



'''simple docstring'''



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class _UpperCamelCase						(   a_					,				unittest.TestCase						):





				'''simple docstring'''





				lowerCAmelCase__    			=			None
				lowerCAmelCase__    			=			BloomTokenizerFast
				lowerCAmelCase__    			=			BloomTokenizerFast
				lowerCAmelCase__    			=			True
				lowerCAmelCase__    			=			False
				lowerCAmelCase__    			=			"""tokenizer_file"""
				lowerCAmelCase__    			=			{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

				def  __lowerCamelCase   (							self     :     List[Any]):







											'''simple docstring'''

											super().setUp()
											__lowercase						=BloomTokenizerFast.from_pretrained('bigscience/tokenizer')
											tokenizer.save_pretrained(self.tmpdirname)

				def  __lowerCamelCase   (							self     :     Optional[Any]     ,				**_lowerCAmelCase     :     Any):







											'''simple docstring'''

											kwargs.update(self.special_tokens_map)
											return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,				**_a)

				def  __lowerCamelCase   (							self     :     Optional[Any]):







											'''simple docstring'''

											__lowercase						=self.get_rust_tokenizer()

											__lowercase						=["The quick brown fox</s>", "jumps over the lazy dog</s>"]
											__lowercase						=[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

											__lowercase						=tokenizer.batch_encode_plus(_a)["input_ids"]
											self.assertListEqual(_a     ,				_a)

											__lowercase						=tokenizer.batch_decode(_a)
											self.assertListEqual(_a     ,				_a)

				def  __lowerCamelCase   (							self     :     Optional[int]     ,				_lowerCAmelCase     :     Dict=6):







											'''simple docstring'''

											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
																									__lowercase						=self.rust_tokenizer_class.from_pretrained(_a     ,				**_a)
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									__lowercase						="This is a simple input"
																									__lowercase						=["This is a simple input 1", "This is a simple input 2"]
																									__lowercase						=("This is a simple input", "This is a pair")
																									__lowercase						=[
																									    ("This is a simple input 1", "This is a simple input 2"),
																									    ("This is a simple pair 1", "This is a simple pair 2"),
																									]

																									# Simple input tests
																									try:
																																tokenizer_r.encode(_a     ,				max_length=_a)
																																tokenizer_r.encode_plus(_a     ,				max_length=_a)

																																tokenizer_r.batch_encode_plus(_a     ,				max_length=_a)
																																tokenizer_r.encode(_a     ,				max_length=_a)
																																tokenizer_r.batch_encode_plus(_a     ,				max_length=_a)
																									except ValueError:
																																self.fail('Bloom Tokenizer should be able to deal with padding')

																									__lowercase						=None  # Hotfixing padding = None
																									self.assertRaises(_a     ,				tokenizer_r.encode     ,				_a     ,				max_length=_a     ,				padding='max_length')

																									# Simple input
																									self.assertRaises(_a     ,				tokenizer_r.encode_plus     ,				_a     ,				max_length=_a     ,				padding='max_length')

																									# Simple input
																									self.assertRaises(
																									    _a     ,				tokenizer_r.batch_encode_plus     ,				_a     ,				max_length=_a     ,				padding='max_length'     ,				)

																									# Pair input
																									self.assertRaises(_a     ,				tokenizer_r.encode     ,				_a     ,				max_length=_a     ,				padding='max_length')

																									# Pair input
																									self.assertRaises(_a     ,				tokenizer_r.encode_plus     ,				_a     ,				max_length=_a     ,				padding='max_length')

																									# Pair input
																									self.assertRaises(
																									    _a     ,				tokenizer_r.batch_encode_plus     ,				_a     ,				max_length=_a     ,				padding='max_length'     ,				)

				def  __lowerCamelCase   (							self     :     Optional[int]):







											'''simple docstring'''

											__lowercase						=self.get_rust_tokenizer()
											__lowercase						=load_dataset('xnli'     ,				'all_languages'     ,				split='test'     ,				streaming=_a)

											__lowercase						=next(iter(_a))["premise"]  # pick up one data
											__lowercase						=list(sample_data.values())

											__lowercase						=list(map(tokenizer.encode     ,				_a))
											__lowercase						=[tokenizer.decode(_a     ,				clean_up_tokenization_spaces=_a) for x in output_tokens]
											self.assertListEqual(_a     ,				_a)

				def  __lowerCamelCase   (							self     :     Optional[int]):







											'''simple docstring'''

											self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)     ,				1)
											self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])     ,				1)





"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

       # extract state_dict for VQVAE
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				Union[str, Any]       		=							"first_stage_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				List[Any]       		=							state_dict[key]

    # extract state_dict for UNetLDM
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				List[Any]       		=							"model.diffusion_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				Optional[Any]       		=							state_dict[key]

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							DDIMScheduler(
           timesteps=config.model.params.timesteps     , beta_schedule="scaled_linear"     , beta_start=config.model.params.linear_start     , beta_end=config.model.params.linear_end     , clip_sample=__snake_case     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", type=str, required=True)
   parser.add_argument("""--config_path""", type=str, required=True)
   parser.add_argument("""--output_path""", type=str, required=True)
   _A			:       Dict   		=       parser.parse_args()

   convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


