

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


UpperCamelCase__		 =     pytest.mark.integration

@require_faiss
class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


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


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              import faiss

              __snake_case       	=							self._create_dummy_dataset()
              __snake_case       	=							dset.map(
                  lambda _UpperCamelCase      , _UpperCamelCase   : {"vecs": i * np.ones(5      , dtype=np.floataa   )}      , with_indices=_UpperCamelCase      , keep_in_memory=_UpperCamelCase   )
              __snake_case       	=							dset.add_faiss_index("""vecs"""      , batch_size=1_00      , metric_type=faiss.METRIC_INNER_PRODUCT   )
              __snake_case	,    __snake_case       	=							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 			a		(		self   )      ->							Dict:






              """simple docstring"""


              import faiss

              __snake_case       	=							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=1_00      , metric_type=faiss.METRIC_INNER_PRODUCT      , )
              __snake_case	,    __snake_case       	=							dset.get_nearest_examples("""vecs"""      , np.ones(5      , dtype=np.floataa   )   )
              self.assertEqual(examples["""filename"""][0]      , """my_name-train_29"""   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              import faiss

              __snake_case       	=							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   )

              __snake_case	,    __snake_case       	=							dset.get_nearest_examples("""vecs2"""      , np.ones(5      , dtype=np.floataa   )   )
              self.assertEqual(examples["""filename"""][0]      , """my_name-train_29"""   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							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 			a		(		self   )      ->							str:






              """simple docstring"""


              from elasticsearch import Elasticsearch

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

                     dset.add_elasticsearch_index("""filename"""      , es_client=_UpperCamelCase   )
                     __snake_case	,    __snake_case       	=							dset.get_nearest_examples("""filename"""      , """my_name-train_29"""   )
                     self.assertEqual(examples["""filename"""][0]      , """my_name-train_29"""   )






@require_faiss
class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              import faiss

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

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


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              import faiss

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


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              import faiss

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






       def 			a		(		self   )      ->							str:






              """simple docstring"""


              import faiss

              __snake_case       	=							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   )
                     __snake_case       	=							FaissIndex.load(tmp_file.name   )
              os.unlink(tmp_file.name   )

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







@require_faiss
def 						lowerCamelCase__      ( __A    :List[Any]		):


       """simple docstring"""




       import faiss

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

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

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




@require_elasticsearch
class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def 			a		(		self   )      ->							int:






              """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:
                     __snake_case       	=							Elasticsearch()
                     __snake_case       	=							{"""acknowledged""": True}
                     __snake_case       	=							ElasticSearchIndex(es_client=_UpperCamelCase   )
                     mocked_bulk.return_value([(True, None)] * 3   )
                     index.add_documents(["""foo""", """bar""", """foobar"""]   )

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

                     # single query with timeout
                     __snake_case       	=							"""foo"""
                     __snake_case       	=							{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
                     __snake_case	,    __snake_case       	=							index.search(_UpperCamelCase      , request_timeout=30   )
                     self.assertEqual(scores[0]      , 1   )
                     self.assertEqual(indices[0]      , 0   )

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

                     # batched queries with timeout
                     __snake_case       	=							["""foo""", """bar""", """foobar"""]
                     __snake_case       	=							{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
                     __snake_case	,    __snake_case       	=							index.search_batch(_UpperCamelCase      , request_timeout=30   )
                     __snake_case       	=							[scores[0] for scores in total_scores]
                     __snake_case       	=							[indices[0] for indices in total_indices]
                     self.assertGreater(np.min(_UpperCamelCase   )      , 0   )
                     self.assertListEqual([1, 1, 1]      , _UpperCamelCase   )




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCamelCase__		 =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')







def 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=False	,):


       """simple docstring"""




       output_path.parent.mkdir(parents=__A	,exist_ok=__A		)
       # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
       # so we check the torch version for backwards compatibility
       if is_torch_less_than_1_11:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,use_external_data_format=__A	,enable_onnx_checker=__A	,opset_version=__A	,)
       else:
              export(
                  __A	,__A	,f=output_path.as_posix()	,input_names=__A	,output_names=__A	,dynamic_axes=__A	,do_constant_folding=__A	,opset_version=__A	,)







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              __snake_case       	=							"""cuda"""
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
       else:
              __snake_case       	=							"""cpu"""
       __snake_case       	=							Path(__A		)

       # VAE DECODER
       __snake_case       	=							AutoencoderKL.from_pretrained(model_path + """/vae"""		)
       __snake_case       	=							vae_decoder.config.latent_channels
       # forward only through the decoder part
       __snake_case       	=							vae_decoder.decode
       onnx_export(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).to(device=__A	,dtype=__A		),
               False,
           )	,output_path=output_path / """vae_decoder""" / """model.onnx"""	,ordered_input_names=["""latent_sample""", """return_dict"""]	,output_names=["""sample"""]	,dynamic_axes={
               """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
           }	,opset=__A	,)
       del vae_decoder


if __name__ == "__main__":
    UpperCamelCase__		 =     argparse.ArgumentParser()

    parser.add_argument(
        '''--model_path''',
        type=str,
        required=True,
        help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
    )

    parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
    parser.add_argument(
        '''--opset''',
        default=14,
        type=int,
        help='''The version of the ONNX operator set to use.''',
    )
    parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

    UpperCamelCase__		 =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('''SD: Done: ONNX''')

