
"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

lowerCamelCase__   					=		torch.device("""cpu""")
def     __lowerCAmelCase				():
	__lowerCAmelCase			:						List[Any]									=      'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase			:						Dict									=      Image.open(requests.get(_UpperCamelCase       ,  stream=_UpperCamelCase		).raw		)
	return im
def     __lowerCAmelCase				(_UpperCamelCase		):
	if swiftformer_name == "swiftformer_xs":
		return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]		)

	elif swiftformer_name == "swiftformer_s":
		return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]		)

	elif swiftformer_name == "swiftformer_l1":
		return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]		)

	elif swiftformer_name == "swiftformer_l3":
		return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]		)
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			:						Tuple									=      dct.pop(_UpperCamelCase		)
	__lowerCAmelCase			:						Dict									=      val
def     __lowerCAmelCase				(_UpperCamelCase		):
	__lowerCAmelCase			:						Optional[Any]									=      []
	for k in state_dict.keys():
		__lowerCAmelCase			:						str									=      k
		if ".pwconv" in k:
			__lowerCAmelCase			:						List[str]									=      k_new.replace('.pwconv'       ,  '.point_wise_conv'		)
		if ".dwconv" in k:
			__lowerCAmelCase			:						Tuple									=      k_new.replace('.dwconv'       ,  '.depth_wise_conv'		)
		if ".Proj." in k:
			__lowerCAmelCase			:						Optional[int]									=      k_new.replace('.Proj.'       ,  '.proj.'		)
		if "patch_embed" in k_new:
			__lowerCAmelCase			:						List[Any]									=      k_new.replace('patch_embed'       ,  'swiftformer.patch_embed.patch_embedding'		)
		if "network" in k_new:
			__lowerCAmelCase			:						Optional[Any]									=      k_new.split('.'		)
			if ls[2].isdigit():
				__lowerCAmelCase			:						Union[str, Any]									=      'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]		)
			else:
				__lowerCAmelCase			:						int									=      k_new.replace('network'       ,  'swiftformer.encoder.network'		)
		rename_keys.append((k, k_new)		)
	return rename_keys
@torch.no_grad()
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			:						Tuple									=      SwiftFormerConfig()

	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	__lowerCAmelCase			:						Tuple									=      1000
	__lowerCAmelCase			:						Tuple									=      'huggingface/label-files'
	__lowerCAmelCase			:						Any									=      'imagenet-1k-id2label.json'
	__lowerCAmelCase			:						Dict									=      json.load(open(hf_hub_download(_UpperCamelCase       ,  _UpperCamelCase       ,  repo_type='dataset'		)       ,  'r'		)		)
	__lowerCAmelCase			:						Optional[Any]									=      {int(_UpperCamelCase		): v for k, v in idalabel.items()}
	__lowerCAmelCase			:						Optional[Any]									=      idalabel
	__lowerCAmelCase			:						Any									=      {v: k for k, v in idalabel.items()}

	# size of the architecture
	if swiftformer_name == "swiftformer_xs":
		__lowerCAmelCase			:						Optional[int]									=      [3, 3, 6, 4]
		__lowerCAmelCase			:						Union[str, Any]									=      [48, 56, 112, 220]

	elif swiftformer_name == "swiftformer_s":
		__lowerCAmelCase			:						str									=      [3, 3, 9, 6]
		__lowerCAmelCase			:						Tuple									=      [48, 64, 168, 224]

	elif swiftformer_name == "swiftformer_l1":
		__lowerCAmelCase			:						int									=      [4, 3, 10, 5]
		__lowerCAmelCase			:						List[Any]									=      [48, 96, 192, 384]

	elif swiftformer_name == "swiftformer_l3":
		__lowerCAmelCase			:						Union[str, Any]									=      [4, 4, 12, 6]
		__lowerCAmelCase			:						List[str]									=      [64, 128, 320, 512]

