


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 _lowerCamelCase(		_a  ):
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						tempfile.mkdtemp()
           _lowercase			:				Optional[Any] 						=						5

           # Realm tok
           _lowercase			:				int 						=						[
               '[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',
           ]
           _lowercase			:				int 						=						os.path.join(self.tmpdirname, 'realm_tokenizer')
           os.makedirs(lowerCamelCase, exist_ok=lowerCamelCase)
           _lowercase			:				Dict 						=						os.path.join(lowerCamelCase, 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]))

           _lowercase			:				Union[str, Any] 						=						os.path.join(self.tmpdirname, 'realm_block_records')
           os.makedirs(lowerCamelCase, exist_ok=lowerCamelCase)
       def      UpperCamelCase	(   self)			->  RealmTokenizer:




           """simple docstring"""

           return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname, 'realm_tokenizer'))
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           shutil.rmtree(self.tmpdirname)
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						RealmConfig(num_block_records=self.num_block_records)
           return config
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Any 						=						Dataset.from_dict(
               {
                   'id': ['0', '1'],
                   'question': ['foo', 'bar'],
                   'answers': [['Foo', 'Bar'], ['Bar']],
               })
           return dataset
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				List[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=lowerCamelCase, )
           return block_records
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				str 						=						RealmRetriever(
               block_records=self.get_dummy_block_records(), tokenizer=self.get_tokenizer(), )
           return retriever
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.get_config()
           _lowercase			:				Dict 						=						self.get_dummy_retriever()
           _lowercase			:				int 						=						retriever.tokenizer

           _lowercase			:				Any 						=						np.array([0, 3], dtype='long')
           _lowercase			:				str 						=						tokenizer(['Test question']).input_ids
           _lowercase			:				List[str] 						=						tokenizer(
               ['the fourth'], add_special_tokens=lowerCamelCase, return_token_type_ids=lowerCamelCase, return_attention_mask=lowerCamelCase, ).input_ids
           _lowercase			:				Tuple 						=						config.reader_seq_len

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						retriever(
               lowerCamelCase, lowerCamelCase, answer_ids=lowerCamelCase, max_length=lowerCamelCase, return_tensors='np')

           self.assertEqual(len(lowerCamelCase), 2)
           self.assertEqual(len(lowerCamelCase), 2)
           self.assertEqual(len(lowerCamelCase), 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      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				str 						=						self.get_config()
           _lowercase			:				List[Any] 						=						self.get_dummy_retriever()
           _lowercase			:				Optional[Any] 						=						retriever.tokenizer

           _lowercase			:				int 						=						np.array([0, 3, 5], dtype='long')
           _lowercase			:				str 						=						tokenizer(['Test question']).input_ids
           _lowercase			:				Tuple 						=						tokenizer(
               ['the fourth', 'longer longer'], add_special_tokens=lowerCamelCase, return_token_type_ids=lowerCamelCase, return_attention_mask=lowerCamelCase, ).input_ids
           _lowercase			:				Dict 						=						config.reader_seq_len

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

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




       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

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

           # Test local path
           _lowercase			:				List[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:
               _lowercase			:				Dict 						=						os.path.join(
                   os.path.join(self.tmpdirname, 'realm_block_records'), _REALM_BLOCK_RECORDS_FILENAME)
               _lowercase			:				int 						=						RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa')

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






import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				List[str] 						=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
    _lowercase			:				Tuple 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Any 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Dict 						=						True if 'large' in model_name or 'huge' in model_name else False

    if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
        if "fl3" in model_name:
            _lowercase			:				Any 						=						[3, 3, 3, 3]
            _lowercase			:				Any 						=						[5, 5, 5, 5]
        elif "fl4" in model_name:
            _lowercase			:				Dict 						=						[4, 4, 4, 4]
            _lowercase			:				Tuple 						=						[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        _lowercase			:				str 						=						[3, 3, 3, 3]
        if "lrf" in model_name:
            _lowercase			:				Optional[int] 						=						[3, 3, 3, 3]
        else:
            _lowercase			:				Dict 						=						[2, 2, 2, 2]

    if "tiny" in model_name:
        _lowercase			:				List[str] 						=						96
    elif "small" in model_name:
        _lowercase			:				Dict 						=						96
    elif "base" in model_name:
        _lowercase			:				Optional[int] 						=						128
    elif "large" in model_name:
        _lowercase			:				List[Any] 						=						192
    elif "xlarge" in model_name:
        _lowercase			:				Optional[Any] 						=						256
    elif "huge" in model_name:
        _lowercase			:				Dict 						=						352

    # set label information
    _lowercase			:				int 						=						'huggingface/label-files'
    if "large" in model_name or "huge" in model_name:
        _lowercase			:				str 						=						'imagenet-22k-id2label.json'
    else:
        _lowercase			:				Tuple 						=						'imagenet-1k-id2label.json'

