'''simple docstring'''







import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]				,						UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  Optional[int]				,						UpperCAmelCase__				:  Any		)			->	List[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = s.rsplit(UpperCAmelCase__				,						UpperCAmelCase__		)
							return new.join(UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	List[Any]:


							'''simple docstring'''






							return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Any		)			->	List[str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = {}

							SCREAMING_SNAKE_CASE__     :List[Any]						    = ['group_1', 'group_2', 'group_3', 'group_4']
							for key, value in state_dict.items():
														for group_key in group_keys:
																					if group_key in key:
																												SCREAMING_SNAKE_CASE__     :Any						    = key.replace(F'''{group_key}.'''				,						F'''{group_key}.group.'''		)

														if "res_path" in key:
																					SCREAMING_SNAKE_CASE__     :Optional[Any]						    = key.replace('res_path.'				,						'res_path.path.'		)

														if key.endswith('.w'		):
																					SCREAMING_SNAKE_CASE__     :List[Any]						    = rreplace(UpperCAmelCase__				,						'.w'				,						'.weight'				,						1		)
														if key.endswith('.b'		):
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rreplace(UpperCAmelCase__				,						'.b'				,						'.bias'				,						1		)

														SCREAMING_SNAKE_CASE__     :Any						    = value.float()

							return upgrade


@torch.no_grad()
def    lowerCamelCase       (			UpperCAmelCase__				:  Tuple				,						UpperCAmelCase__				:  Union[str, Any]				,						UpperCAmelCase__				:  int=None				,						UpperCAmelCase__				:  int=True		)			->	List[str]:


							'''simple docstring'''






							from dall_e import Encoder

							SCREAMING_SNAKE_CASE__     :Tuple						    = Encoder()
							if os.path.exists(UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :Dict						    = torch.load(UpperCAmelCase__		)
							else:
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.hub.load_state_dict_from_url(UpperCAmelCase__		)

							if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ckpt.state_dict()
							encoder.load_state_dict(UpperCAmelCase__		)

							if config_path is not None:
														SCREAMING_SNAKE_CASE__     :Any						    = FlavaImageCodebookConfig.from_pretrained(UpperCAmelCase__		)
							else:
														SCREAMING_SNAKE_CASE__     :Any						    = FlavaImageCodebookConfig()

							SCREAMING_SNAKE_CASE__     :Any						    = FlavaImageCodebook(UpperCAmelCase__		).eval()
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = encoder.state_dict()

							SCREAMING_SNAKE_CASE__     :int						    = upgrade_state_dict(UpperCAmelCase__		)
							hf_model.load_state_dict(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = hf_model.state_dict()
							SCREAMING_SNAKE_CASE__     :Tuple						    = count_parameters(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :List[Any]						    = count_parameters(UpperCAmelCase__		)

							assert torch.allclose(UpperCAmelCase__				,						UpperCAmelCase__				,						atol=1e-3		)

							if save_checkpoint:
														hf_model.save_pretrained(UpperCAmelCase__		)
							else:
														return hf_state_dict


if __name__ == "__main__":
		UpperCamelCase_      	=	argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		UpperCamelCase_      	=	parser.parse_args()

		convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''







import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece.model''')
UpperCamelCase_      	=	get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

UpperCamelCase_      	=	'''pt''' if is_torch_available() else '''tf'''




@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Tuple     		= CamembertTokenizer
						A_			:							Dict     		= CamembertTokenizerFast
						A_			:							Any     		= True
						A_			:							List[Any]     		= True




						def      __lowerCamelCase       (    self :       int      )     ->				Tuple:
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = '<pad>'
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_      )							,							UpperCamelCase_      )
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_      )							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       int      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Any						    = list(self.get_tokenizer().get_vocab().keys()      )

													self.assertEqual(vocab_keys[0]							,							'<s>NOTUSED'      )
													self.assertEqual(vocab_keys[1]							,							'<pad>'      )
													self.assertEqual(vocab_keys[-1]							,							'<mask>'      )
													self.assertEqual(len(UpperCamelCase_      )							,							10_04      )




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													self.assertEqual(self.get_tokenizer().vocab_size							,							10_05      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = CamembertTokenizer(UpperCamelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )
													SCREAMING_SNAKE_CASE__     :Dict						    = CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :List[str]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													# <unk> tokens are not the same for `rust` than for `slow`.
													# Because spm gives back raw token instead of `unk` in EncodeAsPieces
													# tokens = tokenizer.tokenize(sequence)
													SCREAMING_SNAKE_CASE__     :int						    = tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[str]:
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_tokenizer()
													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'I was born in 92000, and this is falsé.'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = tokenizer.tokenize(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = rust_tokenizer.tokenize(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = rust_tokenizer.encode(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer.encode(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rust_tokenizer.encode(UpperCamelCase_      )
													self.assertListEqual(UpperCamelCase_							,							UpperCamelCase_      )




						@slow
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													# fmt: off
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 0, 0, 0, 0, 0, 0, 0, 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, 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]]}  # noqa: E501
													# fmt: on

													# camembert is a french model. So we also use french texts.
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [
													    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
													    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
													    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
													    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
													    'telles que la traduction et la synthèse de texte.',
													]

													self.tokenizer_integration_test_util(
													    expected_encoding=UpperCamelCase_							,							model_name='camembert-base'							,							revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'							,							sequences=UpperCamelCase_							,							)






