





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			List[str]	):
   __lowerCAmelCase								=	AutoConfig.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase_	)
   __lowerCAmelCase								=	checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

   __lowerCAmelCase								=	'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

   if config.model_type == "t5":
      __lowerCAmelCase								=	'SelfAttention'
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      __lowerCAmelCase								=	'LocalSelfAttention'
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      __lowerCAmelCase								=	'TransientGlobalSelfAttention'
   else:
      raise ValueError(
          'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
          ' attribute with a value from [\'local\', \'transient-global].'	)

   # Encoder
   for layer_index in range(config.num_layers	):
      __lowerCAmelCase								=	F"""layers_{str(lowerCAmelCase_	)}"""

      # Self-Attention
      __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
      __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
      __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
      __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

      # Layer Normalization
      __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

      if split_mlp_wi:
         __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
         __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
      else:
         __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

      __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

      # Layer Normalization
      __lowerCAmelCase								=	tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

      # Assigning
      __lowerCAmelCase								=	flax_model.params['encoder']['block'][str(lowerCAmelCase_	)]['layer']
      __lowerCAmelCase								=	tax_attention_key
      __lowerCAmelCase								=	tax_attention_out
      __lowerCAmelCase								=	tax_attention_query
      __lowerCAmelCase								=	tax_attention_value

      __lowerCAmelCase								=	tax_attention_layer_norm

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         __lowerCAmelCase								=	tax_global_layer_norm

      if split_mlp_wi:
         __lowerCAmelCase								=	tax_mlp_wi_a
         __lowerCAmelCase								=	tax_mlp_wi_a
      else:
         __lowerCAmelCase								=	tax_mlp_wi

      __lowerCAmelCase								=	tax_mlp_wo
      __lowerCAmelCase								=	tax_mlp_layer_norm

      __lowerCAmelCase								=	flax_model_encoder_layer_block

   # Only for layer 0:
   __lowerCAmelCase								=	tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
   __lowerCAmelCase								=	tax_encoder_rel_embedding

   # Side/global relative position_bias + layer norm
   if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      __lowerCAmelCase								=	tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
      __lowerCAmelCase								=	tax_encoder_global_rel_embedding

   # Assigning
   __lowerCAmelCase								=	tax_model['target']['encoder']['encoder_norm']['scale']
   __lowerCAmelCase								=	tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers	):
      __lowerCAmelCase								=	F"""layers_{str(lowerCAmelCase_	)}"""

      # Self-Attention
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

      # Layer Normalization
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
          'scale'
      ]

      # Encoder-Decoder-Attention
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
      __lowerCAmelCase								=	tax_enc_dec_attention_module['key']['kernel']
      __lowerCAmelCase								=	tax_enc_dec_attention_module['out']['kernel']
      __lowerCAmelCase								=	tax_enc_dec_attention_module['query']['kernel']
      __lowerCAmelCase								=	tax_enc_dec_attention_module['value']['kernel']

      # Layer Normalization
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

      # MLP
      if split_mlp_wi:
         __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
         __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
      else:
         __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

      # Layer Normalization
      __lowerCAmelCase								=	tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

      # Assigning
      __lowerCAmelCase								=	flax_model.params['decoder']['block'][str(lowerCAmelCase_	)]['layer']
      __lowerCAmelCase								=	tax_attention_key
      __lowerCAmelCase								=	tax_attention_out
      __lowerCAmelCase								=	tax_attention_query
      __lowerCAmelCase								=	tax_attention_value

      __lowerCAmelCase								=	tax_pre_attention_layer_norm

      __lowerCAmelCase								=	tax_enc_dec_attention_key
      __lowerCAmelCase								=	tax_enc_dec_attention_out
      __lowerCAmelCase								=	tax_enc_dec_attention_query
      __lowerCAmelCase								=	tax_enc_dec_attention_value

      __lowerCAmelCase								=	tax_cross_layer_norm

      if split_mlp_wi:
         __lowerCAmelCase								=	tax_mlp_wi_a
         __lowerCAmelCase								=	tax_mlp_wi_a
      else:
         __lowerCAmelCase								=	tax_mlp_wi

