
"""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 		UpperCAmelCase (		a_,						a_      ):





  '''simple docstring'''


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

  lowerCamelCase					:      Union[str, Any]          =   ['key_proj', 'value_proj', 'query_proj']

  lowerCamelCase					:      Optional[Any]          =   {
      '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					:      List[str]          =   key.split('.'      )

    if attributes[0] == "lm_head":
      lowerCamelCase					:      str          =   prophet
      lowerCamelCase					:      Union[str, Any]          =   prophet_old
    else:
      lowerCamelCase					:      List[Any]          =   prophet.prophetnet
      lowerCamelCase					:      List[str]          =   prophet_old.model

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

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

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

      if attribute.isdigit():
        lowerCamelCase					:      Dict          =   model[int(a_      )]
        lowerCamelCase					:      List[Any]          =   old_model[int(a_      )]
      else:
        lowerCamelCase					:      int          =   getattr(a_,						a_      )

        if old_attribute == "":
          lowerCamelCase					:      List[Any]          =   old_model
        else:
          if not hasattr(a_,						a_      ):
            raise ValueError(F"""{old_model} does not have {old_attribute}"""      )
          lowerCamelCase					:      List[str]          =   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__":
     _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)








"""simple docstring"""

import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_A						=			pytest.mark.integration






@require_faiss
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Any          =   Dataset.from_dict({'filename': ['my_name-train' + '_' + str(UpperCAmelCase_ ) for x in np.arange(30 ).tolist()]} )
			return dset




	def       _UpperCamelCase   (			self )		->							List[Any]:
			import faiss

			lowerCamelCase					:      Dataset          =   self._create_dummy_dataset()
			lowerCamelCase					:      Optional[int]          =   dset.map(
			    lambda UpperCAmelCase_		,			UpperCAmelCase_ : {"vecs": i * np.ones(5		,			dtype=np.floataa )}		,			with_indices=UpperCAmelCase_		,			keep_in_memory=UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   dset.add_faiss_index('vecs'		,			batch_size=100		,			metric_type=faiss.METRIC_INNER_PRODUCT )
			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   dset.get_nearest_examples('vecs'		,			np.ones(5		,			dtype=np.floataa ) )
			self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )
			dset.drop_index('vecs' )




	def       _UpperCamelCase   (			self )		->							Tuple:
			import faiss

			lowerCamelCase					:      Dataset          =   self._create_dummy_dataset()
			dset.add_faiss_index_from_external_arrays(
			    external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1		,			1 )		,			index_name='vecs'		,			batch_size=100		,			metric_type=faiss.METRIC_INNER_PRODUCT		,			)
			lowerCamelCase     ,   lowerCamelCase					:      str          =   dset.get_nearest_examples('vecs'		,			np.ones(5		,			dtype=np.floataa ) )
			self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )




	def       _UpperCamelCase   (			self )		->							int:
			import faiss

			lowerCamelCase					:      Dataset          =   self._create_dummy_dataset()
			dset.add_faiss_index_from_external_arrays(
			    external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1		,			1 )		,			index_name='vecs'		,			metric_type=faiss.METRIC_INNER_PRODUCT		,			)

			# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
			# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
			# see https://bugs.python.org/issue14243 and
			# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
			with tempfile.NamedTemporaryFile(delete=UpperCAmelCase_ ) as tmp_file:
					dset.save_faiss_index('vecs'		,			tmp_file.name )
					dset.load_faiss_index('vecs2'		,			tmp_file.name )
			os.unlink(tmp_file.name )

