
"""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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = SwinvaConfig()
		SCREAMING_SNAKE_CASE_						 = swinva_name.split('''_'''    )

		SCREAMING_SNAKE_CASE_						 = name_split[1]
		if "to" in name_split[3]:
				SCREAMING_SNAKE_CASE_						 = int(name_split[3][-3:]    )
		else:
				SCREAMING_SNAKE_CASE_						 = int(name_split[3]    )
		if "to" in name_split[2]:
				SCREAMING_SNAKE_CASE_						 = int(name_split[2][-2:]    )
		else:
				SCREAMING_SNAKE_CASE_						 = int(name_split[2][6:]    )

		if model_size == "tiny":
				SCREAMING_SNAKE_CASE_						 = 96
				SCREAMING_SNAKE_CASE_						 = (2, 2, 6, 2)
				SCREAMING_SNAKE_CASE_						 = (3, 6, 12, 24)
		elif model_size == "small":
				SCREAMING_SNAKE_CASE_						 = 96
				SCREAMING_SNAKE_CASE_						 = (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE_						 = (3, 6, 12, 24)
		elif model_size == "base":
				SCREAMING_SNAKE_CASE_						 = 128
				SCREAMING_SNAKE_CASE_						 = (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE_						 = (4, 8, 16, 32)
		else:
				SCREAMING_SNAKE_CASE_						 = 192
				SCREAMING_SNAKE_CASE_						 = (2, 2, 18, 2)
				SCREAMING_SNAKE_CASE_						 = (6, 12, 24, 48)

		if "to" in swinva_name:
				SCREAMING_SNAKE_CASE_						 = (12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				SCREAMING_SNAKE_CASE_						 = 21_841
				SCREAMING_SNAKE_CASE_						 = '''huggingface/label-files'''
				SCREAMING_SNAKE_CASE_						 = '''imagenet-22k-id2label.json'''
				SCREAMING_SNAKE_CASE_						 = json.load(open(hf_hub_download(__a,							__a,							repo_type='''dataset'''    ),							'''r'''    )    )
				SCREAMING_SNAKE_CASE_						 = {int(__a    ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE_						 = idalabel
				SCREAMING_SNAKE_CASE_						 = {v: k for k, v in idalabel.items()}

		else:
				SCREAMING_SNAKE_CASE_						 = 1_000
				SCREAMING_SNAKE_CASE_						 = '''huggingface/label-files'''
				SCREAMING_SNAKE_CASE_						 = '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE_						 = json.load(open(hf_hub_download(__a,							__a,							repo_type='''dataset'''    ),							'''r'''    )    )
				SCREAMING_SNAKE_CASE_						 = {int(__a    ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE_						 = idalabel
				SCREAMING_SNAKE_CASE_						 = {v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_						 = img_size
		SCREAMING_SNAKE_CASE_						 = num_classes
		SCREAMING_SNAKE_CASE_						 = embed_dim
		SCREAMING_SNAKE_CASE_						 = depths
		SCREAMING_SNAKE_CASE_						 = num_heads
		SCREAMING_SNAKE_CASE_						 = window_size

		return config



def        _lowerCamelCase ( __a    ):
		if "patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''patch_embed.proj''',							'''embeddings.patch_embeddings.projection'''    )
		if "patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''patch_embed.norm''',							'''embeddings.norm'''    )
		if "layers" in name:
				SCREAMING_SNAKE_CASE_						 = '''encoder.''' + name
		if "attn.proj" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''attn.proj''',							'''attention.output.dense'''    )
		if "attn" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''attn''',							'''attention.self'''    )
		if "norm1" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''norm1''',							'''layernorm_before'''    )
		if "norm2" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''norm2''',							'''layernorm_after'''    )
		if "mlp.fc1" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''mlp.fc1''',							'''intermediate.dense'''    )
		if "mlp.fc2" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''mlp.fc2''',							'''output.dense'''    )
		if "q_bias" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''q_bias''',							'''query.bias'''    )
		if "k_bias" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''k_bias''',							'''key.bias'''    )
		if "v_bias" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''v_bias''',							'''value.bias'''    )
		if "cpb_mlp" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''cpb_mlp''',							'''continuous_position_bias_mlp'''    )
		if name == "norm.weight":
				SCREAMING_SNAKE_CASE_						 = '''layernorm.weight'''
		if name == "norm.bias":
				SCREAMING_SNAKE_CASE_						 = '''layernorm.bias'''

