddings_layer_norm': 'emb_layer_norm',
			    'relative_pos_embeddings': 'relative_linear',
			    'ngram_embeddings': 'ngram_input_embed',
			    'position_embeddings': 'embed_positions',
			}

			for key in loading_info["missing_keys"]:
						snake_case:    List[str]      							=key.split('.'					)

						if attributes[0] == "lm_head":
									snake_case:    Dict      							=prophet
									snake_case:    List[str]      							=prophet_old
						else:
									snake_case:    Any      							=prophet.prophetnet
									snake_case:    int      							=prophet_old.model

						snake_case:    int      							=False
						for attribute in attributes:
									if attribute in mapping:
												snake_case:    Dict      							=mapping[attribute]
												if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					) and len(__UpperCAmelCase					) > 0:
															snake_case:    Optional[int]      							=attribute
									elif hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
												snake_case:    Optional[Any]      							=attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												snake_case:    Optional[Any]      							=old_model.weight
												logger.info(f'''{attribute} is initialized.'''					)
												snake_case:    List[str]      							=True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												snake_case:    List[Any]      							=old_model.bias
												logger.info(f'''{attribute} is initialized'''					)
												snake_case:    List[Any]      							=True
												break
									elif attribute in special_keys and hasattr(__UpperCAmelCase				,							'in_proj_weight'					):
												snake_case:    Optional[int]      							=old_model.in_proj_weight.shape[0] // 3
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															snake_case:    int      							=nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
															snake_case:    Any      							=nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

												elif attribute == "key_proj":
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
												elif attribute == "value_proj":
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
												snake_case:    Any      							=True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
												snake_case:    Dict      							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
												snake_case:    Dict      							=True
												break

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

						if not is_key_init:
									raise ValueError(f'''{key} was not correctly initialized!'''					)

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			prophet.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--prophetnet_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							   =				parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




