




"""simple docstring"""

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_lowerCamelCase     :	Dict         =				False
_lowerCamelCase     :	List[str]         =				True
_lowerCamelCase     :	Tuple         =				False


if __name__ == "__main__":
		_lowerCamelCase     :	List[Any]         =				argparse.ArgumentParser()

		parser.add_argument(
		    '--repo_path',
		    default=None,
		    type=str,
		    required=True,
		    help='The config json file corresponding to the architecture.',
		)

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		_lowerCamelCase     :	Optional[Any]         =				parser.parse_args()

		_lowerCamelCase     :	Dict         =				{
		    '''image_size''': '''sample_size''',
		    '''num_res_blocks''': '''layers_per_block''',
		    '''block_channels''': '''block_out_channels''',
		    '''down_blocks''': '''down_block_types''',
		    '''up_blocks''': '''up_block_types''',
		    '''downscale_freq_shift''': '''freq_shift''',
		    '''resnet_num_groups''': '''norm_num_groups''',
		    '''resnet_act_fn''': '''act_fn''',
		    '''resnet_eps''': '''norm_eps''',
		    '''num_head_channels''': '''attention_head_dim''',
		}

		_lowerCamelCase     :	List[str]         =				{
		    '''time_steps''': '''time_proj''',
		    '''mid''': '''mid_block''',
		    '''downsample_blocks''': '''down_blocks''',
		    '''upsample_blocks''': '''up_blocks''',
		}

		_lowerCamelCase     :	Optional[int]         =				'''''' if has_file(args.repo_path, 'config.json') else '''unet'''

		with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
				_lowerCamelCase     :	Union[str, Any]         =				reader.read()
				_lowerCamelCase     :	List[str]         =				json.loads(text)

		if do_only_config:
				for key in config_parameters_to_change.keys():
						config.pop(key, None)

		if has_file(args.repo_path, 'config.json'):
				_lowerCamelCase     :	List[str]         =				UNetaDModel(**config)
		else:
				_lowerCamelCase     :	str         =				UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
				_lowerCamelCase     :	Optional[Any]         =				class_name(**config)

		if do_only_config:
				model.save_config(os.path.join(args.repo_path, subfolder))

		_lowerCamelCase     :	Any         =				dict(model.config)

		if do_only_renaming:
				for key, value in config_parameters_to_change.items():
						if key in config:
								_lowerCamelCase     :	List[str]         =				config[key]
								del config[key]

				_lowerCamelCase     :	Optional[Any]         =				[k.replace('UNetRes', '') for k in config['''down_block_types''']]
				_lowerCamelCase     :	Optional[int]         =				[k.replace('UNetRes', '') for k in config['''up_block_types''']]

		if do_only_weights:
				_lowerCamelCase     :	List[Any]         =				torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

				_lowerCamelCase     :	int         =				{}
				for param_key, param_value in state_dict.items():
						if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
								continue
						_lowerCamelCase     :	Tuple         =				False
						for key, new_key in key_parameters_to_change.items():
								if not has_changed and param_key.split('.')[0] == key:
										_lowerCamelCase     :	int         =				param_value
										_lowerCamelCase     :	int         =				True
						if not has_changed:
								_lowerCamelCase     :	str         =				param_value

				model.load_state_dict(new_state_dict)
				model.save_pretrained(os.path.join(args.repo_path, subfolder))













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


lowerCamelCase       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece.model''')
lowerCamelCase       :       Any        =				get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

lowerCamelCase       :       Optional[int]        =				'''pt''' if is_torch_available() else '''tf'''

@require_sentencepiece
@require_tokenizers
class 						lowerCAmelCase   (       __a					,				unittest.TestCase	):

					'''simple docstring'''

					_A  :	Dict     =					CamembertTokenizer
					_A  :	Union[str, Any]     =					CamembertTokenizerFast
					_A  :	Union[str, Any]     =					True
					_A  :	Tuple     =					True



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[int]:


												"""simple docstring"""

												super().setUp()

												# We have a SentencePiece fixture for testing
												__lowercase					:							Union[str, Any]			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)



					def 					lowerCAmelCase (     self :		Optional[int]							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Dict			=   """<pad>"""
												__lowercase					:							Dict			=   1

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



					def 					lowerCAmelCase (     self :		Optional[Any]							)	->     Tuple:


												"""simple docstring"""

												__lowercase					:							int			=   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(__a							)						,   1004							)



					def 					lowerCAmelCase (     self :		str							)	->     List[Any]:


												"""simple docstring"""

												self.assertEqual(self.get_tokenizer().vocab_size						,   1005							)



					def 					lowerCAmelCase (     self :		Any							)	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   CamembertTokenizer(__a							)
												tokenizer.save_pretrained(self.tmpdirname							)
												__lowercase					:							Tuple			=   CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

												__lowercase					:							List[str]			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Optional[Any]			=   tokenizer.encode(__a							)
												__lowercase					:							List[Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Tuple			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												# <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)
												__lowercase					:							Dict			=   tokenizer.convert_ids_to_tokens(__a							)
												__lowercase					:							Any			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)



					def 					lowerCAmelCase (     self :		Any							)	->     Dict:


												"""simple docstring"""

												if not self.test_rust_tokenizer:
																			return

												__lowercase					:							List[str]			=   self.get_tokenizer()
												__lowercase					:							Any			=   self.get_rust_tokenizer()

												__lowercase					:							Any			=   """I was born in 92000, and this is falsé."""

												__lowercase					:							Tuple			=   tokenizer.tokenize(__a							)
												__lowercase					:							Optional[Any]			=   rust_tokenizer.tokenize(__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Dict			=   tokenizer.encode(__a						,   add_special_tokens=__a							)
												__lowercase					:							Dict			=   rust_tokenizer.encode(__a						,   add_special_tokens=__a							)
												self.assertListEqual(__a						,   __a							)

												__lowercase					:							Any			=   self.get_rust_tokenizer()
												__lowercase					:							str			=   tokenizer.encode(__a							)
												__lowercase					:							Union[str, Any]			=   rust_tokenizer.encode(__a							)
												self.assertListEqual(__a						,   __a							)



					@slow
					def 					lowerCAmelCase (     self :		Optional[int]							)	->     List[Any]:


												"""simple docstring"""

												__lowercase					:							str			=   {"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 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.
												__lowercase					:							List[str]			=   [
												    """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=__a						,   model_name="""camembert-base"""						,   revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""						,   sequences=__a						,   )