






'''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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__lowerCAmelCase       		=      logging.get_logger(__name__)

# General docstring
__lowerCAmelCase       		=      'ResNetConfig'

# Base docstring
__lowerCAmelCase       		=      'microsoft/resnet-50'
__lowerCAmelCase       		=      [1, 2_048, 7, 7]

# Image classification docstring
__lowerCAmelCase       		=      'microsoft/resnet-50'
__lowerCAmelCase       		=      'tiger cat'

__lowerCAmelCase       		=      [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):






			def __init__( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int = 3  ,						__SCREAMING_SNAKE_CASE      :			int = 1  ,						__SCREAMING_SNAKE_CASE      :			str = "relu"					)			->			List[Any]:
							super().__init__()
							a_		:  Union[str, Any]											=      nn.Convad(
							    UpperCamelCase_  ,						UpperCamelCase_  ,						kernel_size=UpperCamelCase_  ,						stride=UpperCamelCase_  ,						padding=kernel_size // 2  ,						bias=UpperCamelCase_					)
							a_		:  List[str]											=      nn.BatchNormad(UpperCamelCase_					)
							a_		:  Optional[Any]											=      ACTaFN[activation] if activation is not None else nn.Identity()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Tensor					)			->			int:
							a_		:  str											=      self.convolution(UpperCamelCase_					)
							a_		:  List[str]											=      self.normalization(UpperCamelCase_					)
							a_		:  Dict											=      self.activation(UpperCamelCase_					)
							return hidden_state





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			ResNetConfig					)			->			Union[str, Any]:
							super().__init__()
							a_		:  Any											=      ResNetConvLayer(
							    config.num_channels  ,						config.embedding_size  ,						kernel_size=7  ,						stride=2  ,						activation=config.hidden_act					)
							a_		:  Optional[int]											=      nn.MaxPoolad(kernel_size=3  ,						stride=2  ,						padding=1					)
							a_		:  Dict											=      config.num_channels







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Tensor					)			->			Any:
							a_		:  List[str]											=      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.'''					)
							a_		:  Tuple											=      self.embedder(UpperCamelCase_					)
							a_		:  str											=      self.pooler(UpperCamelCase_					)
							return embedding





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):






			def __init__( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int = 2					)			->			List[Any]:
							super().__init__()
							a_		:  Union[str, Any]											=      nn.Convad(UpperCamelCase_  ,						UpperCamelCase_  ,						kernel_size=1  ,						stride=UpperCamelCase_  ,						bias=UpperCamelCase_					)
							a_		:  Dict											=      nn.BatchNormad(UpperCamelCase_					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			Tensor					)			->			Optional[int]:
							a_		:  Any											=      self.convolution(UpperCamelCase_					)
							a_		:  str											=      self.normalization(UpperCamelCase_					)
							return hidden_state





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):






			def __init__( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int = 1  ,						__SCREAMING_SNAKE_CASE      :			str = "relu"					)			->			str:
							super().__init__()
							a_		:  Tuple											=      in_channels != out_channels or stride != 1
							a_		:  Optional[int]											=      (
							    ResNetShortCut(UpperCamelCase_  ,						UpperCamelCase_  ,						stride=UpperCamelCase_					) if should_apply_shortcut else nn.Identity()
							)
							a_		:  Optional[Any]											=      nn.Sequential(
							    ResNetConvLayer(UpperCamelCase_  ,						UpperCamelCase_  ,						stride=UpperCamelCase_					)  ,						ResNetConvLayer(UpperCamelCase_  ,						UpperCamelCase_  ,						activation=UpperCamelCase_					)  ,						)
							a_		:  List[str]											=      ACTaFN[activation]







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Tuple					)			->			str:
							a_		:  Tuple											=      hidden_state
							a_		:  str											=      self.layer(UpperCamelCase_					)
							a_		:  int											=      self.shortcut(UpperCamelCase_					)
							hidden_state += residual
							a_		:  Any											=      self.activation(UpperCamelCase_					)
							return hidden_state





