





"""simple docstring"""





from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


_UpperCamelCase					      =   logging.get_logger(__name__)

# General docstring
_UpperCamelCase					      =   'RegNetConfig'

# Base docstring
_UpperCamelCase					      =   'facebook/regnet-y-040'
_UpperCamelCase					      =   [1, 1088, 7, 7]

# Image classification docstring
_UpperCamelCase					      =   'facebook/regnet-y-040'
_UpperCamelCase					      =   'tabby, tabby cat'

_UpperCamelCase					      =   [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Any    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:int = 3    ,     __lowercase	:int = 1    ,     __lowercase	:int = 1    ,     __lowercase	:Optional[str] = "relu"    ,     ):
											super().__init__()
											__lowerCamelCase   :   Any       =nn.Convad(
											    __lowercase    ,     __lowercase    ,     kernel_size=__lowercase    ,     stride=__lowercase    ,     padding=kernel_size // 2    ,     groups=__lowercase    ,     bias=__lowercase    ,     )
											__lowerCamelCase   :   Optional[int]       =nn.BatchNormad(__lowercase      )
											__lowerCamelCase   :   str       =ACTaFN[activation] if activation is not None else nn.Identity()




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:Any      ):
											__lowerCamelCase   :   List[str]       =self.convolution(__lowercase      )
											__lowerCamelCase   :   List[str]       =self.normalization(__lowercase      )
											__lowerCamelCase   :   str       =self.activation(__lowercase      )
											return hidden_state

class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Optional[int]    ,     __lowercase	:RegNetConfig      ):
											super().__init__()
											__lowerCamelCase   :   int       =RegNetConvLayer(
											    config.num_channels    ,     config.embedding_size    ,     kernel_size=3    ,     stride=2    ,     activation=config.hidden_act      )
											__lowerCamelCase   :   str       =config.num_channels




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =pixel_values.shape[1]
											if num_channels != self.num_channels:
																		raise ValueError(
																		    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''      )
											__lowerCamelCase   :   List[Any]       =self.embedder(__lowercase      )
											return hidden_state

class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Any    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:int = 2      ):
											super().__init__()
											__lowerCamelCase   :   List[Any]       =nn.Convad(__lowercase    ,     __lowercase    ,     kernel_size=1    ,     stride=__lowercase    ,     bias=__lowercase      )
											__lowerCamelCase   :   Any       =nn.BatchNormad(__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Tensor      ):
											__lowerCamelCase   :   int       =self.convolution(__lowercase      )
											__lowerCamelCase   :   Dict       =self.normalization(__lowercase      )
											return hidden_state

class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Any    ,     __lowercase	:int    ,     __lowercase	:int      ):
											super().__init__()

											__lowerCamelCase   :   List[str]       =nn.AdaptiveAvgPoolad((1, 1)      )
											__lowerCamelCase   :   List[Any]       =nn.Sequential(
											    nn.Convad(__lowercase    ,     __lowercase    ,     kernel_size=1      )    ,     nn.ReLU()    ,     nn.Convad(__lowercase    ,     __lowercase    ,     kernel_size=1      )    ,     nn.Sigmoid()    ,     )




				def        __lowercase	(  self	:int    ,     __lowercase	:Optional[int]      ):
											# b c h w -> b c 1 1
											__lowerCamelCase   :   int       =self.pooler(__lowercase      )
											__lowerCamelCase   :   Optional[int]       =self.attention(__lowercase      )
											__lowerCamelCase   :   List[Any]       =hidden_state * attention
											return hidden_state

class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Union[str, Any]    ,     __lowercase	:RegNetConfig    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:int = 1      ):
											super().__init__()
											__lowerCamelCase   :   Any       =in_channels != out_channels or stride != 1
											__lowerCamelCase   :   str       =max(1    ,     out_channels // config.groups_width      )
											__lowerCamelCase   :   Optional[Any]       =(
											    RegNetShortCut(__lowercase    ,     __lowercase    ,     stride=__lowercase      ) if should_apply_shortcut else nn.Identity()
											)
											__lowerCamelCase   :   List[str]       =nn.Sequential(
											    RegNetConvLayer(__lowercase    ,     __lowercase    ,     kernel_size=1    ,     activation=config.hidden_act      )    ,     RegNetConvLayer(__lowercase    ,     __lowercase    ,     stride=__lowercase    ,     groups=__lowercase    ,     activation=config.hidden_act      )    ,     RegNetConvLayer(__lowercase    ,     __lowercase    ,     kernel_size=1    ,     activation=__lowercase      )    ,     )
											__lowerCamelCase   :   Optional[int]       =ACTaFN[config.hidden_act]




