





import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase       :     List[str]   =     logging.get_logger(__name__)



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase=False     )						-> Any:
 UpperCamelCase	:					Dict       	=   []
 for i in range(config.num_hidden_layers     ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")     )
  rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")     )
  rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")     )
  rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")     )
  rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")     )
  rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")     )
  rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")     )
  rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")     )
  rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")     )
  rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")     )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ("cls_token", "deit.embeddings.cls_token"),
         ("dist_token", "deit.embeddings.distillation_token"),
         ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
         ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
         ("pos_embed", "deit.embeddings.position_embeddings"),
     ]     )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
          ("pre_logits.fc.weight", "pooler.dense.weight"),
          ("pre_logits.fc.bias", "pooler.dense.bias"),
      ]     )

  # if just the base model, we should remove "deit" from all keys that start with "deit"
  UpperCamelCase	:					Optional[int]       	=   [(pair[0], pair[1][4:]) if pair[1].startswith("deit"     ) else pair for pair in rename_keys]
 else:
  # layernorm + classification heads
  rename_keys.extend(
      [
          ("norm.weight", "deit.layernorm.weight"),
          ("norm.bias", "deit.layernorm.bias"),
          ("head.weight", "cls_classifier.weight"),
          ("head.bias", "cls_classifier.bias"),
          ("head_dist.weight", "distillation_classifier.weight"),
          ("head_dist.bias", "distillation_classifier.bias"),
      ]     )

 return rename_keys



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=False     )						-> Union[str, Any]:
 for i in range(config.num_hidden_layers     ):
  if base_model:
   UpperCamelCase	:					Tuple       	=   ""
  else:
   UpperCamelCase	:					Dict       	=   "deit."
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  UpperCamelCase	:					List[str]       	=   state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""     )
  UpperCamelCase	:					Dict       	=   state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""     )
  # next, add query, keys and values (in that order) to the state dict
  UpperCamelCase	:					Dict       	=   in_proj_weight[
      : config.hidden_size, :
  ]
  UpperCamelCase	:					str       	=   in_proj_bias[: config.hidden_size]
  UpperCamelCase	:					Optional[Any]       	=   in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  UpperCamelCase	:					Optional[int]       	=   in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  UpperCamelCase	:					Dict       	=   in_proj_weight[
      -config.hidden_size :, :
  ]
  UpperCamelCase	:					Optional[int]       	=   in_proj_bias[-config.hidden_size :]



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[Any]       	=   dct.pop(_lowerCAmelCase     )
 UpperCamelCase	:					Optional[Any]       	=   val



def 		A_				(     )						-> Optional[Any]:
 UpperCamelCase	:					Optional[int]       	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
 UpperCamelCase	:					List[Any]       	=   Image.open(requests.get(_lowerCAmelCase							,   stream=_lowerCAmelCase     ).raw     )
 return im



@torch.no_grad()
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[str]:
 UpperCamelCase	:					str       	=   DeiTConfig()
 # all deit models have fine-tuned heads
 UpperCamelCase	:					Tuple       	=   False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 UpperCamelCase	:					List[str]       	=   1000
 UpperCamelCase	:					int       	=   "huggingface/label-files"
 UpperCamelCase	:					Optional[int]       	=   "imagenet-1k-id2label.json"
 UpperCamelCase	:					Union[str, Any]       	=   json.load(open(hf_hub_download(_lowerCAmelCase							,   _lowerCAmelCase							,   repo_type="dataset"     )							,   "r"     )     )
 UpperCamelCase	:					List[str]       	=   {int(_lowerCAmelCase     ): v for k, v in idalabel.items()}
 UpperCamelCase	:					Tuple       	=   idalabel
 UpperCamelCase	:					List[Any]       	=   {v: k for k, v in idalabel.items()}
 UpperCamelCase	:					int       	=   int(deit_name[-6:-4]     )
 UpperCamelCase	:					str       	=   int(deit_name[-3:]     )
 # size of the architecture
 if deit_name[9:].startswith("tiny"     ):
  UpperCamelCase	:					Dict       	=   192
  UpperCamelCase	:					Optional[int]       	=   768
  UpperCamelCase	:					List[str]       	=   12
  UpperCamelCase	:					Any       	=   3
 elif deit_name[9:].startswith("small"     ):
  UpperCamelCase	:					Tuple       	=   384
  UpperCamelCase	:					Union[str, Any]       	=   1536
  UpperCamelCase	:					Optional[Any]       	=   12
  UpperCamelCase	:					Union[str, Any]       	=   6
 if deit_name[9:].startswith("base"     ):
  pass
 elif deit_name[4:].startswith("large"     ):
  UpperCamelCase	:					Dict       	=   1024
  UpperCamelCase	:					Optional[int]       	=   4096
  UpperCamelCase	:					str       	=   24
  UpperCamelCase	:					str       	=   16

 # load original model from timm
 UpperCamelCase	:					str       	=   timm.create_model(_lowerCAmelCase							,   pretrained=_lowerCAmelCase     )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 UpperCamelCase	:					str       	=   timm_model.state_dict()
 UpperCamelCase	:					Dict       	=   create_rename_keys(_lowerCAmelCase							,   _lowerCAmelCase     )
 for src, dest in rename_keys:
  rename_key(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )
 read_in_q_k_v(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )

 # load HuggingFace model
 UpperCamelCase	:					List[Any]       	=   DeiTForImageClassificationWithTeacher(_lowerCAmelCase     ).eval()
 model.load_state_dict(_lowerCAmelCase     )

 # Check outputs on an image, prepared by DeiTImageProcessor
 UpperCamelCase	:					Dict       	=   int(
     (256 / 224) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
 UpperCamelCase	:					Union[str, Any]       	=   DeiTImageProcessor(size=_lowerCAmelCase							,   crop_size=config.image_size     )
 UpperCamelCase	:					List[str]       	=   image_processor(images=prepare_img()							,   return_tensors="pt"     )
 UpperCamelCase	:					List[Any]       	=   encoding["pixel_values"]
 UpperCamelCase	:					Tuple       	=   model(_lowerCAmelCase     )

 UpperCamelCase	:					Union[str, Any]       	=   timm_model(_lowerCAmelCase     )
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(_lowerCAmelCase							,   outputs.logits							,   atol=1e-3     )

 Path(_lowerCAmelCase     ).mkdir(exist_ok=_lowerCAmelCase     )
 print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""     )
 model.save_pretrained(_lowerCAmelCase     )
 print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
 image_processor.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
    __lowerCamelCase       :     str   =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--deit_name""",
        default="""vit_deit_base_distilled_patch16_224""",
        type=str,
        help="""Name of the DeiT timm model you'd like to convert.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )

    __lowerCamelCase       :     List[str]   =     parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_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


__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_   )