	# load state_dict of original model, remove and rename some keys
	if original_ckpt:
		if original_ckpt.startswith('https'		):
			__lowerCAmelCase			:						List[Any]									=      torch.hub.load_state_dict_from_url(_UpperCamelCase       ,  map_location='cpu'       ,  check_hash=_UpperCamelCase		)
		else:
			__lowerCAmelCase			:						Optional[int]									=      torch.load(_UpperCamelCase       ,  map_location='cpu'		)
	__lowerCAmelCase			:						List[Any]									=      checkpoint

	__lowerCAmelCase			:						Optional[int]									=      create_rename_keys(_UpperCamelCase		)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		)

	# load HuggingFace model
	__lowerCAmelCase			:						Optional[Any]									=      SwiftFormerForImageClassification(_UpperCamelCase		).eval()
	hf_model.load_state_dict(_UpperCamelCase		)

	# prepare test inputs
	__lowerCAmelCase			:						Optional[Any]									=      prepare_img()
	__lowerCAmelCase			:						Optional[Any]									=      ViTImageProcessor.from_pretrained('preprocessor_config'		)
	__lowerCAmelCase			:						Dict									=      processor(images=_UpperCamelCase       ,  return_tensors='pt'		)

	# compare outputs from both models
	__lowerCAmelCase			:						int									=      get_expected_output(_UpperCamelCase		)
	__lowerCAmelCase			:						Tuple									=      hf_model(inputs['pixel_values']		).logits

	assert hf_logits.shape == torch.Size([1, 1000]		)
	assert torch.allclose(hf_logits[0, 0:5]       ,  _UpperCamelCase       ,  atol=1e-3		)

	Path(_UpperCamelCase		).mkdir(exist_ok=_UpperCamelCase		)
	print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"		)
	hf_model.save_pretrained(_UpperCamelCase		)


if __name__ == "__main__":
							lowerCamelCase__   					=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--swiftformer_name""",
							    default="""swiftformer_xs""",
							    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
							    type=str,
							    help="""Name of the SwiftFormer model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""./converted_outputs/""",
							    type=str,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

							lowerCamelCase__   					=		parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







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 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Any:




       """simple docstring"""


       import faiss

       a		  =      self._create_dummy_dataset()
       a		  =      dset.map(
           lambda __UpperCAmelCase ,					__UpperCAmelCase  : {"vecs": i * np.ones(5 ,					dtype=np.floataa  )} ,					with_indices=__UpperCAmelCase ,					keep_in_memory=__UpperCAmelCase  )
       a		  =      dset.add_faiss_index('''vecs''' ,					batch_size=100 ,					metric_type=faiss.METRIC_INNER_PRODUCT  )
       a  ,     a		  =      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       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[Any]:




       """simple docstring"""


       import faiss

       a		  =      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 ,					)
       a  ,     a		  =      dset.get_nearest_examples('''vecs''' ,					np.ones(5 ,					dtype=np.floataa  )  )
       self.assertEqual(examples['''filename'''][0] ,					'''my_name-train_29'''  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->Union[str, Any]:




       """simple docstring"""


       import faiss

       a		  =      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  )

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







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Any:




       """simple docstring"""


       a		  =      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       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       from elasticsearch import Elasticsearch

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

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






@require_faiss
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						Any  )    ->Any:




       """simple docstring"""


       import faiss

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

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







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




       """simple docstring"""


       import faiss

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







      def       __lowerCAmelCase					(      self      :						int  )    ->Optional[Any]:




       """simple docstring"""


       import faiss

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







      def       __lowerCAmelCase					(      self      :						int  )    ->Dict:




       """simple docstring"""


       import faiss

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

       a		  =      np.zeros(5 ,					dtype=np.floataa  )
       a		  =      1
       a  ,     a		  =      index.search(__UpperCAmelCase  )
       self.assertGreater(scores[0] ,					0  )
       self.assertEqual(indices[0] ,					1  )
@require_faiss
def 						_a    (      a :Dict )							->			Any:
 import faiss

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

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

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






@require_elasticsearch
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						int  )    ->List[Any]:




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

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

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

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

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


