





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
					import tensorflow as tf


UpperCAmelCase_ 							=       logging.get_logger(__name__)
@dataclass
class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		[
					    '''no_inference''',
					    '''no_cuda''',
					    '''no_tpu''',
					    '''no_speed''',
					    '''no_memory''',
					    '''no_env_print''',
					    '''no_multi_process''',
					]




					def __init__(						self			,  **lowerCAmelCase_					)						->							Dict:
										for deprecated_arg in self.deprecated_args:
															if deprecated_arg in kwargs:
																				_snake_case													=			deprecated_arg[3:]
																				_snake_case													=			not kwargs.pop(lowerCAmelCase_					)
																				logger.warning(
																				    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
																				    F''' {positive_arg}={kwargs[positive_arg]}'''					)
										_snake_case													=			kwargs.pop('tpu_name'			,  self.tpu_name					)
										_snake_case													=			kwargs.pop('device_idx'			,  self.device_idx					)
										_snake_case													=			kwargs.pop('eager_mode'			,  self.eager_mode					)
										_snake_case													=			kwargs.pop('use_xla'			,  self.use_xla					)
										super().__init__(**lowerCAmelCase_					)

					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={'''help''': '''Name of TPU'''}							,					)
					lowerCAmelCase_							=		field(
					    default=0							,					metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}							,					)
					lowerCAmelCase_							=		field(default=_lowerCamelCase							,					metadata={'''help''': '''Benchmark models in eager model.'''}   )
					lowerCAmelCase_							=		field(
					    default=_lowerCamelCase							,					metadata={
					        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
					    }							,					)




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
										requires_backends(self			,  ['tf']					)
										_snake_case													=			None
										if self.tpu:
															try:
																				if self.tpu_name:
																									_snake_case													=			tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name					)
																				else:
																									_snake_case													=			tf.distribute.cluster_resolver.TPUClusterResolver()
															except ValueError:
																				_snake_case													=			None
										return tpu




					@cached_property
					def 		lowerCAmelCase     (						self					)						->							Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
										requires_backends(self			,  ['tf']					)
										if self.is_tpu:
															tf.config.experimental_connect_to_cluster(self._setup_tpu					)
															tf.tpu.experimental.initialize_tpu_system(self._setup_tpu					)

															_snake_case													=			tf.distribute.TPUStrategy(self._setup_tpu					)
										else:
															# currently no multi gpu is allowed
															if self.is_gpu:
																				# TODO: Currently only single GPU is supported
																				tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,  'GPU'					)
																				_snake_case													=			tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''					)
															else:
																				tf.config.set_visible_devices([]			,  'GPU'					)  # disable GPU
																				_snake_case													=			tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''					)

										return strategy




					@property
					def 		lowerCAmelCase     (						self					)						->							bool:
										requires_backends(self			,  ['tf']					)
										return self._setup_tpu is not None




					@property
					def 		lowerCAmelCase     (						self					)						->							"tf.distribute.Strategy":
										requires_backends(self			,  ['tf']					)
										return self._setup_strategy




					@property
					def 		lowerCAmelCase     (						self					)						->							Union[str, Any]:
										requires_backends(self			,  ['tf']					)
										return tf.config.list_physical_devices('GPU'					)




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										requires_backends(self			,  ['tf']					)
										if self.cuda:
															return len(self.gpu_list					)
										return 0






					@property
					def 		lowerCAmelCase     (						self					)						->							bool:
										return self.n_gpu > 0










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


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





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str  )     ->	Union[str, Any]:



					'''simple docstring'''




					if "xprophetnet" in prophetnet_checkpoint_path:
										_snake_case													=			XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )
					else:
										_snake_case													=			ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )

					_snake_case													=			['key_proj', 'value_proj', 'query_proj']

					_snake_case													=			{
					    '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													=			key.split('.'  )

										if attributes[0] == "lm_head":
															_snake_case													=			prophet
															_snake_case													=			prophet_old
										else:
															_snake_case													=			prophet.prophetnet
															_snake_case													=			prophet_old.model

										_snake_case													=			False
										for attribute in attributes:
															if attribute in mapping:
																				_snake_case													=			mapping[attribute]
																				if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ) and len(UpperCamelCase__  ) > 0:
																									_snake_case													=			attribute
															elif hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																				_snake_case													=			attribute

															if attribute == "weight":
																				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																				_snake_case													=			old_model.weight
																				logger.info(F'''{attribute} is initialized.'''  )
																				_snake_case													=			True
																				break
															elif attribute == "bias":
																				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																				_snake_case													=			old_model.bias
																				logger.info(F'''{attribute} is initialized'''  )
																				_snake_case													=			True
																				break
															elif attribute in special_keys and hasattr(UpperCamelCase__    ,      'in_proj_weight'  ):
																				_snake_case													=			old_model.in_proj_weight.shape[0] // 3
																				_snake_case													=			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													=			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[:embed_dim]  )

																				elif attribute == "key_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
																				elif attribute == "value_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
																				_snake_case													=			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."
																				_snake_case													=			nn.Parameter(old_model.embed_positions.weight[:512, :]  )
																				_snake_case													=			True
																				break

															if attribute.isdigit():
																				_snake_case													=			model[int(UpperCamelCase__  )]
																				_snake_case													=			old_model[int(UpperCamelCase__  )]
															else:
																				_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

																				if old_attribute == "":
																									_snake_case													=			old_model
																				else:
																									if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																														raise ValueError(F'''{old_model} does not have {old_attribute}'''  )
																									_snake_case													=			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__":
					UpperCAmelCase_ 							=       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."""
					)
					UpperCAmelCase_ 							=       parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



