


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 							__lowercase       (			__lowerCAmelCase							:    str       ):
							a__					=					[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 1_8, 2]
							a__					=					True if 'large' in model_name or 'huge' in model_name else False
							a__					=					True if 'large' in model_name or 'huge' in model_name else False
							a__					=					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:
																					a__					=					[3, 3, 3, 3]
																					a__					=					[5, 5, 5, 5]
														elif "fl4" in model_name:
																					a__					=					[4, 4, 4, 4]
																					a__					=					[3, 3, 3, 3]

							if "tiny" in model_name or "small" in model_name or "base" in model_name:
														a__					=					[3, 3, 3, 3]
														if "lrf" in model_name:
																					a__					=					[3, 3, 3, 3]
														else:
																					a__					=					[2, 2, 2, 2]

							if "tiny" in model_name:
														a__					=					9_6
							elif "small" in model_name:
														a__					=					9_6
							elif "base" in model_name:
														a__					=					1_2_8
							elif "large" in model_name:
														a__					=					1_9_2
							elif "xlarge" in model_name:
														a__					=					2_5_6
							elif "huge" in model_name:
														a__					=					3_5_2

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

							a__					=					json.load(open(hf_hub_download(__lowerCAmelCase ,   __lowerCAmelCase ,   repo_type='dataset'       ) ,   'r'       )       )
							a__					=					{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
							a__					=					{v: k for k, v in idalabel.items()}

							a__					=					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 							__lowercase       (			__lowerCAmelCase							:    Optional[int]       ):
							if "patch_embed.proj" in name:
														a__					=					name.replace('patch_embed.proj' ,   'embeddings.patch_embeddings.projection'       )
							if "patch_embed.norm" in name:
														a__					=					name.replace('patch_embed.norm' ,   'embeddings.norm'       )
							if "layers" in name:
														a__					=					'encoder.' + name
							if "encoder.layers" in name:
														a__					=					name.replace('encoder.layers' ,   'encoder.stages'       )
							if "downsample.proj" in name:
														a__					=					name.replace('downsample.proj' ,   'downsample.projection'       )
							if "blocks" in name:
														a__					=					name.replace('blocks' ,   'layers'       )
							if "modulation.f.weight" in name or "modulation.f.bias" in name:
														a__					=					name.replace('modulation.f' ,   'modulation.projection_in'       )
							if "modulation.h.weight" in name or "modulation.h.bias" in name:
														a__					=					name.replace('modulation.h' ,   'modulation.projection_context'       )
							if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
														a__					=					name.replace('modulation.proj' ,   'modulation.projection_out'       )

							if name == "norm.weight":
														a__					=					'layernorm.weight'
							if name == "norm.bias":
														a__					=					'layernorm.bias'

							if "head" in name:
														a__					=					name.replace('head' ,   'classifier'       )
							else:
														a__					=					'focalnet.' + name

							return name






def 							__lowercase       (			__lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Dict=False       ):
							# fmt: off
							a__					=					{
							    '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

							a__					=					model_name_to_url[model_name]
							print('Checkpoint URL: ' ,   __lowerCAmelCase       )
							a__					=					torch.hub.load_state_dict_from_url(__lowerCAmelCase ,   map_location='cpu'       )['model']

							# rename keys
							for key in state_dict.copy().keys():
														a__					=					state_dict.pop(__lowerCAmelCase       )
														a__					=					val

							a__					=					get_focalnet_config(__lowerCAmelCase       )
							a__					=					FocalNetForImageClassification(__lowerCAmelCase       )
							model.eval()

							# load state dict
							model.load_state_dict(__lowerCAmelCase       )

							# verify conversion
							a__					=					'http://images.cocodataset.org/val2017/000000039769.jpg'

							a__					=					BitImageProcessor(
							    do_resize=__lowerCAmelCase ,   size={'shortest_edge': 2_5_6} ,   resample=PILImageResampling.BILINEAR ,   do_center_crop=__lowerCAmelCase ,   crop_size=2_2_4 ,   do_normalize=__lowerCAmelCase ,   image_mean=__lowerCAmelCase ,   image_std=__lowerCAmelCase ,   )
							a__					=					Image.open(requests.get(__lowerCAmelCase ,   stream=__lowerCAmelCase       ).raw       )
							a__					=					processor(images=__lowerCAmelCase ,   return_tensors='pt'       )

							a__					=					transforms.Compose(
							    [
							        transforms.Resize(2_5_6       ),
							        transforms.CenterCrop(2_2_4       ),
							        transforms.ToTensor(),
							        transforms.Normalize(mean=[0.485, 0.456, 0.406] ,   std=[0.229, 0.224, 0.225]       ),
							    ]       )

							a__					=					image_transforms(__lowerCAmelCase       ).unsqueeze(0       )