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):






			def __init__( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int = 1  ,						__SCREAMING_SNAKE_CASE      :			str = "relu"  ,						__SCREAMING_SNAKE_CASE      :			int = 4					)			->			List[Any]:
							super().__init__()
							a_		:  str											=      in_channels != out_channels or stride != 1
							a_		:  Any											=      out_channels // reduction
							a_		:  str											=      (
							    ResNetShortCut(UpperCamelCase_  ,						UpperCamelCase_  ,						stride=UpperCamelCase_					) if should_apply_shortcut else nn.Identity()
							)
							a_		:  List[Any]											=      nn.Sequential(
							    ResNetConvLayer(UpperCamelCase_  ,						UpperCamelCase_  ,						kernel_size=1					)  ,						ResNetConvLayer(UpperCamelCase_  ,						UpperCamelCase_  ,						stride=UpperCamelCase_					)  ,						ResNetConvLayer(UpperCamelCase_  ,						UpperCamelCase_  ,						kernel_size=1  ,						activation=UpperCamelCase_					)  ,						)
							a_		:  List[str]											=      ACTaFN[activation]







			def 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			List[str]					)			->			Optional[int]:
							a_		:  Union[str, Any]											=      hidden_state
							a_		:  Optional[int]											=      self.layer(UpperCamelCase_					)
							a_		:  str											=      self.shortcut(UpperCamelCase_					)
							hidden_state += residual
							a_		:  List[Any]											=      self.activation(UpperCamelCase_					)
							return hidden_state





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			ResNetConfig  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int  ,						__SCREAMING_SNAKE_CASE      :			int = 2  ,						__SCREAMING_SNAKE_CASE      :			int = 2  ,						)			->			int:
							super().__init__()

							a_		:  Union[str, Any]											=      ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

							a_		:  str											=      nn.Sequential(
							    # downsampling is done in the first layer with stride of 2
							    layer(UpperCamelCase_  ,						UpperCamelCase_  ,						stride=UpperCamelCase_  ,						activation=config.hidden_act					)  ,						*[layer(UpperCamelCase_  ,						UpperCamelCase_  ,						activation=config.hidden_act					) for _ in range(depth - 1					)]  ,						)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Tensor					)			->			List[str]:
							a_		:  Union[str, Any]											=      input
							for layer in self.layers:
											a_		:  str											=      layer(UpperCamelCase_					)
							return hidden_state





class 	SCREAMING_SNAKE_CASE					(					nn.Module       ):






			def __init__( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			ResNetConfig					)			->			List[str]:
							super().__init__()
							a_		:  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(
							    ResNetStage(
							        UpperCamelCase_  ,						config.embedding_size  ,						config.hidden_sizes[0]  ,						stride=2 if config.downsample_in_first_stage else 1  ,						depth=config.depths[0]  ,						)					)
							a_		:  Optional[int]											=      zip(config.hidden_sizes  ,						config.hidden_sizes[1:]					)
							for (in_channels, out_channels), depth in zip(UpperCamelCase_  ,						config.depths[1:]					):
											self.stages.append(ResNetStage(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						depth=UpperCamelCase_					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			Tensor  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			bool = True					)			->			Dict:
							a_		:  Tuple											=      () if output_hidden_states else None

							for stage_module in self.stages:
											if output_hidden_states:
															a_		:  int											=      hidden_states + (hidden_state,)

											a_		:  Any											=      stage_module(UpperCamelCase_					)

							if output_hidden_states:
											a_		:  Tuple											=      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=UpperCamelCase_  ,						hidden_states=UpperCamelCase_  ,						)





class 	SCREAMING_SNAKE_CASE					(					__lowerCamelCase       ):
			snake_case__  =				ResNetConfig
			snake_case__  =				'resnet'
			snake_case__  =				'pixel_values'
			snake_case__  =				True






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







			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			List[str]=False					)			->			Any:
							if isinstance(UpperCamelCase_  ,						UpperCamelCase_					):
											a_		:  List[Any]											=      value





__lowerCAmelCase       		=      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 ([`ResNetConfig`]): 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'

__lowerCAmelCase       		=      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 [`~utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."	,   __lowerCamelCase	,   )
class 	SCREAMING_SNAKE_CASE					(					__lowerCamelCase       ):






			def __init__( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			Dict:
							super().__init__(UpperCamelCase_					)
							a_		:  List[str]											=      config
							a_		:  Union[str, Any]											=      ResNetEmbeddings(UpperCamelCase_					)
							a_		:  Tuple											=      ResNetEncoder(UpperCamelCase_					)
							a_		:  str											=      nn.AdaptiveAvgPoolad((1, 1)					)
							# Initialize weights and apply final processing
							self.post_init()







			@add_start_docstrings_to_model_forward(UpperCamelCase_					)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC  ,						output_type=UpperCamelCase_  ,						config_class=_CONFIG_FOR_DOC  ,						modality='''vision'''  ,						expected_output=_EXPECTED_OUTPUT_SHAPE  ,						)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Tensor  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None					)			->			int:
							a_		:  Dict											=      (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							a_		:  Tuple											=      return_dict if return_dict is not None else self.config.use_return_dict

							a_		:  Any											=      self.embedder(UpperCamelCase_					)

							a_		:  Optional[Any]											=      self.encoder(
							    UpperCamelCase_  ,						output_hidden_states=UpperCamelCase_  ,						return_dict=UpperCamelCase_					)

							a_		:  List[str]											=      encoder_outputs[0]

							a_		:  Any											=      self.pooler(UpperCamelCase_					)