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:int      ):
											__lowerCamelCase   :   Tuple       =hidden_state
											__lowerCamelCase   :   Optional[int]       =self.layer(__lowercase      )
											__lowerCamelCase   :   Any       =self.shortcut(__lowercase      )
											hidden_state += residual
											__lowerCamelCase   :   Dict       =self.activation(__lowercase      )
											return hidden_state

class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Tuple    ,     __lowercase	:RegNetConfig    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:int = 1      ):
											super().__init__()
											__lowerCamelCase   :   List[Any]       =in_channels != out_channels or stride != 1
											__lowerCamelCase   :   int       =max(1    ,     out_channels // config.groups_width      )
											__lowerCamelCase   :   Tuple       =(
											    RegNetShortCut(__lowercase    ,     __lowercase    ,     stride=__lowercase      ) if should_apply_shortcut else nn.Identity()
											)
											__lowerCamelCase   :   Any       =nn.Sequential(
											    RegNetConvLayer(__lowercase    ,     __lowercase    ,     kernel_size=1    ,     activation=config.hidden_act      )    ,     RegNetConvLayer(__lowercase    ,     __lowercase    ,     stride=__lowercase    ,     groups=__lowercase    ,     activation=config.hidden_act      )    ,     RegNetSELayer(__lowercase    ,     reduced_channels=int(round(in_channels / 4      )      )      )    ,     RegNetConvLayer(__lowercase    ,     __lowercase    ,     kernel_size=1    ,     activation=__lowercase      )    ,     )
											__lowerCamelCase   :   Optional[int]       =ACTaFN[config.hidden_act]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											__lowerCamelCase   :   Optional[Any]       =hidden_state
											__lowerCamelCase   :   List[str]       =self.layer(__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =self.shortcut(__lowercase      )
											hidden_state += residual
											__lowerCamelCase   :   str       =self.activation(__lowercase      )
											return hidden_state

class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Optional[Any]    ,     __lowercase	:RegNetConfig    ,     __lowercase	:int    ,     __lowercase	:int    ,     __lowercase	:int = 2    ,     __lowercase	:int = 2    ,     ):
											super().__init__()

											__lowerCamelCase   :   Union[str, Any]       =RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

											__lowerCamelCase   :   int       =nn.Sequential(
											    # downsampling is done in the first layer with stride of 2
											    layer(
											        __lowercase    ,     __lowercase    ,     __lowercase    ,     stride=__lowercase    ,     )    ,     *[layer(__lowercase    ,     __lowercase    ,     __lowercase      ) for _ in range(depth - 1      )]    ,     )




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:List[str]      ):
											__lowerCamelCase   :   Optional[Any]       =self.layers(__lowercase      )
											return hidden_state

class 		SCREAMING_SNAKE_CASE_				(     nn.Module ):
				"""simple docstring"""




				def __init__(  self	:Optional[Any]    ,     __lowercase	:RegNetConfig      ):
											super().__init__()
											__lowerCamelCase   :   Dict       =nn.ModuleList([]      )
											# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
											self.stages.append(
											    RegNetStage(
											        __lowercase    ,     config.embedding_size    ,     config.hidden_sizes[0]    ,     stride=2 if config.downsample_in_first_stage else 1    ,     depth=config.depths[0]    ,     )      )
											__lowerCamelCase   :   Optional[Any]       =zip(config.hidden_sizes    ,     config.hidden_sizes[1:]      )
											for (in_channels, out_channels), depth in zip(__lowercase    ,     config.depths[1:]      ):
																		self.stages.append(RegNetStage(__lowercase    ,     __lowercase    ,     __lowercase    ,     depth=__lowercase      )      )




				def        __lowercase	(  self	:int    ,     __lowercase	:Tensor    ,     __lowercase	:bool = False    ,     __lowercase	:bool = True      ):
											__lowerCamelCase   :   Any       =() if output_hidden_states else None

											for stage_module in self.stages:
																		if output_hidden_states:
																									__lowerCamelCase   :   Optional[int]       =hidden_states + (hidden_state,)

																		__lowerCamelCase   :   Dict       =stage_module(__lowercase      )

											if output_hidden_states:
																		__lowerCamelCase   :   int       =hidden_states + (hidden_state,)

											if not return_dict:
																		return tuple(v for v in [hidden_state, hidden_states] if v is not None      )

											return BaseModelOutputWithNoAttention(last_hidden_state=__lowercase    ,     hidden_states=__lowercase      )

class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		RegNetConfig
				__snake_case :							Tuple									=		"""regnet"""
				__snake_case :							List[Any]									=		"""pixel_values"""
				__snake_case :							Dict									=		True




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:Optional[int]      ):
											if isinstance(__lowercase    ,     nn.Convad      ):
																		nn.init.kaiming_normal_(module.weight    ,     mode='''fan_out'''    ,     nonlinearity='''relu'''      )
											elif isinstance(__lowercase    ,     (nn.BatchNormad, nn.GroupNorm)      ):
																		nn.init.constant_(module.weight    ,     1      )
																		nn.init.constant_(module.bias    ,     0      )




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:Any    ,     __lowercase	:Tuple=False      ):
											if isinstance(__lowercase    ,     __lowercase      ):
																		__lowerCamelCase   :   List[str]       =value

_UpperCamelCase					      =   r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

_UpperCamelCase					      =   r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""							,	snake_case__							,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def __init__(  self	:int    ,     __lowercase	:List[str]      ):
											super().__init__(__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =config
											__lowerCamelCase   :   Optional[int]       =RegNetEmbeddings(__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =RegNetEncoder(__lowercase      )
											__lowerCamelCase   :   Union[str, Any]       =nn.AdaptiveAvgPoolad((1, 1)      )
											# Initialize weights and apply final processing
											self.post_init()




