


"""simple docstring"""



import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

lowercase_							=							collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowercase_							=							'https://storage.googleapis.com/cvdf-datasets/mnist/'
def      UpperCAmelCase     (       _lowercase :   Optional[Any]	)						-> int:


	"""simple docstring"""




	lowerCAmelCase_      							=			numpy.dtype(numpy.uintaa	).newbyteorder('''>'''	)
	return numpy.frombuffer(bytestream.read(4	)			,   dtype=__UpperCAmelCase	)[0]
@deprecated(__UpperCAmelCase			,   '''Please use tf.data to implement this functionality.'''	)
def      UpperCAmelCase     (       _lowercase :   List[str]	)						-> Union[str, Any]:


	"""simple docstring"""




	print('''Extracting'''			,   f.name	)
	with gzip.GzipFile(fileobj=__UpperCAmelCase	) as bytestream:
		lowerCAmelCase_      							=			_readaa(__UpperCAmelCase	)
		if magic != 2_0_5_1:
			raise ValueError(
			    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)	)
		lowerCAmelCase_      							=			_readaa(__UpperCAmelCase	)
		lowerCAmelCase_      							=			_readaa(__UpperCAmelCase	)
		lowerCAmelCase_      							=			_readaa(__UpperCAmelCase	)
		lowerCAmelCase_      							=			bytestream.read(rows * cols * num_images	)
		lowerCAmelCase_      							=			numpy.frombuffer(__UpperCAmelCase			,   dtype=numpy.uinta	)
		lowerCAmelCase_      							=			data.reshape(__UpperCAmelCase			,   __UpperCAmelCase			,   __UpperCAmelCase			,   1	)
		return data
@deprecated(__UpperCAmelCase			,   '''Please use tf.one_hot on tensors.'''	)
def      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   Any	)						-> List[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			labels_dense.shape[0]
	lowerCAmelCase_      							=			numpy.arange(__UpperCAmelCase	) * num_classes
	lowerCAmelCase_      							=			numpy.zeros((num_labels, num_classes)	)
	lowerCAmelCase_      							=			1
	return labels_one_hot
@deprecated(__UpperCAmelCase			,   '''Please use tf.data to implement this functionality.'''	)
def      UpperCAmelCase     (       _lowercase :   Optional[int]			,   _lowercase :   List[Any]=False			,   _lowercase :   Tuple=1_0	)						-> str:


	"""simple docstring"""




	print('''Extracting'''			,   f.name	)
	with gzip.GzipFile(fileobj=__UpperCAmelCase	) as bytestream:
		lowerCAmelCase_      							=			_readaa(__UpperCAmelCase	)
		if magic != 2_0_4_9:
			raise ValueError(
			    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)	)
		lowerCAmelCase_      							=			_readaa(__UpperCAmelCase	)
		lowerCAmelCase_      							=			bytestream.read(__UpperCAmelCase	)
		lowerCAmelCase_      							=			numpy.frombuffer(__UpperCAmelCase			,   dtype=numpy.uinta	)
		if one_hot:
			return _dense_to_one_hot(__UpperCAmelCase			,   __UpperCAmelCase	)
		return labels







class    __a						:







				@deprecated(
				    a_      ,	'''Please use alternatives such as official/mnist/_DataSet.py'''
				    ''' from tensorflow/models.'''      ,	)
				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase=False      ,	UpperCAmelCase=False      ,	UpperCAmelCase=dtypes.floataa      ,	UpperCAmelCase=True      ,	UpperCAmelCase=None      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			random_seed.get_seed(a_   )
					# If op level seed is not set, use whatever graph level seed is returned
					numpy.random.seed(seeda if seed is None else seeda   )
					lowerCAmelCase_      							=			dtypes.as_dtype(a_   ).base_dtype
					if dtype not in (dtypes.uinta, dtypes.floataa):
						raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype   )
					if fake_data:
						lowerCAmelCase_      							=			1_0000
						lowerCAmelCase_      							=			one_hot
					else:
						assert (
						    images.shape[0] == labels.shape[0]
						), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
						lowerCAmelCase_      							=			images.shape[0]