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

							a__					=					model(**__lowerCAmelCase       )

							a__					=					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":
														a__					=					torch.tensor([0.2_166, -0.4_368, 0.2_191]       )
							elif model_name == "focalnet-tiny-lrf":
														a__					=					torch.tensor([1.1_669, 0.0_125, -0.1_695]       )
							elif model_name == "focalnet-small":
														a__					=					torch.tensor([0.4_917, -0.0_430, 0.1_341]       )
							elif model_name == "focalnet-small-lrf":
														a__					=					torch.tensor([-0.2_588, -0.5_342, -0.2_331]       )
							elif model_name == "focalnet-base":
														a__					=					torch.tensor([-0.1_655, -0.4_090, -0.1_730]       )
							elif model_name == "focalnet-base-lrf":
														a__					=					torch.tensor([0.5_306, -0.0_483, -0.3_928]       )
							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__":
						snake_case							:	Dict					      =				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.''',
						)

						snake_case							:	Union[str, Any]					      =				parser.parse_args()
						convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      MvpTokenizer
					UpperCAmelCase__ :   List[Any]   				=      MvpTokenizerFast
					UpperCAmelCase__ :   Optional[int]   				=      True
					UpperCAmelCase__ :   Tuple   				=      filter_roberta_detectors
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Union[str, Any]:
												super().setUp()
												a__					=					[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												a__					=					dict(zip(__snake_case      ,range(len(__snake_case				)				)				)				)
												a__					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												a__					=					{'unk_token': '<unk>'}

												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file']				)
												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['merges_file']				)
												with open(self.vocab_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write(json.dumps(__snake_case				) + '\n'				)
												with open(self.merges_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write('\n'.join(__snake_case				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Tuple				) ->   int:
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Dict				) ->   Dict:
												kwargs.update(self.special_tokens_map				)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]				) ->   List[Any]:
												return "lower newer", "lower newer"
					@cached_property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)
					@require_torch
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												a__					=					[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,max_length=len(__snake_case				)      ,padding=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)

																			self.assertEqual((2, 9)      ,batch.input_ids.shape				)
																			self.assertEqual((2, 9)      ,batch.attention_mask.shape				)
																			a__					=					batch.input_ids.tolist()[0]
																			self.assertListEqual(__snake_case      ,__snake_case				)
																			# Test that special tokens are reset
					@require_torch
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[Any]:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,padding=__snake_case      ,return_tensors='pt'				)
																			# check if input_ids are returned and no labels
																			self.assertIn('input_ids'      ,__snake_case				)
																			self.assertIn('attention_mask'      ,__snake_case				)
																			self.assertNotIn('labels'      ,__snake_case				)
																			self.assertNotIn('decoder_attention_mask'      ,__snake_case				)
					@require_torch
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												a__					=					[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(text_target=__snake_case      ,max_length=32      ,padding='max_length'      ,return_tensors='pt'				)
																			self.assertEqual(32      ,targets['input_ids'].shape[1]				)
					@require_torch
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(
																			    ['I am a small frog' * 10_24, 'I am a small frog']      ,padding=__snake_case      ,truncation=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)
																			self.assertEqual(batch.input_ids.shape      ,(2, 10_24)				)
					@require_torch
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Any:
												a__					=					['A long paragraph for summarization.']
												a__					=					[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,text_target=__snake_case      ,return_tensors='pt'				)
																			a__					=					inputs['input_ids']
																			a__					=					inputs['labels']
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Tuple:
												pass



					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																										a__					=					self.rust_tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					self.tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					'A, <mask> AllenNLP sentence.'
																										a__					=					tokenizer_r.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)
																										a__					=					tokenizer_p.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)

																										# token_type_ids should put 0 everywhere
																										self.assertEqual(sum(tokens_r['token_type_ids']				)      ,sum(tokens_p['token_type_ids']				)				)

																										# attention_mask should put 1 everywhere, so sum over length should be 1
																										self.assertEqual(
																										    sum(tokens_r['attention_mask']				) / len(tokens_r['attention_mask']				)      ,sum(tokens_p['attention_mask']				) / len(tokens_p['attention_mask']				)      ,)

																										a__					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
																										a__					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']				)

																										# Rust correctly handles the space before the mask while python doesnt
																										self.assertSequenceEqual(tokens_p['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
																										self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)

																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)
																										self.assertSequenceEqual(
																										    __snake_case      ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']				)