				@add_start_docstrings_to_model_forward(__lowercase      )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC    ,     output_type=__lowercase    ,     config_class=_CONFIG_FOR_DOC    ,     modality='''vision'''    ,     expected_output=_EXPECTED_OUTPUT_SHAPE    ,     )
				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:Tensor    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[bool] = None      ):
											__lowerCamelCase   :   str       =(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCamelCase   :   int       =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase   :   Dict       =self.embedder(__lowercase      )

											__lowerCamelCase   :   Any       =self.encoder(
											    __lowercase    ,     output_hidden_states=__lowercase    ,     return_dict=__lowercase      )

											__lowerCamelCase   :   Any       =encoder_outputs[0]

											__lowerCamelCase   :   Union[str, Any]       =self.pooler(__lowercase      )

											if not return_dict:
																		return (last_hidden_state, pooled_output) + encoder_outputs[1:]

											return BaseModelOutputWithPoolingAndNoAttention(
											    last_hidden_state=__lowercase    ,     pooler_output=__lowercase    ,     hidden_states=encoder_outputs.hidden_states    ,     )

@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """							,	snake_case__							,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def __init__(  self	:List[str]    ,     __lowercase	:Tuple      ):
											super().__init__(__lowercase      )
											__lowerCamelCase   :   Optional[int]       =config.num_labels
											__lowerCamelCase   :   List[str]       =RegNetModel(__lowercase      )
											# classification head
											__lowerCamelCase   :   Optional[Any]       =nn.Sequential(
											    nn.Flatten()    ,     nn.Linear(config.hidden_sizes[-1]    ,     config.num_labels      ) if config.num_labels > 0 else nn.Identity()    ,     )
											# initialize weights and apply final processing
											self.post_init()




				@add_start_docstrings_to_model_forward(__lowercase      )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,     output_type=__lowercase    ,     config_class=_CONFIG_FOR_DOC    ,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,     )
				def        __lowercase	(  self	:str    ,     __lowercase	:Optional[torch.FloatTensor] = None    ,     __lowercase	:Optional[torch.LongTensor] = None    ,     __lowercase	:Optional[bool] = None    ,     __lowercase	:Optional[bool] = None    ,     ):
											__lowerCamelCase   :   str       =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase   :   List[str]       =self.regnet(__lowercase    ,     output_hidden_states=__lowercase    ,     return_dict=__lowercase      )

											__lowerCamelCase   :   Tuple       =outputs.pooler_output if return_dict else outputs[1]

											__lowerCamelCase   :   List[Any]       =self.classifier(__lowercase      )

											__lowerCamelCase   :   Tuple       =None

											if labels is not None:
																		if self.config.problem_type is None:
																									if self.num_labels == 1:
																																__lowerCamelCase   :   Optional[int]       ='''regression'''
																									elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																__lowerCamelCase   :   Optional[Any]       ='''single_label_classification'''
																									else:
																																__lowerCamelCase   :   int       ='''multi_label_classification'''
																		if self.config.problem_type == "regression":
																									__lowerCamelCase   :   Optional[int]       =MSELoss()
																									if self.num_labels == 1:
																																__lowerCamelCase   :   Dict       =loss_fct(logits.squeeze()    ,     labels.squeeze()      )
																									else:
																																__lowerCamelCase   :   Optional[int]       =loss_fct(__lowercase    ,     __lowercase      )
																		elif self.config.problem_type == "single_label_classification":
																									__lowerCamelCase   :   List[Any]       =CrossEntropyLoss()
																									__lowerCamelCase   :   int       =loss_fct(logits.view(-1    ,     self.num_labels      )    ,     labels.view(-1      )      )
																		elif self.config.problem_type == "multi_label_classification":
																									__lowerCamelCase   :   str       =BCEWithLogitsLoss()
																									__lowerCamelCase   :   Optional[Any]       =loss_fct(__lowercase    ,     __lowercase      )

											if not return_dict:
																		__lowerCamelCase   :   Optional[Any]       =(logits,) + outputs[2:]
																		return (loss,) + output if loss is not None else output

											return ImageClassifierOutputWithNoAttention(loss=__lowercase    ,     logits=__lowercase    ,     hidden_states=outputs.hidden_states      )











"""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


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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







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

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''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"]:
														__lowerCamelCase   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

														__lowerCamelCase   :   Optional[Any]       =False
														for attribute in attributes:
																					if attribute in mapping:
																												__lowerCamelCase   :   Optional[Any]       =mapping[attribute]
																												if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
																																			__lowerCamelCase   :   Any       =attribute
																					elif hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   Any       =attribute

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

																												elif attribute == "key_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
																																			__lowerCamelCase   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   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."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

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

																												if old_attribute == "":
																																			__lowerCamelCase   :   Dict       =old_model
																												else:
																																			if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}' )
																																			__lowerCamelCase   :   Tuple       =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__":
							_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)