      __lowerCAmelCase								=	tax_mlp_wo

      __lowerCAmelCase								=	txa_mlp_layer_norm

      __lowerCAmelCase								=	flax_model_decoder_layer_block

   # Decoder Normalization
   __lowerCAmelCase								=	tax_model['target']['decoder']['decoder_norm']['scale']
   __lowerCAmelCase								=	txa_decoder_norm

   # Only for layer 0:
   __lowerCAmelCase								=	tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
   __lowerCAmelCase								=	tax_decoder_rel_embedding

   # Token Embeddings
   __lowerCAmelCase								=	tax_model['target']['token_embedder']['embedding']
   __lowerCAmelCase								=	txa_token_embeddings

   # LM Head (only in v1.1 and LongT5 checkpoints)
   if "logits_dense" in tax_model["target"]["decoder"]:
      __lowerCAmelCase								=	tax_model['target']['decoder']['logits_dense']['kernel']

   flax_model.save_pretrained(lowerCAmelCase_	)
   print('T5X Model was sucessfully converted!'	)


if __name__ == "__main__":
     _snake_case  :    Any						    =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
     )
     parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
     parser.add_argument(
         '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
     )
     _snake_case  :    Tuple						    =  parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








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


_snake_case  :    Dict						    =  pytest.mark.integration



@require_faiss
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		List[Any]     )							->						Optional[Any]:
     __lowerCAmelCase								=	Dataset.from_dict({'filename': ['my_name-train' + '_' + str(lowerCAmelCase_     ) for x in np.arange(3_0     ).tolist()]}     )
     return dset






  def 				lowercase		(				self :		List[str]     )							->						Tuple:
     import faiss

     __lowerCAmelCase								=	self._create_dummy_dataset()
     __lowerCAmelCase								=	dset.map(
         lambda lowerCAmelCase_					,  lowerCAmelCase_     : {"vecs": i * np.ones(5					,  dtype=np.floataa     )}					,  with_indices=lowerCAmelCase_					,  keep_in_memory=lowerCAmelCase_     )
     __lowerCAmelCase								=	dset.add_faiss_index('vecs'					,  batch_size=1_0_0					,  metric_type=faiss.METRIC_INNER_PRODUCT     )
     __lowerCAmelCase	,	__lowerCAmelCase								=	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 				lowercase		(				self :		Optional[Any]     )							->						str:
     import faiss

     __lowerCAmelCase								=	self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).reshape(-1					,  1     )					,  index_name='vecs'					,  batch_size=1_0_0					,  metric_type=faiss.METRIC_INNER_PRODUCT					,  )
     __lowerCAmelCase	,	__lowerCAmelCase								=	dset.get_nearest_examples('vecs'					,  np.ones(5					,  dtype=np.floataa     )     )
     self.assertEqual(examples['filename'][0]					,  'my_name-train_29'     )






  def 				lowercase		(				self :		int     )							->						Optional[Any]:
     import faiss

     __lowerCAmelCase								=	self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).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=lowerCAmelCase_     ) 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								=	dset.get_nearest_examples('vecs2'					,  np.ones(5					,  dtype=np.floataa     )     )
     self.assertEqual(examples['filename'][0]					,  'my_name-train_29'     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						List[Any]:
     __lowerCAmelCase								=	self._create_dummy_dataset()
     dset.add_faiss_index_from_external_arrays(
         external_arrays=np.ones((3_0, 5)     ) * np.arange(3_0     ).reshape(-1					,  1     )					,  index_name='vecs'     )
     dset.drop_index('vecs'     )
     self.assertRaises(lowerCAmelCase_					,  partial(dset.get_nearest_examples					,  'vecs2'					,  np.ones(5					,  dtype=np.floataa     )     )     )






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

     __lowerCAmelCase								=	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								=	{'acknowledged': True}
        mocked_bulk.return_value([(True, None)] * 3_0     )
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 2_9}]}}
        __lowerCAmelCase								=	Elasticsearch()

        dset.add_elasticsearch_index('filename'					,  es_client=lowerCAmelCase_     )
        __lowerCAmelCase	,	__lowerCAmelCase								=	dset.get_nearest_examples('filename'					,  'my_name-train_29'     )
        self.assertEqual(examples['filename'][0]					,  'my_name-train_29'     )





@require_faiss
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		str     )							->						int:
     import faiss

