"""simple docstring"""


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    _UpperCAmelCase         =					flax_key_tuple[:-1] + ("weight",)
    _UpperCAmelCase         =					torch.permute(_lowerCAmelCase      ,(0, 2, 1)						)
  elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase						):
    # linear layer
    _UpperCAmelCase         =					flax_key_tuple[:-1] + ("weight",)
    _UpperCAmelCase         =					flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    _UpperCAmelCase         =					flax_key_tuple[:-1] + ("weight",)

  return flax_key_tuple, flax_tensor






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  if "metadata" in layer:
    _UpperCAmelCase         =					layer.split("""metadata"""						)
    _UpperCAmelCase         =					"".join(split_layer[0]						)[:-1]
    _UpperCAmelCase         =					[tuple(("""metadata""" + split_layer[1]).split("""/"""						)						)]
  elif "kvstore" in layer:
    _UpperCAmelCase         =					layer.split("""kvstore"""						)
    _UpperCAmelCase         =					"".join(split_layer[0]						)[:-1]
    _UpperCAmelCase         =					[tuple(("""kvstore""" + split_layer[1]).split("""/"""						)						)]

  else:
    _UpperCAmelCase         =					layer.split("""/"""						)
    _UpperCAmelCase         =					"/".join(split_layer[:-1]						)
    _UpperCAmelCase         =					(split_layer[-1],)

  if "kvstore/path" in layer:
    _UpperCAmelCase         =					f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
  elif "kvstore/driver" in layer:
    _UpperCAmelCase         =					"file"
  else:
    _UpperCAmelCase         =					checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					rename_keys(_lowerCAmelCase						)
  _UpperCAmelCase         =					{}
  for k, v in current_block.items():
    _UpperCAmelCase         =					v
  _UpperCAmelCase         =					new_current_block
  torch.save(_lowerCAmelCase      ,_lowerCAmelCase						)






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase = WEIGHTS_NAME						):







  """simple docstring"""
  _UpperCAmelCase         =					convert_file_size_to_int(_lowerCAmelCase						)
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					{}
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					0

  os.makedirs(_lowerCAmelCase      ,exist_ok=_lowerCAmelCase						)
  with gfile.GFile(switch_checkpoint_path + """/checkpoint"""      ,"""rb"""						) as fp:
    _UpperCAmelCase         =					serialization.msgpack_restore(fp.read()						)["optimizer"]["target"]
    _UpperCAmelCase         =					flatten_dict(_lowerCAmelCase      ,sep="""/"""						)

  _UpperCAmelCase         =					{}
  for layer in checkpoint_info.keys():
    _UpperCAmelCase         =					get_key_and_tensorstore_dict(
        _lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
    if curr_real_layer_name in all_layers:
      _UpperCAmelCase         =					content
    else:
      _UpperCAmelCase         =					{split_layer[-1]: content}

  for key in all_layers.keys():
    # open tensorstore file
    _UpperCAmelCase         =					ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
    _UpperCAmelCase         =					torch.tensor(_lowerCAmelCase						)
    _UpperCAmelCase         =					raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

    # use the renaming pattern from the small conversion scripts
    _UpperCAmelCase         =					rename_base_flax_keys(tuple(key.split("""/"""						)						)      ,_lowerCAmelCase						)
    _UpperCAmelCase         =					"/".join(_lowerCAmelCase						)

    # If this weight is going to tip up over the maximal size, we split.
    if current_block_size + weight_size > max_shard_size:
      _UpperCAmelCase         =					os.path.join(
          _lowerCAmelCase      ,weights_name.replace(""".bin"""      ,f'''-{len(_lowerCAmelCase						)+1:05d}-of-???.bin'''						)						)
      rename_and_save_block(_lowerCAmelCase      ,_lowerCAmelCase						)
      sharded_state_dicts.append(current_block.keys()						)
      del current_block
      _UpperCAmelCase         =					{}
      _UpperCAmelCase         =					0

    _UpperCAmelCase         =					raw_weights.to(getattr(_lowerCAmelCase      ,_lowerCAmelCase						)						)
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  _UpperCAmelCase         =					os.path.join(_lowerCAmelCase      ,weights_name.replace(""".bin"""      ,f'''-{len(_lowerCAmelCase						)+1:05d}-of-???.bin'''						)						)
  rename_and_save_block(_lowerCAmelCase      ,_lowerCAmelCase						)
  sharded_state_dicts.append(current_block.keys()						)