		if "head" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''head''',							'''classifier'''    )
		else:
				SCREAMING_SNAKE_CASE_						 = '''swinv2.''' + name

		return name



def        _lowerCamelCase ( __a,							__a    ):
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_						 = orig_state_dict.pop(__a    )

				if "mask" in key:
						continue
				elif "qkv" in key:
						SCREAMING_SNAKE_CASE_						 = key.split('''.'''    )
						SCREAMING_SNAKE_CASE_						 = int(key_split[1]    )
						SCREAMING_SNAKE_CASE_						 = int(key_split[3]    )
						SCREAMING_SNAKE_CASE_						 = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								SCREAMING_SNAKE_CASE_						 = val[:dim, :]
								SCREAMING_SNAKE_CASE_						 = val[dim : dim * 2, :]
								SCREAMING_SNAKE_CASE_						 = val[-dim:, :]
						else:
								SCREAMING_SNAKE_CASE_						 = val[:dim]
								SCREAMING_SNAKE_CASE_						 = val[
								    dim : dim * 2
								]
								SCREAMING_SNAKE_CASE_						 = val[-dim:]
				else:
						SCREAMING_SNAKE_CASE_						 = val

		return orig_state_dict



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = timm.create_model(__a,							pretrained=__a    )
		timm_model.eval()

		SCREAMING_SNAKE_CASE_						 = get_swinva_config(__a    )
		SCREAMING_SNAKE_CASE_						 = SwinvaForImageClassification(__a    )
		model.eval()

		SCREAMING_SNAKE_CASE_						 = convert_state_dict(timm_model.state_dict(),							__a    )
		model.load_state_dict(__a    )

		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''

		SCREAMING_SNAKE_CASE_						 = AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''',							'''-'''    )    )    )
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )
		SCREAMING_SNAKE_CASE_						 = image_processor(images=__a,							return_tensors='''pt'''    )

		SCREAMING_SNAKE_CASE_						 = timm_model(inputs['''pixel_values''']    )
		SCREAMING_SNAKE_CASE_						 = model(**__a    ).logits

		assert torch.allclose(__a,							__a,							atol=1E-3    )

		print(F'Saving model {swinva_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    )

		model.push_to_hub(
		    repo_path_or_name=Path(__a,							__a    ),							organization='''nandwalritik''',							commit_message='''Add model''',							)


if __name__ == "__main__":
						lowerCAmelCase__							=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--swinv2_name',
						    default='swinv2_tiny_patch4_window8_256',
						    type=str,
						    help='Name of the Swinv2 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__							=				parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)

"""simple docstring"""




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      TransfoXLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									SCREAMING_SNAKE_CASE_						 = [
									    '''<unk>''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''want''',
									    '''unwanted''',
									    '''wa''',
									    '''un''',
									    '''running''',
									    ''',''',
									    '''low''',
									    '''l''',
									]
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       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]    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''<unk> UNwanted , running'''
									SCREAMING_SNAKE_CASE_						 = '''<unk> unwanted, running'''
									return input_text, output_text

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(vocab_file=self.vocab_file       ,       lower_case=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize('''<unk> UNwanted , running'''    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       ['''<unk>''', '''unwanted''', ''',''', '''running''']    )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       [0, 4, 8, 7]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''    )       ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TransfoXLTokenizer(lower_case=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
									SCREAMING_SNAKE_CASE_						 = [
									    '''Hello''',
									    '''(''',
									    '''bracket''',
									    ''')''',
									    '''and''',
									    '''side''',
									    '''@-@''',
									    '''scrolled''',
									    '''[''',
									    '''and''',
									    ''']''',
									    '''Henry''',
									    '''\'s''',
									    '''$''',
									    '''5''',
									    '''@,@''',
									    '''000''',
									    '''with''',
									    '''3''',
									    '''@.@''',
									    '''34''',
									    '''m''',
									    '''.''',
									    '''What''',
									    '''\'s''',
									    '''up''',
									    '''!''',
									    '''?''',
									]

									self.assertListEqual(tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )

									self.assertEqual(tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizer()
									SCREAMING_SNAKE_CASE_						 = len(SCREAMING_SNAKE_CASE_    )

									tokenizer.add_tokens(['''new1''', '''new2''']    )
									tokenizer.move_added_token('''new1'''       ,       1    )

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(SCREAMING_SNAKE_CASE_    )       ,       original_len + 2    )
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode('''new1'''    )       ,       [1]    )
									self.assertEqual(tokenizer.decode([1]    )       ,       '''new1'''    )