     __lowerCAmelCase								=	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					,  1_0     )

     # single query
     __lowerCAmelCase								=	np.zeros(5					,  dtype=np.floataa     )
     __lowerCAmelCase								=	1
     __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_     )
     self.assertRaises(lowerCAmelCase_					,  index.search					,  query.reshape(-1					,  1     )     )
     self.assertGreater(scores[0]					,  0     )
     self.assertEqual(indices[0]					,  1     )

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






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

     __lowerCAmelCase								=	FaissIndex(string_factory='Flat'     )
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index					,  faiss.IndexFlat     )
     __lowerCAmelCase								=	FaissIndex(string_factory='LSH'     )
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index					,  faiss.IndexLSH     )
     with self.assertRaises(lowerCAmelCase_     ):
        __lowerCAmelCase								=	FaissIndex(string_factory='Flat'					,  custom_index=faiss.IndexFlat(5     )     )






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

     __lowerCAmelCase								=	faiss.IndexFlat(5     )
     __lowerCAmelCase								=	FaissIndex(custom_index=lowerCAmelCase_     )
     index.add_vectors(np.eye(5					,  dtype=np.floataa     )     )
     self.assertIsInstance(index.faiss_index					,  faiss.IndexFlat     )






  def 				lowercase		(				self :		str     )							->						Any:
     import faiss

     __lowerCAmelCase								=	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=lowerCAmelCase_     ) as tmp_file:
        index.save(tmp_file.name     )
        __lowerCAmelCase								=	FaissIndex.load(tmp_file.name     )
     os.unlink(tmp_file.name     )

     __lowerCAmelCase								=	np.zeros(5					,  dtype=np.floataa     )
     __lowerCAmelCase								=	1
     __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_     )
     self.assertGreater(scores[0]					,  0     )
     self.assertEqual(indices[0]					,  1     )





@require_faiss
def   a_    ( lowerCAmelCase_      :			Union[str, Any]	):
   import faiss

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

   __lowerCAmelCase								=	'index.faiss'
   __lowerCAmelCase								=	F"""mock://{index_name}"""
   index.save(lowerCAmelCase_,	storage_options=mockfs.storage_options	)
   __lowerCAmelCase								=	FaissIndex.load(lowerCAmelCase_,	storage_options=mockfs.storage_options	)

   __lowerCAmelCase								=	np.zeros(5,	dtype=np.floataa	)
   __lowerCAmelCase								=	1
   __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_	)
   assert scores[0] > 0
   assert indices[0] == 1




@require_elasticsearch
class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		Any     )							->						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								=	Elasticsearch()
        __lowerCAmelCase								=	{'acknowledged': True}
        __lowerCAmelCase								=	ElasticSearchIndex(es_client=lowerCAmelCase_     )
        mocked_bulk.return_value([(True, None)] * 3     )
        index.add_documents(['foo', 'bar', 'foobar']     )

        # single query
        __lowerCAmelCase								=	'foo'
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_     )
        self.assertEqual(scores[0]					,  1     )
        self.assertEqual(indices[0]					,  0     )

        # single query with timeout
        __lowerCAmelCase								=	'foo'
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search(lowerCAmelCase_					,  request_timeout=3_0     )
        self.assertEqual(scores[0]					,  1     )
        self.assertEqual(indices[0]					,  0     )

        # batched queries
        __lowerCAmelCase								=	['foo', 'bar', 'foobar']
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search_batch(lowerCAmelCase_     )
        __lowerCAmelCase								=	[scores[0] for scores in total_scores]
        __lowerCAmelCase								=	[indices[0] for indices in total_indices]
        self.assertGreater(np.min(lowerCAmelCase_     )					,  0     )
        self.assertListEqual([1, 1, 1]					,  lowerCAmelCase_     )

        # batched queries with timeout
        __lowerCAmelCase								=	['foo', 'bar', 'foobar']
        __lowerCAmelCase								=	{'hits': {'hits': [{'_score': 1, '_id': 1}]}}
        __lowerCAmelCase	,	__lowerCAmelCase								=	index.search_batch(lowerCAmelCase_					,  request_timeout=3_0     )
        __lowerCAmelCase								=	[scores[0] for scores in total_scores]
        __lowerCAmelCase								=	[indices[0] for indices in total_indices]
        self.assertGreater(np.min(lowerCAmelCase_     )					,  0     )
        self.assertListEqual([1, 1, 1]					,  lowerCAmelCase_     )

