




import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__  :  Optional[int]					=       get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")





@require_sentencepiece
@require_tokenizers
class     SCREAMING_SNAKE_CASE__		(UpperCAmelCase_    ,					unittest.TestCase			):
     lowercase_ :      Union[str, Any]				= GPTSwaTokenizer
     lowercase_ :      Dict				= False
     lowercase_ :      List[str]				= True
     lowercase_ :      Optional[Any]				= False
     def        A__      (						self	:		str						):



       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       lowerCAmelCase__										=      GPTSwaTokenizer(_snake_case       ,						eos_token='''<unk>'''       ,						bos_token='''<unk>'''       ,						pad_token='''<unk>'''						)

       tokenizer.save_pretrained(self.tmpdirname						)
     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      '''This is a test'''
       lowerCAmelCase__										=      '''This is a test'''
       return input_text, output_text
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      '''<s>'''
       lowerCAmelCase__										=      1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case						)       ,						_snake_case						)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case						)       ,						_snake_case						)
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      list(self.get_tokenizer().get_vocab().keys()						)

       self.assertEqual(vocab_keys[0]       ,						'''<unk>'''						)
       self.assertEqual(vocab_keys[1]       ,						'''<s>'''						)
       self.assertEqual(vocab_keys[-1]       ,						'''j'''						)
       self.assertEqual(len(_snake_case						)       ,						20_00						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size       ,						20_00						)
     def        A__      (						self	:		str						):



       """simple docstring"""
       lowerCAmelCase__										=      GPTSwaTokenizer(_snake_case						)

       lowerCAmelCase__										=      tokenizer.tokenize('''This is a test'''						)
       self.assertListEqual(_snake_case       ,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case						)       ,						[4_65, 2_87, 2_65, 6_31, 8_42]						)

       lowerCAmelCase__										=      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
       # fmt: off
       self.assertListEqual(
           _snake_case       ,						['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']       ,						)
       # fmt: on

       lowerCAmelCase__										=      tokenizer.convert_tokens_to_ids(_snake_case						)
       self.assertListEqual(
           _snake_case       ,						[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]       ,						)

       lowerCAmelCase__										=      tokenizer.convert_ids_to_tokens(_snake_case						)
       # fmt: off
       self.assertListEqual(
           _snake_case       ,						['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']						)
       # fmt: on
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      GPTSwaTokenizer(_snake_case						)
       lowerCAmelCase__										=      ['''This is a test''', '''I was born in 92000, and this is falsé.''']
       lowerCAmelCase__										=      [
           [4_65, 2_87, 2_65, 6_31, 8_42],
           [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
       ]

       # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
       for text, expected_ids in zip(_snake_case       ,						_snake_case						):
         self.assertListEqual(tokenizer.encode_fast(_snake_case						)       ,						_snake_case						)

       # Test that decode_fast returns the input text
       for text, token_ids in zip(_snake_case       ,						_snake_case						):
         self.assertEqual(tokenizer.decode_fast(_snake_case						)       ,						_snake_case						)



     @slow
     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      [
           '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
           '''Hey there, how are you doing this fine day?''',
           '''This is a text with a trailing spaces followed by a dot     .''',
           '''Häj sväjs lillebrör! =)''',
           '''Det är inget fel på Mr. Cool''',
       ]

       # fmt: off
       lowerCAmelCase__										=      {'''input_ids''': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
       # fmt: on
       self.tokenizer_integration_test_util(
           expected_encoding=_snake_case       ,						model_name='''AI-Sweden/gpt-sw3-126m'''       ,						sequences=_snake_case       ,						)






import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Dict:
			A__	  =     'backbone.' if is_semantic else ''

			A__	  =     []
			for i in range(config.num_hidden_layers       ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')       )
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')       )
						rename_keys.append(
						    (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')       )
						rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')       )

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        (f'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
			        (f'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
			        (f'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
			        (f'''{prefix}pos_embed''', 'beit.embeddings.position_embeddings'),
			    ]       )

			if has_lm_head:
						# mask token + layernorm
						rename_keys.extend(
						    [
						        ('mask_token', 'beit.embeddings.mask_token'),
						        ('norm.weight', 'layernorm.weight'),
						        ('norm.bias', 'layernorm.bias'),
						    ]       )
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
						        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
						        ('head.weight', 'classifier.weight'),
						        ('head.bias', 'classifier.bias'),
						    ]       )

			return rename_keys


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Optional[Any]:
			for i in range(config.num_hidden_layers       ):
						A__	  =     'backbone.' if is_semantic else ''
						# queries, keys and values
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''       )

						A__	  =     in_proj_weight[
						    : config.hidden_size, :
						]
						A__	  =     q_bias
						A__	  =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A__	  =     in_proj_weight[
						    -config.hidden_size :, :
						]
						A__	  =     v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''       )

						A__	  =     gamma_a
						A__	  =     gamma_a


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							)    ->   Dict:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False       )    ->   str:
			A__	  =     False if 'rvlcdip' in checkpoint_url else True
			A__	  =     BeitConfig(use_absolute_position_embeddings=__UpperCamelCase			,						use_mask_token=__UpperCamelCase       )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						A__	  =     1_024
						A__	  =     4_096
						A__	  =     24
						A__	  =     16

			# labels
			if "rvlcdip" in checkpoint_url:
						A__	  =     16
						A__	  =     'huggingface/label-files'
						A__	  =     'rvlcdip-id2label.json'
						A__	  =     json.load(open(hf_hub_download(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )			,						'r'       )       )
						A__	  =     {int(__UpperCamelCase       ): v for k, v in idalabel.items()}
						A__	  =     idalabel
						A__	  =     {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			A__	  =     torch.hub.load_state_dict_from_url(__UpperCamelCase			,						map_location='cpu'       )['model']

			A__	  =     create_rename_keys(__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )
			for src, dest in rename_keys:
						rename_key(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			read_in_q_k_v(__UpperCamelCase			,						__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )

			# load HuggingFace model
			A__	  =     BeitForMaskedImageModeling(__UpperCamelCase       ) if has_lm_head else BeitForImageClassification(__UpperCamelCase       )
			model.eval()
			model.load_state_dict(__UpperCamelCase       )

			# Check outputs on an image
			A__	  =     BeitImageProcessor(
			    size=config.image_size			,						resample=PILImageResampling.BILINEAR			,						do_center_crop=__UpperCamelCase       )
			A__	  =     prepare_img()

			A__	  =     image_processor(images=__UpperCamelCase			,						return_tensors='pt'       )
			A__	  =     encoding['pixel_values']

			A__	  =     model(__UpperCamelCase       )
			A__	  =     outputs.logits

			# verify logits
			A__	  =     [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
			assert logits.shape == torch.Size(__UpperCamelCase       ), "Shape of logits not as expected"

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

			if push_to_hub:
						if has_lm_head:
									A__	  =     'dit-base' if 'base' in checkpoint_url else 'dit-large'
						else:
									A__	  =     'dit-base-finetuned-rvlcdip' if 'dit-b' in checkpoint_url else 'dit-large-finetuned-rvlcdip'
						image_processor.push_to_hub(
						    repo_path_or_name=Path(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add image processor'			,						use_temp_dir=__UpperCamelCase			,						)
						model.push_to_hub(
						    repo_path_or_name=Path(__UpperCamelCase			,						__UpperCamelCase       )			,						organization='nielsr'			,						commit_message='Add model'			,						use_temp_dir=__UpperCamelCase			,						)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
							    type=str,
							    help='''URL to the original PyTorch checkpoint (.pth file).''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




