



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


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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :str       ):







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

				SCREAMING_SNAKE_CASE  :						Dict      					= ["key_proj", "value_proj", "query_proj"]

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {
				    "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"]:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= key.split('''.'''       )

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

								SCREAMING_SNAKE_CASE  :						Dict      					= False
								for attribute in attributes:
												if attribute in mapping:
																SCREAMING_SNAKE_CASE  :						Tuple      					= mapping[attribute]
																if not hasattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ) and len(_SCREAMING_SNAKE_CASE       ) > 0:
																				SCREAMING_SNAKE_CASE  :						Tuple      					= attribute
												elif hasattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
																SCREAMING_SNAKE_CASE  :						List[Any]      					= attribute

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

																elif attribute == "key_proj":
																				SCREAMING_SNAKE_CASE  :						Tuple      					= nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																				SCREAMING_SNAKE_CASE  :						str      					= nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																elif attribute == "value_proj":
																				SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																				SCREAMING_SNAKE_CASE  :						Optional[int]      					= nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																SCREAMING_SNAKE_CASE  :						Union[str, 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."
																SCREAMING_SNAKE_CASE  :						List[Any]      					= nn.Parameter(old_model.embed_positions.weight[:5_12, :]       )
																SCREAMING_SNAKE_CASE  :						Dict      					= True
																break

												if attribute.isdigit():
																SCREAMING_SNAKE_CASE  :						Optional[int]      					= model[int(_SCREAMING_SNAKE_CASE       )]
																SCREAMING_SNAKE_CASE  :						List[str]      					= old_model[int(_SCREAMING_SNAKE_CASE       )]
												else:
																SCREAMING_SNAKE_CASE  :						Optional[Any]      					= getattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

																if old_attribute == "":
																				SCREAMING_SNAKE_CASE  :						int      					= old_model
																else:
																				if not hasattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
																								raise ValueError(F'''{old_model} does not have {old_attribute}'''       )
																				SCREAMING_SNAKE_CASE  :						str      					= getattr(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								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(_SCREAMING_SNAKE_CASE       )


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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimesformerModel""",
	    """TimesformerForVideoClassification""",
	    """TimesformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	snake_case_			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