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

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





@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "	,   __lowerCamelCase	,   )
class 	SCREAMING_SNAKE_CASE					(					__lowerCamelCase       ):






			def __init__( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			int:
							super().__init__(UpperCamelCase_					)
							a_		:  Dict											=      config.num_labels
							a_		:  Optional[Any]											=      ResNetModel(UpperCamelCase_					)
							# classification head
							a_		:  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(UpperCamelCase_					)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT  ,						output_type=UpperCamelCase_  ,						config_class=_CONFIG_FOR_DOC  ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,						)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[torch.FloatTensor] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[torch.LongTensor] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None  ,						)			->			Union[str, Any]:
							a_		:  Any											=      return_dict if return_dict is not None else self.config.use_return_dict

							a_		:  Union[str, Any]											=      self.resnet(UpperCamelCase_  ,						output_hidden_states=UpperCamelCase_  ,						return_dict=UpperCamelCase_					)

							a_		:  str											=      outputs.pooler_output if return_dict else outputs[1]

							a_		:  List[Any]											=      self.classifier(UpperCamelCase_					)

							a_		:  Optional[int]											=      None

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

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

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





@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "	,   __lowerCamelCase	,   )
class 	SCREAMING_SNAKE_CASE					(					__lowerCamelCase	,   __lowerCamelCase       ):






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			List[str]:
							super().__init__(UpperCamelCase_					)
							super()._init_backbone(UpperCamelCase_					)

							a_		:  int											=      [config.embedding_size] + config.hidden_sizes
							a_		:  List[str]											=      ResNetEmbeddings(UpperCamelCase_					)
							a_		:  Tuple											=      ResNetEncoder(UpperCamelCase_					)

							# initialize weights and apply final processing
							self.post_init()







			@add_start_docstrings_to_model_forward(UpperCamelCase_					)
			@replace_return_docstrings(output_type=UpperCamelCase_  ,						config_class=_CONFIG_FOR_DOC					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Tensor  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None  ,						__SCREAMING_SNAKE_CASE      :			Optional[bool] = None					)			->			Tuple:
							a_		:  Tuple											=      return_dict if return_dict is not None else self.config.use_return_dict
							a_		:  Any											=      (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)

							a_		:  Any											=      self.embedder(UpperCamelCase_					)

							a_		:  List[Any]											=      self.encoder(UpperCamelCase_  ,						output_hidden_states=UpperCamelCase_  ,						return_dict=UpperCamelCase_					)

							a_		:  str											=      outputs.hidden_states

							a_		:  int											=      ()
							for idx, stage in enumerate(self.stage_names					):
											if stage in self.out_features:
															feature_maps += (hidden_states[idx],)

							if not return_dict:
											a_		:  List[Any]											=      (feature_maps,)
											if output_hidden_states:
															output += (outputs.hidden_states,)
											return output

							return BackboneOutput(
							    feature_maps=UpperCamelCase_  ,						hidden_states=outputs.hidden_states if output_hidden_states else None  ,						attentions=UpperCamelCase_  ,						)












'''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 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):
				if "xprophetnet" in prophetnet_checkpoint_path:
								a_		:  Tuple											=      XLMProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Optional[Any]											=      XLMProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )
				else:
								a_		:  List[Any]											=      ProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Any											=      ProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )

				a_		:  str											=      ['''key_proj''', '''value_proj''', '''query_proj''']

				a_		:  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"]:
								a_		:  List[str]											=      key.split('''.'''      )

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

								a_		:  str											=      False
								for attribute in attributes:
												if attribute in mapping:
																a_		:  Dict											=      mapping[attribute]
																if not hasattr(__A						,		__A      ) and len(__A      ) > 0:
																				a_		:  List[str]											=      attribute
												elif hasattr(__A						,		__A      ):
																a_		:  Union[str, Any]											=      attribute

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

																elif attribute == "key_proj":
																				a_		:  List[Any]											=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
																				a_		:  Optional[int]											=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
																elif attribute == "value_proj":
																				a_		:  Tuple											=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
																				a_		:  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] == 5_12, "We want 512 position_embeddings."
																a_		:  Union[str, Any]											=      nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
																a_		:  Optional[Any]											=      True
																break

												if attribute.isdigit():
																a_		:  Union[str, Any]											=      model[int(__A      )]
																a_		:  str											=      old_model[int(__A      )]
												else:
																a_		:  Tuple											=      getattr(__A						,		__A      )

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

								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(__A      )


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)