  # If we only have one shard, we return it
  if len(_lowerCAmelCase						) == 1:
    return {weights_name: sharded_state_dicts[0]}, None

  # Otherwise, let's build the index
  _UpperCAmelCase         =					{}
  _UpperCAmelCase         =					{}
  for idx, shard in enumerate(_lowerCAmelCase						):
    _UpperCAmelCase         =					weights_name.replace(
        """.bin"""      ,f'''-{idx+1:05d}-of-{len(_lowerCAmelCase						):05d}.bin'''						)  # len(sharded_state_dicts):05d}
    _UpperCAmelCase         =					os.path.join(_lowerCAmelCase      ,weights_name.replace(""".bin"""      ,f'''-{idx+1:05d}-of-???.bin'''						)						)
    os.rename(_lowerCAmelCase      ,os.path.join(_lowerCAmelCase      ,_lowerCAmelCase						)						)
    _UpperCAmelCase         =					shard
    for key in shard:
      _UpperCAmelCase         =					shard_file

       # Add the metadata
  _UpperCAmelCase         =					{"total_size": total_size}
  _UpperCAmelCase         =					{"metadata": metadata, "weight_map": weight_map}

  with open(os.path.join(_lowerCAmelCase      ,_lowerCAmelCase						)      ,"""w"""      ,encoding="""utf-8"""						) as f:
    _UpperCAmelCase         =					json.dumps(_lowerCAmelCase      ,indent=2      ,sort_keys=_lowerCAmelCase						) + "\n"
    f.write(_lowerCAmelCase						)

  return metadata, index


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--switch_t5x_checkpoint_path""",
        default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
        type=str,
        required=False,
        help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
    )
    parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
    parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
        type=str,
        required=False,
        help="""Path to the output pytorch model.""",
    )
    UpperCAmelCase__     						=  parser.parse_args()
    shard_on_the_fly(
        args.switch_tax_checkpoint_path,
        args.pytorch_dump_folder_path,
        args.max_shard_size,
        args.dtype,
    )






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  _UpperCAmelCase         =					SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""						)
  config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""						)
  _UpperCAmelCase         =					SwitchTransformersForConditionalGeneration.from_pretrained(
      """/home/arthur_huggingface_co/transformers/switch_converted"""      ,device_map="""auto"""						)

  _UpperCAmelCase         =					TaTokenizer.from_pretrained("""t5-small"""						)
  _UpperCAmelCase         =					"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

  _UpperCAmelCase         =					tokenizer(_lowerCAmelCase      ,return_tensors="""pt"""						).input_ids
  _UpperCAmelCase         =					model.generate(_lowerCAmelCase      ,decoder_start_token_id=0						)
  print(tokenizer.decode(out[0]						)						)







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


__lowerCamelCase       :     Union[str, Any]   =     pytest.mark.integration



@require_faiss
class 					A__   (       __snake_case			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   Dataset.from_dict({"filename": ["my_name-train" + "_" + str(A_   ) for x in np.arange(30   ).tolist()]}   )
       return dset





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Dataset       	=   self._create_dummy_dataset()
       UpperCamelCase	:					List[Any]       	=   dset.map(
           lambda A_       ,							A_   : {"vecs": i * np.ones(5       ,							dtype=np.floataa   )}       ,							with_indices=A_       ,							keep_in_memory=A_   )
       UpperCamelCase	:					List[str]       	=   dset.add_faiss_index("vecs"       ,							batch_size=100       ,							metric_type=faiss.METRIC_INNER_PRODUCT   )
       UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   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   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					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       ,							)
       UpperCamelCase	,    UpperCamelCase	:					int       	=   dset.get_nearest_examples("vecs"       ,							np.ones(5       ,							dtype=np.floataa   )   )
       self.assertEqual(examples["filename"][0]       ,							"my_name-train_29"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					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=A_   ) as tmp_file:
        dset.save_faiss_index("vecs"       ,							tmp_file.name   )
        dset.load_faiss_index("vecs2"       ,							tmp_file.name   )
       os.unlink(tmp_file.name   )

       UpperCamelCase	,    UpperCamelCase	:					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   ):
       '''simple docstring'''




       UpperCamelCase	:					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(A_       ,							partial(dset.get_nearest_examples       ,							"vecs2"       ,							np.ones(5       ,							dtype=np.floataa   )   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       from elasticsearch import Elasticsearch