			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   dset.get_nearest_examples('vecs2'		,			np.ones(5		,			dtype=np.floataa ) )
			self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Dataset          =   self._create_dummy_dataset()
			dset.add_faiss_index_from_external_arrays(
			    external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1		,			1 )		,			index_name='vecs' )
			dset.drop_index('vecs' )
			self.assertRaises(UpperCAmelCase_		,			partial(dset.get_nearest_examples		,			'vecs2'		,			np.ones(5		,			dtype=np.floataa ) ) )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			from elasticsearch import Elasticsearch

			lowerCamelCase					:      Dataset          =   self._create_dummy_dataset()
			with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
			    'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
					lowerCamelCase					:      Tuple          =   {'acknowledged': True}
					mocked_bulk.return_value([(True, None)] * 30 )
					lowerCamelCase					:      int          =   {'hits': {'hits': [{'_score': 1, '_id': 29}]}}
					lowerCamelCase					:      Optional[Any]          =   Elasticsearch()

					dset.add_elasticsearch_index('filename'		,			es_client=UpperCAmelCase_ )
					lowerCamelCase     ,   lowerCamelCase					:      str          =   dset.get_nearest_examples('filename'		,			'my_name-train_29' )
					self.assertEqual(examples['filename'][0]		,			'my_name-train_29' )






@require_faiss
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			import faiss

			lowerCamelCase					:      str          =   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )

			# add vectors
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )
			self.assertIsNotNone(index.faiss_index )
			self.assertEqual(index.faiss_index.ntotal		,			5 )
			index.add_vectors(np.zeros((5, 5)		,			dtype=np.floataa ) )
			self.assertEqual(index.faiss_index.ntotal		,			10 )

			# single query
			lowerCamelCase					:      Optional[int]          =   np.zeros(5		,			dtype=np.floataa )
			lowerCamelCase					:      List[str]          =   1
			lowerCamelCase     ,   lowerCamelCase					:      int          =   index.search(UpperCAmelCase_ )
			self.assertRaises(UpperCAmelCase_		,			index.search		,			query.reshape(-1		,			1 ) )
			self.assertGreater(scores[0]		,			0 )
			self.assertEqual(indices[0]		,			1 )

			# batched queries
			lowerCamelCase					:      Tuple          =   np.eye(5		,			dtype=np.floataa )[::-1]
			lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   index.search_batch(UpperCAmelCase_ )
			self.assertRaises(UpperCAmelCase_		,			index.search_batch		,			queries[0] )
			lowerCamelCase					:      List[str]          =   [scores[0] for scores in total_scores]
			lowerCamelCase					:      List[str]          =   [indices[0] for indices in total_indices]
			self.assertGreater(np.min(UpperCAmelCase_ )		,			0 )
			self.assertListEqual([4, 3, 2, 1, 0]		,			UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Dict:
			import faiss

			lowerCamelCase					:      List[Any]          =   FaissIndex(string_factory='Flat' )
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )
			self.assertIsInstance(index.faiss_index		,			faiss.IndexFlat )
			lowerCamelCase					:      int          =   FaissIndex(string_factory='LSH' )
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )
			self.assertIsInstance(index.faiss_index		,			faiss.IndexLSH )
			with self.assertRaises(UpperCAmelCase_ ):
					lowerCamelCase					:      str          =   FaissIndex(string_factory='Flat'		,			custom_index=faiss.IndexFlat(5 ) )




	def       _UpperCamelCase   (			self )		->							Any:
			import faiss

			lowerCamelCase					:      Any          =   faiss.IndexFlat(5 )
			lowerCamelCase					:      Any          =   FaissIndex(custom_index=UpperCAmelCase_ )
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )
			self.assertIsInstance(index.faiss_index		,			faiss.IndexFlat )




	def       _UpperCamelCase   (			self )		->							Any:
			import faiss

			lowerCamelCase					:      Any          =   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
			index.add_vectors(np.eye(5		,			dtype=np.floataa ) )

			# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
			# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
			# see https://bugs.python.org/issue14243 and
			# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
			with tempfile.NamedTemporaryFile(delete=UpperCAmelCase_ ) as tmp_file:
					index.save(tmp_file.name )
					lowerCamelCase					:      List[str]          =   FaissIndex.load(tmp_file.name )
			os.unlink(tmp_file.name )

			lowerCamelCase					:      Dict          =   np.zeros(5		,			dtype=np.floataa )
			lowerCamelCase					:      Optional[Any]          =   1
			lowerCamelCase     ,   lowerCamelCase					:      str          =   index.search(UpperCAmelCase_ )
			self.assertGreater(scores[0]		,			0 )
			self.assertEqual(indices[0]		,			1 )


