


'''simple docstring'''







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     :  Union[str, Any]											=    logging.get_logger(__name__)






def       lowercase					(_A ,    _A=False       ):







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							[]
       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"
              _lowerCAmelCase     :				Optional[Any]       =							[(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       lowercase					(_A ,    _A ,    _A=False       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[Any]       =							dct.pop(_A       )
       _lowerCAmelCase     :				int       =							val






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							'http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCAmelCase     :				Optional[Any]       =							Image.open(requests.get(_A ,    stream=_A       ).raw       )
       return im






@torch.no_grad()
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				int       =							DeiTConfig()
       # all deit models have fine-tuned heads
       _lowerCAmelCase     :				int       =							False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       _lowerCAmelCase     :				Optional[Any]       =							1_0_0_0
       _lowerCAmelCase     :				Optional[int]       =							'huggingface/label-files'
       _lowerCAmelCase     :				Optional[int]       =							'imagenet-1k-id2label.json'
       _lowerCAmelCase     :				List[Any]       =							json.load(open(hf_hub_download(_A ,    _A ,    repo_type='dataset'       ) ,    'r'       )       )
       _lowerCAmelCase     :				Any       =							{int(_A       ): v for k, v in idalabel.items()}
       _lowerCAmelCase     :				Optional[Any]       =							idalabel
       _lowerCAmelCase     :				Tuple       =							{v: k for k, v in idalabel.items()}
       _lowerCAmelCase     :				Tuple       =							int(deit_name[-6:-4]       )
       _lowerCAmelCase     :				List[Any]       =							int(deit_name[-3:]       )
       # size of the architecture
       if deit_name[9:].startswith('tiny'       ):
              _lowerCAmelCase     :				str       =							1_9_2
              _lowerCAmelCase     :				Any       =							7_6_8
              _lowerCAmelCase     :				str       =							1_2
              _lowerCAmelCase     :				List[str]       =							3
       elif deit_name[9:].startswith('small'       ):
              _lowerCAmelCase     :				Optional[Any]       =							3_8_4
              _lowerCAmelCase     :				Optional[Any]       =							1_5_3_6
              _lowerCAmelCase     :				Optional[Any]       =							1_2
              _lowerCAmelCase     :				int       =							6
       if deit_name[9:].startswith('base'       ):
              pass
       elif deit_name[4:].startswith('large'       ):
              _lowerCAmelCase     :				str       =							1_0_2_4
              _lowerCAmelCase     :				Optional[int]       =							4_0_9_6
              _lowerCAmelCase     :				str       =							2_4
              _lowerCAmelCase     :				Any       =							1_6

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

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

       # load HuggingFace model
       _lowerCAmelCase     :				int       =							DeiTForImageClassificationWithTeacher(_A       ).eval()
       model.load_state_dict(_A       )

       # Check outputs on an image, prepared by DeiTImageProcessor
       _lowerCAmelCase     :				Union[str, Any]       =							int(
           (2_5_6 / 2_2_4) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       _lowerCAmelCase     :				str       =							DeiTImageProcessor(size=_A ,    crop_size=config.image_size       )
       _lowerCAmelCase     :				Union[str, Any]       =							image_processor(images=prepare_img() ,    return_tensors='pt'       )
       _lowerCAmelCase     :				Optional[Any]       =							encoding['pixel_values']
       _lowerCAmelCase     :				List[Any]       =							model(_A       )

       _lowerCAmelCase     :				Any       =							timm_model(_A       )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(_A ,    outputs.logits ,    atol=1E-3       )

       Path(_A       ).mkdir(exist_ok=_A       )
       print(f'Saving model {deit_name} to {pytorch_dump_folder_path}'       )
       model.save_pretrained(_A       )
       print(f'Saving image processor to {pytorch_dump_folder_path}'       )
       image_processor.save_pretrained(_A       )


if __name__ == "__main__":
       lowerCAmelCase     :  int											=    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     :  int											=    parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





'''simple docstring'''







import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							tempfile.mkdtemp()
            _lowerCAmelCase     :				List[str]       =							5