						# Convert shape from [num examples, rows, columns, depth]
						# to [num examples, rows*columns] (assuming depth == 1)
						if reshape:
							assert images.shape[3] == 1
							lowerCAmelCase_      							=			images.reshape(
							    images.shape[0]      ,	images.shape[1] * images.shape[2]   )
						if dtype == dtypes.floataa:
							# Convert from [0, 255] -> [0.0, 1.0].
							lowerCAmelCase_      							=			images.astype(numpy.floataa   )
							lowerCAmelCase_      							=			numpy.multiply(a_      ,	1.0 / 255.0   )
					lowerCAmelCase_      							=			images
					lowerCAmelCase_      							=			labels
					lowerCAmelCase_      							=			0
					lowerCAmelCase_      							=			0







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return self._images







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return self._labels







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return self._num_examples







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return self._epochs_completed







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase=False      ,	UpperCAmelCase=True   ):

					'''simple docstring'''






					if fake_data:
						lowerCAmelCase_      							=			[1] * 784
						lowerCAmelCase_      							=			[1] + [0] * 9 if self.one_hot else 0
						return (
						    [fake_image for _ in range(a_   )],
						    [fake_label for _ in range(a_   )],
						)
					lowerCAmelCase_      							=			self._index_in_epoch
					# Shuffle for the first epoch
					if self._epochs_completed == 0 and start == 0 and shuffle:
						lowerCAmelCase_      							=			numpy.arange(self._num_examples   )
						numpy.random.shuffle(a_   )
						lowerCAmelCase_      							=			self.images[perma]
						lowerCAmelCase_      							=			self.labels[perma]
					# Go to the next epoch
					if start + batch_size > self._num_examples:
						# Finished epoch
						self._epochs_completed += 1
						# Get the rest examples in this epoch
						lowerCAmelCase_      							=			self._num_examples - start
						lowerCAmelCase_      							=			self._images[start : self._num_examples]
						lowerCAmelCase_      							=			self._labels[start : self._num_examples]
						# Shuffle the data
						if shuffle:
							lowerCAmelCase_      							=			numpy.arange(self._num_examples   )
							numpy.random.shuffle(a_   )
							lowerCAmelCase_      							=			self.images[perm]
							lowerCAmelCase_      							=			self.labels[perm]
						# Start next epoch
						lowerCAmelCase_      							=			0
						lowerCAmelCase_      							=			batch_size - rest_num_examples
						lowerCAmelCase_      							=			self._index_in_epoch
						lowerCAmelCase_      							=			self._images[start:end]
						lowerCAmelCase_      							=			self._labels[start:end]
						return (
						    numpy.concatenate((images_rest_part, images_new_part)      ,	axis=0   ),
						    numpy.concatenate((labels_rest_part, labels_new_part)      ,	axis=0   ),
						)
					else:
						self._index_in_epoch += batch_size
						lowerCAmelCase_      							=			self._index_in_epoch
						return self._images[start:end], self._labels[start:end]




@deprecated(__UpperCAmelCase			,   '''Please write your own downloading logic.'''	)
def      UpperCAmelCase     (       _lowercase :   int			,   _lowercase :   List[str]			,   _lowercase :   int	)						-> Dict:


	"""simple docstring"""




	if not gfile.Exists(__UpperCAmelCase	):
		gfile.MakeDirs(__UpperCAmelCase	)
	lowerCAmelCase_      							=			os.path.join(__UpperCAmelCase			,   __UpperCAmelCase	)
	if not gfile.Exists(__UpperCAmelCase	):
		urllib.request.urlretrieve(__UpperCAmelCase			,   __UpperCAmelCase	)  # noqa: S310
		with gfile.GFile(__UpperCAmelCase	) as f:
			lowerCAmelCase_      							=			f.size()
		print('''Successfully downloaded'''			,   __UpperCAmelCase			,   __UpperCAmelCase			,   '''bytes.'''	)
	return filepath





@deprecated(
    __UpperCAmelCase			,   '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''	)
def      UpperCAmelCase     (       _lowercase :   List[str]			,   _lowercase :   Optional[int]=False			,   _lowercase :   Optional[int]=False			,   _lowercase :   int=dtypes.floataa			,   _lowercase :   List[str]=True			,   _lowercase :   str=5_0_0_0			,   _lowercase :   List[str]=None			,   _lowercase :   Optional[Any]=DEFAULT_SOURCE_URL			,   )						-> List[Any]:


	"""simple docstring"""




	if fake_data:

		def fake():
			return _DataSet(
			    []			,   []			,   fake_data=__UpperCAmelCase			,   one_hot=__UpperCAmelCase			,   dtype=__UpperCAmelCase			,   seed=__UpperCAmelCase	)

		lowerCAmelCase_      							=			fake()
		lowerCAmelCase_      							=			fake()
		lowerCAmelCase_      							=			fake()
		return _Datasets(train=__UpperCAmelCase			,   validation=__UpperCAmelCase			,   test=__UpperCAmelCase	)

	if not source_url:  # empty string check
		lowerCAmelCase_      							=			DEFAULT_SOURCE_URL

	lowerCAmelCase_      							=			'train-images-idx3-ubyte.gz'
	lowerCAmelCase_      							=			'train-labels-idx1-ubyte.gz'
	lowerCAmelCase_      							=			't10k-images-idx3-ubyte.gz'
	lowerCAmelCase_      							=			't10k-labels-idx1-ubyte.gz'

	lowerCAmelCase_      							=			_maybe_download(
	    __UpperCAmelCase			,   __UpperCAmelCase			,   source_url + train_images_file	)
	with gfile.Open(__UpperCAmelCase			,   '''rb'''	) as f:
		lowerCAmelCase_      							=			_extract_images(__UpperCAmelCase	)

	lowerCAmelCase_      							=			_maybe_download(
	    __UpperCAmelCase			,   __UpperCAmelCase			,   source_url + train_labels_file	)
	with gfile.Open(__UpperCAmelCase			,   '''rb'''	) as f:
		lowerCAmelCase_      							=			_extract_labels(__UpperCAmelCase			,   one_hot=__UpperCAmelCase	)

	lowerCAmelCase_      							=			_maybe_download(
	    __UpperCAmelCase			,   __UpperCAmelCase			,   source_url + test_images_file	)
	with gfile.Open(__UpperCAmelCase			,   '''rb'''	) as f:
		lowerCAmelCase_      							=			_extract_images(__UpperCAmelCase	)

	lowerCAmelCase_      							=			_maybe_download(
	    __UpperCAmelCase			,   __UpperCAmelCase			,   source_url + test_labels_file	)
	with gfile.Open(__UpperCAmelCase			,   '''rb'''	) as f:
		lowerCAmelCase_      							=			_extract_labels(__UpperCAmelCase			,   one_hot=__UpperCAmelCase	)

	if not 0 <= validation_size <= len(__UpperCAmelCase	):
		lowerCAmelCase_      							=			(
		    'Validation size should be between 0 and '
		    F"""{len(__UpperCAmelCase	)}. Received: {validation_size}."""
		)
		raise ValueError(__UpperCAmelCase	)

	lowerCAmelCase_      							=			train_images[:validation_size]
	lowerCAmelCase_      							=			train_labels[:validation_size]
	lowerCAmelCase_      							=			train_images[validation_size:]
	lowerCAmelCase_      							=			train_labels[validation_size:]

	lowerCAmelCase_      							=			{'dtype': dtype, 'reshape': reshape, 'seed': seed}

	lowerCAmelCase_      							=			_DataSet(__UpperCAmelCase			,   __UpperCAmelCase			,   **__UpperCAmelCase	)
	lowerCAmelCase_      							=			_DataSet(__UpperCAmelCase			,   __UpperCAmelCase			,   **__UpperCAmelCase	)
	lowerCAmelCase_      							=			_DataSet(__UpperCAmelCase			,   __UpperCAmelCase			,   **__UpperCAmelCase	)

	return _Datasets(train=__UpperCAmelCase			,   validation=__UpperCAmelCase			,   test=__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


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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







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

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

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

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

						snake_case:    int      							=False
						for attribute in attributes:
									if attribute in mapping:
												snake_case:    Dict      							=mapping[attribute]
												if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					) and len(__UpperCAmelCase					) > 0:
															snake_case:    Optional[int]      							=attribute
									elif hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
												snake_case:    Optional[Any]      							=attribute

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

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

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

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

						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(__UpperCAmelCase					)


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