       UpperCamelCase	:					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:
        UpperCamelCase	:					List[str]       	=   {"acknowledged": True}
        mocked_bulk.return_value([(True, None)] * 30   )
        UpperCamelCase	:					List[Any]       	=   {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
        UpperCamelCase	:					Optional[Any]       	=   Elasticsearch()

        dset.add_elasticsearch_index("filename"       ,							es_client=A_   )
        UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   dset.get_nearest_examples("filename"       ,							"my_name-train_29"   )
        self.assertEqual(examples["filename"][0]       ,							"my_name-train_29"   )



@require_faiss
class 					A__   (       __snake_case			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Optional[int]       	=   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
       UpperCamelCase	:					Any       	=   np.zeros(5       ,							dtype=np.floataa   )
       UpperCamelCase	:					Optional[Any]       	=   1
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   index.search(A_   )
       self.assertRaises(A_       ,							index.search       ,							query.reshape(-1       ,							1   )   )
       self.assertGreater(scores[0]       ,							0   )
       self.assertEqual(indices[0]       ,							1   )

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					List[str]       	=   FaissIndex(string_factory="Flat"   )
       index.add_vectors(np.eye(5       ,							dtype=np.floataa   )   )
       self.assertIsInstance(index.faiss_index       ,							faiss.IndexFlat   )
       UpperCamelCase	:					List[str]       	=   FaissIndex(string_factory="LSH"   )
       index.add_vectors(np.eye(5       ,							dtype=np.floataa   )   )
       self.assertIsInstance(index.faiss_index       ,							faiss.IndexLSH   )
       with self.assertRaises(A_   ):
        UpperCamelCase	:					List[str]       	=   FaissIndex(string_factory="Flat"       ,							custom_index=faiss.IndexFlat(5   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					Dict       	=   faiss.IndexFlat(5   )
       UpperCamelCase	:					Union[str, Any]       	=   FaissIndex(custom_index=A_   )
       index.add_vectors(np.eye(5       ,							dtype=np.floataa   )   )
       self.assertIsInstance(index.faiss_index       ,							faiss.IndexFlat   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       import faiss

       UpperCamelCase	:					str       	=   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=A_   ) as tmp_file:
        index.save(tmp_file.name   )
        UpperCamelCase	:					int       	=   FaissIndex.load(tmp_file.name   )
       os.unlink(tmp_file.name   )

       UpperCamelCase	:					str       	=   np.zeros(5       ,							dtype=np.floataa   )
       UpperCamelCase	:					int       	=   1
       UpperCamelCase	,    UpperCamelCase	:					Dict       	=   index.search(A_   )
       self.assertGreater(scores[0]       ,							0   )
       self.assertEqual(indices[0]       ,							1   )



@require_faiss
def 		A_				(     _lowerCAmelCase     )						-> Optional[int]:
 import faiss

 UpperCamelCase	:					Union[str, Any]       	=   FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
 index.add_vectors(np.eye(5							,   dtype=np.floataa     )     )

 UpperCamelCase	:					List[Any]       	=   "index.faiss"
 UpperCamelCase	:					List[str]       	=   F"""mock://{index_name}"""
 index.save(_lowerCAmelCase							,   storage_options=mockfs.storage_options     )
 UpperCamelCase	:					List[str]       	=   FaissIndex.load(_lowerCAmelCase							,   storage_options=mockfs.storage_options     )

 UpperCamelCase	:					List[str]       	=   np.zeros(5							,   dtype=np.floataa     )
 UpperCamelCase	:					Optional[int]       	=   1
 UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   index.search(_lowerCAmelCase     )
 assert scores[0] > 0
 assert indices[0] == 1



@require_elasticsearch
class 					A__   (       __snake_case			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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:
        UpperCamelCase	:					List[str]       	=   Elasticsearch()
        UpperCamelCase	:					Union[str, Any]       	=   {"acknowledged": True}
        UpperCamelCase	:					Union[str, Any]       	=   ElasticSearchIndex(es_client=A_   )
        mocked_bulk.return_value([(True, None)] * 3   )
        index.add_documents(["foo", "bar", "foobar"]   )

        # single query
        UpperCamelCase	:					str       	=   "foo"
        UpperCamelCase	:					Dict       	=   {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
        UpperCamelCase	,    UpperCamelCase	:					Tuple       	=   index.search(A_   )
        self.assertEqual(scores[0]       ,							1   )
        self.assertEqual(indices[0]       ,							0   )

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

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

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