





import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class A_   (     unittest.TestCase  ):
   lowerCAmelCase__  	=					EsmTokenizer




   def _lowerCAmelCase (self      :str					)->   Tuple:
         super().setUp()
         __A			  =							tempfile.mkdtemp()
         # fmt: off
         __A			  =							["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
         # fmt: on
         __A			  =							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 _lowerCAmelCase (self      :str       ,  **_UpperCamelCase      :str					)->   List[PreTrainedTokenizerBase]:
         return [self.get_tokenizer(**_UpperCamelCase					)]




   def _lowerCAmelCase (self      :Optional[int]       ,  **_UpperCamelCase      :Tuple					)->   PreTrainedTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname       ,  **_UpperCamelCase					)




   def _lowerCAmelCase (self      :Union[str, Any]					)->   Union[str, Any]:
         __A			  =							self.tokenizer_class(self.vocab_file					)

         __A			  =							tokenizer.tokenize('''LAGVS'''					)
         self.assertListEqual(_UpperCamelCase       ,  ['''L''', '''A''', '''G''', '''V''', '''S''']					)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase					)       ,  [4, 5, 6, 7, 8]					)




   def _lowerCAmelCase (self      :Optional[int]					)->   Union[str, Any]:
         __A			  =							self.tokenizer_class(self.vocab_file					)

         __A			  =							'''LAGVS'''
         self.assertListEqual(tokenizer.encode(_UpperCamelCase					)       ,  [0, 4, 5, 6, 7, 8, 2]					)




   def _lowerCAmelCase (self      :Tuple					)->   Optional[Any]:
         __A			  =							self.tokenizer_class(self.vocab_file					)

         __A			  =							['''LAGVS''', '''WCB''']
         __A			  =							tokenizer(_UpperCamelCase       ,  padding=_UpperCamelCase					)['''input_ids''']

         self.assertListEqual(_UpperCamelCase       ,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]					)




   def _lowerCAmelCase (self      :int					)->   Optional[int]:
         __A			  =							self.tokenizer_class(self.vocab_file					)

         __A			  =							['''LAGVS''', '''WCB''']
         __A			  =							tokenizer(_UpperCamelCase       ,  padding=_UpperCamelCase					)['''input_ids''']

         self.assertListEqual(_UpperCamelCase       ,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]					)






   def _lowerCAmelCase (self      :str					)->   str:
         __A			  =							self.get_tokenizers(fast=_UpperCamelCase					)
         for tokenizer in tokenizers:
               with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
                     __A			  =							'''<unk>'''
                     __A			  =							'''<mask>'''

                     __A			  =							tokenizer.tokenize(_UpperCamelCase					)
                     __A			  =							tokenizer.tokenize(_UpperCamelCase					)

                     self.assertEqual(len(_UpperCamelCase					)       ,  1					)
                     self.assertEqual(len(_UpperCamelCase					)       ,  1					)
                     self.assertEqual(token_a[0]       ,  _UpperCamelCase					)
                     self.assertEqual(token_a[0]       ,  _UpperCamelCase					)












import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					(LMSDiscreteScheduler,)
   lowerCAmelCase__  	=					10




   def _lowerCAmelCase (self      :List[str]       ,  **_UpperCamelCase      :Optional[int]					)->   List[str]:
         __A			  =							{
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**_UpperCamelCase					)
         return config




   def _lowerCAmelCase (self      :List[str]					)->   Tuple:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Dict					)->   Optional[int]:
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
               self.check_over_configs(beta_start=_UpperCamelCase       ,  beta_end=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[int]					)->   Any:
         for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=_UpperCamelCase					)




   def _lowerCAmelCase (self      :List[str]					)->   str:
         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_UpperCamelCase					)




   def _lowerCAmelCase (self      :str					)->   List[str]:
         for t in [0, 500, 800]:
               self.check_over_forward(time_step=_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   Tuple:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[int]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config(prediction_type='''v_prediction'''					)
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 0.0_0_1_7					) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06					) < 1e-3




   def _lowerCAmelCase (self      :List[Any]					)->   List[Any]:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A			  =							sample.to(_UpperCamelCase					)

         for i, t in enumerate(scheduler.timesteps					):
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 1_0_0_6.3_8_8					) < 1e-2
         assert abs(result_mean.item() - 1.3_1					) < 1e-3






   def _lowerCAmelCase (self      :List[str]					)->   int:
         __A			  =							self.scheduler_classes[0]
         __A			  =							self.get_scheduler_config()
         __A			  =							scheduler_class(**_UpperCamelCase       ,  use_karras_sigmas=_UpperCamelCase					)

         scheduler.set_timesteps(self.num_inference_steps       ,  device=_UpperCamelCase					)

         __A			  =							self.dummy_model()
         __A			  =							self.dummy_sample_deter.to(_UpperCamelCase					) * scheduler.init_noise_sigma
         __A			  =							sample.to(_UpperCamelCase					)

         for t in scheduler.timesteps:
               __A			  =							scheduler.scale_model_input(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							model(_UpperCamelCase       ,  _UpperCamelCase					)

               __A			  =							scheduler.step(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)
               __A			  =							output.prev_sample

         __A			  =							torch.sum(torch.abs(_UpperCamelCase					)					)
         __A			  =							torch.mean(torch.abs(_UpperCamelCase					)					)

         assert abs(result_sum.item() - 3_8_1_2.9_9_2_7					) < 2e-2
         assert abs(result_mean.item() - 4.9_6_4_8					) < 1e-3