    _lowercase			:				Union[str, Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    _lowercase			:				Optional[Any] 						=						FocalNetConfig(
        embed_dim=lowerCamelCase_				,					depths=lowerCamelCase_				,					focal_levels=lowerCamelCase_				,					focal_windows=lowerCamelCase_				,					use_conv_embed=lowerCamelCase_				,					idalabel=lowerCamelCase_				,					labelaid=lowerCamelCase_				,					use_post_layernorm=lowerCamelCase_				,					use_layerscale=lowerCamelCase_				,					)

    return config



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    if "patch_embed.proj" in name:
        _lowercase			:				Optional[Any] 						=						name.replace('patch_embed.proj'				,					'embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        _lowercase			:				str 						=						name.replace('patch_embed.norm'				,					'embeddings.norm'					)
    if "layers" in name:
        _lowercase			:				Any 						=						'encoder.' + name
    if "encoder.layers" in name:
        _lowercase			:				int 						=						name.replace('encoder.layers'				,					'encoder.stages'					)
    if "downsample.proj" in name:
        _lowercase			:				Tuple 						=						name.replace('downsample.proj'				,					'downsample.projection'					)
    if "blocks" in name:
        _lowercase			:				str 						=						name.replace('blocks'				,					'layers'					)
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        _lowercase			:				List[str] 						=						name.replace('modulation.f'				,					'modulation.projection_in'					)
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        _lowercase			:				int 						=						name.replace('modulation.h'				,					'modulation.projection_context'					)
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        _lowercase			:				Any 						=						name.replace('modulation.proj'				,					'modulation.projection_out'					)

    if name == "norm.weight":
        _lowercase			:				Any 						=						'layernorm.weight'
    if name == "norm.bias":
        _lowercase			:				Tuple 						=						'layernorm.bias'

    if "head" in name:
        _lowercase			:				Optional[int] 						=						name.replace('head'				,					'classifier'					)
    else:
        _lowercase			:				Optional[int] 						=						'focalnet.' + name

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    # fmt: off
    _lowercase			:				Dict 						=						{
        'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
        'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
        'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
        'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
        'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
        'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
        'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
        'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
        'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
        'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
    }
    # fmt: on

    _lowercase			:				Dict 						=						model_name_to_url[model_name]
    print('Checkpoint URL: '				,					lowerCamelCase_					)
    _lowercase			:				List[str] 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    # rename keys
    for key in state_dict.copy().keys():
        _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						val

    _lowercase			:				Union[str, Any] 						=						get_focalnet_config(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						FocalNetForImageClassification(lowerCamelCase_					)
    model.eval()

    # load state dict
    model.load_state_dict(lowerCamelCase_					)

    # verify conversion
    _lowercase			:				Optional[Any] 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'

    _lowercase			:				Any 						=						BitImageProcessor(
        do_resize=lowerCamelCase_				,					size={'shortest_edge': 256}				,					resample=PILImageResampling.BILINEAR				,					do_center_crop=lowerCamelCase_				,					crop_size=224				,					do_normalize=lowerCamelCase_				,					image_mean=lowerCamelCase_				,					image_std=lowerCamelCase_				,					)
    _lowercase			:				List[str] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    _lowercase			:				List[Any] 						=						processor(images=lowerCamelCase_				,					return_tensors='pt'					)

    _lowercase			:				str 						=						transforms.Compose(
        [
            transforms.Resize(256					),
            transforms.CenterCrop(224					),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,					std=[0.2_29, 0.2_24, 0.2_25]					),
        ]					)

    _lowercase			:				List[str] 						=						image_transforms(lowerCamelCase_					).unsqueeze(0					)

    # verify pixel_values
    assert torch.allclose(inputs.pixel_values				,					lowerCamelCase_				,					atol=1e-4					)

    _lowercase			:				Dict 						=						model(**lowerCamelCase_					)

    _lowercase			:				int 						=						outputs.logits.argmax(-1					).item()
    print('Predicted class:'				,					model.config.idalabel[predicted_class_idx]					)

    print('First values of logits:'				,					outputs.logits[0, :3]					)

    if model_name == "focalnet-tiny":
        _lowercase			:				Optional[Any] 						=						torch.tensor([0.21_66, -0.43_68, 0.21_91]					)
    elif model_name == "focalnet-tiny-lrf":
        _lowercase			:				int 						=						torch.tensor([1.16_69, 0.01_25, -0.16_95]					)
    elif model_name == "focalnet-small":
        _lowercase			:				str 						=						torch.tensor([0.49_17, -0.04_30, 0.13_41]					)
    elif model_name == "focalnet-small-lrf":
        _lowercase			:				Any 						=						torch.tensor([-0.25_88, -0.53_42, -0.23_31]					)
    elif model_name == "focalnet-base":
        _lowercase			:				List[Any] 						=						torch.tensor([-0.16_55, -0.40_90, -0.17_30]					)
    elif model_name == "focalnet-base-lrf":
        _lowercase			:				int 						=						torch.tensor([0.53_06, -0.04_83, -0.39_28]					)
    assert torch.allclose(outputs.logits[0, :3]				,					lowerCamelCase_				,					atol=1e-4					)
    print('Looks ok!'					)

    if pytorch_dump_folder_path is not None:
        print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''					)
        model.save_pretrained(lowerCamelCase_					)
        processor.save_pretrained(lowerCamelCase_					)

    if push_to_hub:
        print(F'''Pushing model and processor of {model_name} to the hub...'''					)
        model.push_to_hub(F'''{model_name}'''					)
        processor.push_to_hub(F'''{model_name}'''					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: int				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="focalnet-tiny",
     type=str,
     help="Name of the FocalNet 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."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
     help="Whether to push the model and processor to the hub.",
 )

 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					parser.parse_args()
 convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