@require_faiss
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		import faiss

		lowerCamelCase					:      int          =   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT      )
		index.add_vectors(np.eye(5,						dtype=np.floataa      )      )

		lowerCamelCase					:      Union[str, Any]          =   'index.faiss'
		lowerCamelCase					:      List[Any]          =   F"""mock://{index_name}"""
		index.save(a_,						storage_options=mockfs.storage_options      )
		lowerCamelCase					:      Optional[int]          =   FaissIndex.load(a_,						storage_options=mockfs.storage_options      )

		lowerCamelCase					:      str          =   np.zeros(5,						dtype=np.floataa      )
		lowerCamelCase					:      str          =   1
		lowerCamelCase     ,   lowerCamelCase					:      int          =   index.search(a_      )
		assert scores[0] > 0
		assert indices[0] == 1






@require_elasticsearch
class 	_lowercase       (    __UpperCAmelCase		):




	def       _UpperCamelCase   (			self )		->							int:
			from elasticsearch import Elasticsearch

			with patch('elasticsearch.Elasticsearch.search' ) as mocked_search, patch(
			    'elasticsearch.client.IndicesClient.create' ) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk' ) as mocked_bulk:
					lowerCamelCase					:      Union[str, Any]          =   Elasticsearch()
					lowerCamelCase					:      Optional[Any]          =   {'acknowledged': True}
					lowerCamelCase					:      str          =   ElasticSearchIndex(es_client=UpperCAmelCase_ )
					mocked_bulk.return_value([(True, None)] * 3 )
					index.add_documents(['foo', 'bar', 'foobar'] )

					# single query
					lowerCamelCase					:      Tuple          =   'foo'
					lowerCamelCase					:      List[str]          =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
					lowerCamelCase     ,   lowerCamelCase					:      Any          =   index.search(UpperCAmelCase_ )
					self.assertEqual(scores[0]		,			1 )
					self.assertEqual(indices[0]		,			0 )

					# single query with timeout
					lowerCamelCase					:      Dict          =   'foo'
					lowerCamelCase					:      Optional[Any]          =   {'hits': {'hits': [{'_score': 1, '_id': 0}]}}
					lowerCamelCase     ,   lowerCamelCase					:      Optional[Any]          =   index.search(UpperCAmelCase_		,			request_timeout=30 )
					self.assertEqual(scores[0]		,			1 )
					self.assertEqual(indices[0]		,			0 )

					# batched queries
					lowerCamelCase					:      str          =   ['foo', 'bar', 'foobar']
					lowerCamelCase					:      Union[str, Any]          =   {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
					lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   index.search_batch(UpperCAmelCase_ )
					lowerCamelCase					:      Dict          =   [scores[0] for scores in total_scores]
					lowerCamelCase					:      Optional[int]          =   [indices[0] for indices in total_indices]
					self.assertGreater(np.min(UpperCAmelCase_ )		,			0 )
					self.assertListEqual([1, 1, 1]		,			UpperCAmelCase_ )

					# batched queries with timeout
					lowerCamelCase					:      List[str]          =   ['foo', 'bar', 'foobar']
					lowerCamelCase					:      str          =   {'hits': {'hits': [{'_score': 1, '_id': 1}]}}
					lowerCamelCase     ,   lowerCamelCase					:      Dict          =   index.search_batch(UpperCAmelCase_		,			request_timeout=30 )
					lowerCamelCase					:      Dict          =   [scores[0] for scores in total_scores]
					lowerCamelCase					:      int          =   [indices[0] for indices in total_indices]
					self.assertGreater(np.min(UpperCAmelCase_ )		,			0 )
					self.assertListEqual([1, 1, 1]		,			UpperCAmelCase_ )






