



'''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


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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Optional[Any]:
		if "xprophetnet" in prophetnet_checkpoint_path:
				_a     :      List[Any]        =							XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_  )
				_a       ,					_a     :      int        =							XLMProphetNetForConditionalGeneration.from_pretrained(
				    lowerCAmelCase_						,			output_loading_info=lowerCAmelCase_  )
		else:
				_a     :      Union[str, Any]        =							ProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase_  )
				_a       ,					_a     :      Union[str, Any]        =							ProphetNetForConditionalGeneration.from_pretrained(
				    lowerCAmelCase_						,			output_loading_info=lowerCAmelCase_  )

		_a     :      Any        =							['key_proj', 'value_proj', 'query_proj']

		_a     :      Dict        =							{
		    '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"]:
				_a     :      Union[str, Any]        =							key.split('.'  )

				if attributes[0] == "lm_head":
						_a     :      Optional[Any]        =							prophet
						_a     :      str        =							prophet_old
				else:
						_a     :      List[str]        =							prophet.prophetnet
						_a     :      Optional[Any]        =							prophet_old.model

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

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

								elif attribute == "key_proj":
										_a     :      str        =							nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
										_a     :      Optional[Any]        =							nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
								elif attribute == "value_proj":
										_a     :      Optional[Any]        =							nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
										_a     :      Union[str, Any]        =							nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
								_a     :      Dict        =							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] == 512, "We want 512 position_embeddings."
								_a     :      Union[str, Any]        =							nn.Parameter(old_model.embed_positions.weight[:512, :]  )
								_a     :      Optional[int]        =							True
								break

						if attribute.isdigit():
								_a     :      Tuple        =							model[int(lowerCAmelCase_  )]
								_a     :      Dict        =							old_model[int(lowerCAmelCase_  )]
						else:
								_a     :      List[Any]        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

								if old_attribute == "":
										_a     :      Optional[Any]        =							old_model
								else:
										if not hasattr(lowerCAmelCase_						,			lowerCAmelCase_  ):
												raise ValueError(f"""{old_model} does not have {old_attribute}"""  )
										_a     :      List[Any]        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

				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(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   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.'''
							)
							__lowerCAmelCase									=   parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''

import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class 	__magic_name__						(    _UpperCamelCase   ):







							@require_torch
							def 							__lowercase			(							self					:       Tuple		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Optional[int]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      List[str]        =							'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

									_a     :      Tuple        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

									# Force fetching the files so that we can use the cache
									_a     :      List[Any]        =							'hf-internal-testing/tiny-random-bert'
									BertConfig.from_pretrained(_UpperCAmelCase		)
									BertModel.from_pretrained(_UpperCAmelCase		)
									BertTokenizer.from_pretrained(_UpperCAmelCase		)
									pipeline(task='fill-mask'	,model=_UpperCAmelCase		)

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[int]        =							[sys.executable, '-c', '\n'.join([load, run, mock]		)]

									# should succeed
									_a     :      Tuple        =							self.get_env()
									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      int        =							'1'
									_a     :      List[Any]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       Any		):
									# python one-liner segments
									# this must be loaded before socket.socket is monkey-patched
									_a     :      Dict        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      Optional[int]        =							'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

									_a     :      Optional[Any]        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

									# Force fetching the files so that we can use the cache
									_a     :      int        =							'hf-internal-testing/tiny-random-bert'
									BertConfig.from_pretrained(_UpperCAmelCase		)
									BertModel.from_pretrained(_UpperCAmelCase		)
									BertTokenizer.from_pretrained(_UpperCAmelCase		)
									pipeline(task='fill-mask'	,model=_UpperCAmelCase		)

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[int]        =							[sys.executable, '-c', '\n'.join([load, run, mock]		)]

									# should succeed
									_a     :      str        =							self.get_env()
									_a     :      Optional[Any]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Union[str, Any]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

									_a     :      Optional[Any]        =							'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

									_a     :      str        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Dict        =							self.get_env()
									_a     :      int        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)

									# next emulate no network
									_a     :      List[Any]        =							[sys.executable, '-c', '\n'.join([load, mock, run]		)]

									# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
									# env["TRANSFORMERS_OFFLINE"] = "0"
									# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
									# self.assertEqual(result.returncode, 1, result.stderr)

									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      int        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[Any]        =							'\nfrom transformers import pipeline\n        '
									_a     :      str        =							'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

									_a     :      List[str]        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
									_a     :      List[Any]        =							self.get_env()
									_a     :      Dict        =							'1'
									_a     :      Dict        =							[sys.executable, '-c', '\n'.join([load, mock, run]		)]
									_a     :      str        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,1	,result.stderr		)
									self.assertIn(
									    'You cannot infer task automatically within `pipeline` when using offline mode'	,result.stderr.decode().replace('\n'	,''		)	,)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[int]        =							'\nfrom transformers import AutoModel\n        '
									_a     :      List[Any]        =							'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

									# baseline - just load from_pretrained with normal network
									_a     :      Union[str, Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Tuple        =							self.get_env()
									_a     :      List[str]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)

									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      Optional[Any]        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)