            # Realm tok
            _lowerCAmelCase     :				str       =							[
                '[UNK]',
                '[CLS]',
                '[SEP]',
                '[PAD]',
                '[MASK]',
                'test',
                'question',
                'this',
                'is',
                'the',
                'first',
                'second',
                'third',
                'fourth',
                'fifth',
                'record',
                'want',
                '##want',
                '##ed',
                'wa',
                'un',
                'runn',
                '##ing',
                ',',
                'low',
                'lowest',
            ]
            _lowerCAmelCase     :				Union[str, Any]       =							os.path.join(self.tmpdirname   ,						'realm_tokenizer' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )
            _lowerCAmelCase     :				Dict       =							os.path.join(snake_case__   ,						VOCAB_FILES_NAMES['vocab_file'] )
            with open(self.vocab_file   ,						'w'   ,						encoding='utf-8' ) as vocab_writer:
                   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

            _lowerCAmelCase     :				List[str]       =							os.path.join(self.tmpdirname   ,						'realm_block_records' )
            os.makedirs(snake_case__   ,						exist_ok=snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_tokenizer' ) )







     def a						(			self ):

            '''simple docstring'''

            shutil.rmtree(self.tmpdirname )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							RealmConfig(num_block_records=self.num_block_records )
            return config







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							Dataset.from_dict(
                {
                    'id': ['0', '1'],
                    'question': ['foo', 'bar'],
                    'answers': [['Foo', 'Bar'], ['Bar']],
                } )
            return dataset







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							np.array(
                [
                    b'This is the first record',
                    b'This is the second record',
                    b'This is the third record',
                    b'This is the fourth record',
                    b'This is the fifth record',
                    b'This is a longer longer longer record',
                ]   ,						dtype=snake_case__   ,						)
            return block_records







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							RealmRetriever(
                block_records=self.get_dummy_block_records()   ,						tokenizer=self.get_tokenizer()   ,						)
            return retriever







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_config()
            _lowerCAmelCase     :				List[Any]       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				Any       =							retriever.tokenizer

            _lowerCAmelCase     :				Tuple       =							np.array([0, 3]   ,						dtype='long' )
            _lowerCAmelCase     :				Dict       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				int       =							tokenizer(
                ['the fourth']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Tuple       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Union[str, Any]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(len(snake_case__ )   ,						2 )
            self.assertEqual(concat_inputs.input_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.attention_mask.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.token_type_ids.shape   ,						(2, 10) )
            self.assertEqual(concat_inputs.special_tokens_mask.shape   ,						(2, 10) )
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']   ,						)
            self.assertEqual(
                tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,						['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							self.get_config()
            _lowerCAmelCase     :				int       =							self.get_dummy_retriever()
            _lowerCAmelCase     :				List[str]       =							retriever.tokenizer

            _lowerCAmelCase     :				List[Any]       =							np.array([0, 3, 5]   ,						dtype='long' )
            _lowerCAmelCase     :				str       =							tokenizer(['Test question'] ).input_ids
            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer(
                ['the fourth', 'longer longer']   ,						add_special_tokens=snake_case__   ,						return_token_type_ids=snake_case__   ,						return_attention_mask=snake_case__   ,						).input_ids
            _lowerCAmelCase     :				Optional[int]       =							config.reader_seq_len

            _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							retriever(
                snake_case__   ,						snake_case__   ,						answer_ids=snake_case__   ,						max_length=snake_case__   ,						return_tensors='np' )

            self.assertEqual([False, True, True]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,						snake_case__ )
            self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.get_dummy_retriever()
            retriever.save_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )

            # Test local path
            _lowerCAmelCase     :				Union[str, Any]       =							retriever.from_pretrained(os.path.join(self.tmpdirname   ,						'realm_block_records' ) )
            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

            # Test mocked remote path
            with patch('transformers.models.realm.retrieval_realm.hf_hub_download' ) as mock_hf_hub_download:
                   _lowerCAmelCase     :				Any       =							os.path.join(
                       os.path.join(self.tmpdirname   ,						'realm_block_records' )   ,						_REALM_BLOCK_RECORDS_FILENAME )
                   _lowerCAmelCase     :				str       =							RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa' )

            self.assertEqual(retriever.block_records[0]   ,						b'This is the first record' )

