





'''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


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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if "xprophetnet" in prophetnet_checkpoint_path:
						__lowercase			  =							XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_			)
						__lowercase  ,						__lowercase			  =							XLMProphetNetForConditionalGeneration.from_pretrained(
						    lowerCamelCase_						,    output_loading_info=lowerCamelCase_			)
			else:
						__lowercase			  =							ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_			)
						__lowercase  ,						__lowercase			  =							ProphetNetForConditionalGeneration.from_pretrained(
						    lowerCamelCase_						,    output_loading_info=lowerCamelCase_			)

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

			__lowercase			  =							{
			    '''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"]:
						__lowercase			  =							key.split('''.'''			)

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

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

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

												elif attribute == "key_proj":
															__lowercase			  =							nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]			)
															__lowercase			  =							nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]			)
												elif attribute == "value_proj":
															__lowercase			  =							nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]			)
															__lowercase			  =							nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]			)
												__lowercase			  =							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_1_2, "We want 512 position_embeddings."
												__lowercase			  =							nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]			)
												__lowercase			  =							True
												break

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

												if old_attribute == "":
															__lowercase			  =							old_model
												else:
															if not hasattr(lowerCamelCase_						,    lowerCamelCase_			):
																		raise ValueError(f"{old_model} does not have {old_attribute}"			)
															__lowercase			  =							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__":
	_SCREAMING_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.'''
	)
	_SCREAMING_SNAKE_CASE   =      parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)












'''simple docstring'''



from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			int     =    ["pixel_values"]


	def __init__(self       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = PILImageResampling.BILINEAR       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = True       ,_lowerCamelCase = 1 / 255       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							None:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase	)
				__lowercase			  =							size if size is not None else {'''shortest_edge''': 256}
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				__lowercase			  =							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,param_name='''crop_size'''	)
				__lowercase			  =							do_resize
				__lowercase			  =							size
				__lowercase			  =							resample
				__lowercase			  =							do_center_crop
				__lowercase			  =							crop_size
				__lowercase			  =							do_rescale
				__lowercase			  =							rescale_factor
				__lowercase			  =							do_normalize
				__lowercase			  =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__lowercase			  =							image_std if image_std is not None else IMAGENET_STANDARD_STD


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = PILImageResampling.BICUBIC       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				if "shortest_edge" not in size:
							raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"	)
				__lowercase			  =							get_resize_output_image_size(_lowerCamelCase       ,size=size['''shortest_edge''']       ,default_to_square=_lowerCamelCase	)
				return resize(_lowerCamelCase       ,size=_lowerCamelCase       ,resample=_lowerCamelCase       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase			  =							get_size_dict(_lowerCamelCase	)
				if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"	)
				return center_crop(_lowerCamelCase       ,size=(size['''height'''], size['''width'''])       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase	)							->							np.ndarray:







				'''simple docstring'''





				return rescale(_lowerCamelCase       ,scale=_lowerCamelCase       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				return normalize(_lowerCamelCase       ,mean=_lowerCamelCase       ,std=_lowerCamelCase       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = None       ,_lowerCamelCase = ChannelDimension.FIRST       ,**_lowerCamelCase       ,)							->							Any:







				'''simple docstring'''





				__lowercase			  =							do_resize if do_resize is not None else self.do_resize
				__lowercase			  =							size if size is not None else self.size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				__lowercase			  =							resample if resample is not None else self.resample
				__lowercase			  =							do_center_crop if do_center_crop is not None else self.do_center_crop
				__lowercase			  =							crop_size if crop_size is not None else self.crop_size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,param_name='''crop_size'''	)
				__lowercase			  =							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase			  =							rescale_factor if rescale_factor is not None else self.rescale_factor
				__lowercase			  =							do_normalize if do_normalize is not None else self.do_normalize
				__lowercase			  =							image_mean if image_mean is not None else self.image_mean
				__lowercase			  =							image_std if image_std is not None else self.image_std

				__lowercase			  =							make_list_of_images(_lowerCamelCase	)

				if not valid_images(_lowerCamelCase	):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

				if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.'''	)

				if do_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop is True.'''	)

				if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

				if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

				# All transformations expect numpy arrays.
				__lowercase			  =							[to_numpy_array(_lowerCamelCase	) for image in images]

				if do_resize:
							__lowercase			  =							[self.resize(image=_lowerCamelCase       ,size=_lowerCamelCase       ,resample=_lowerCamelCase	) for image in images]

				if do_center_crop:
							__lowercase			  =							[self.center_crop(image=_lowerCamelCase       ,size=_lowerCamelCase	) for image in images]

				if do_rescale:
							__lowercase			  =							[self.rescale(image=_lowerCamelCase       ,scale=_lowerCamelCase	) for image in images]

				if do_normalize:
							__lowercase			  =							[self.normalize(image=_lowerCamelCase       ,mean=_lowerCamelCase       ,std=_lowerCamelCase	) for image in images]

				__lowercase			  =							[to_channel_dimension_format(_lowerCamelCase       ,_lowerCamelCase	) for image in images]

				__lowercase			  =							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase       ,tensor_type=_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							str:







				'''simple docstring'''





				__lowercase			  =							outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(_lowerCamelCase	) != len(_lowerCamelCase	):
										raise ValueError(
										    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''	)

							if is_torch_tensor(_lowerCamelCase	):
										__lowercase			  =							target_sizes.numpy()

							__lowercase			  =							[]

							for idx in range(len(_lowerCamelCase	)	):
										__lowercase			  =							torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0	)       ,size=target_sizes[idx]       ,mode='''bilinear'''       ,align_corners=_lowerCamelCase	)
										__lowercase			  =							resized_logits[0].argmax(dim=0	)
										semantic_segmentation.append(_lowerCamelCase	)
				else:
							__lowercase			  =							logits.argmax(dim=1	)
							__lowercase			  =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

				return semantic_segmentation





