



'''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"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "google/vivit-b-16x2-kinetics400": (
        "https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[str]							=  '''vivit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=[2, 16, 16]		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu_fast"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-06		,SCREAMING_SNAKE_CASE__=True		,**SCREAMING_SNAKE_CASE__		,)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[Any]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Any						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :str						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Any						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  layer_norm_eps

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  image_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_frames
             __SCREAMING_SNAKE_CASE   :Any						=  tubelet_size
             __SCREAMING_SNAKE_CASE   :str						=  num_channels
             __SCREAMING_SNAKE_CASE   :Any						=  qkv_bias

             super().__init__(**SCREAMING_SNAKE_CASE__							)