



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Dict       				=    (PNDMScheduler,)
   A    :  List[Any]       				=    (("num_inference_steps", 50),)


   def 			UpperCAmelCase		(						self  :			int			,				**lowerCAmelCase  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  {
             "num_train_timesteps": 10_00,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**UpperCamelCase_)
         return config


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			str=0			,				**lowerCAmelCase  :			Any)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  dict(self.forward_default_kwargs)
         lowercase__     =  kwargs.pop('num_inference_steps'			,				UpperCamelCase_)
         lowercase__     =  self.dummy_sample
         lowercase__     =  0.1 * sample
         lowercase__     =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
               lowercase__     =  self.get_scheduler_config(**UpperCamelCase_)
               lowercase__     =  scheduler_class(**UpperCamelCase_)
               scheduler.set_timesteps(UpperCamelCase_)
               # copy over dummy past residuals
               lowercase__     =  dummy_past_residuals[:]

               with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(UpperCamelCase_)
                     lowercase__     =  scheduler_class.from_pretrained(UpperCamelCase_)
                     new_scheduler.set_timesteps(UpperCamelCase_)
                     # copy over dummy past residuals
                     lowercase__     =  dummy_past_residuals[:]

               lowercase__     =  scheduler.step_prk(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample
               lowercase__     =  new_scheduler.step_prk(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"

               lowercase__     =  scheduler.step_plms(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample
               lowercase__     =  new_scheduler.step_plms(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"


   def 			UpperCAmelCase		(						self  :			Any)	->				Union[str, Any]:




         """simple docstring"""

         pass


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			Optional[Any]=0			,				**lowerCAmelCase  :			Dict)	->				str:




         """simple docstring"""

         lowercase__     =  dict(self.forward_default_kwargs)
         lowercase__     =  kwargs.pop('num_inference_steps'			,				UpperCamelCase_)
         lowercase__     =  self.dummy_sample
         lowercase__     =  0.1 * sample
         lowercase__     =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
               lowercase__     =  self.get_scheduler_config()
               lowercase__     =  scheduler_class(**UpperCamelCase_)
               scheduler.set_timesteps(UpperCamelCase_)

               # copy over dummy past residuals (must be after setting timesteps)
               lowercase__     =  dummy_past_residuals[:]

               with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(UpperCamelCase_)
                     lowercase__     =  scheduler_class.from_pretrained(UpperCamelCase_)
                     # copy over dummy past residuals
                     new_scheduler.set_timesteps(UpperCamelCase_)

                     # copy over dummy past residual (must be after setting timesteps)
                     lowercase__     =  dummy_past_residuals[:]

               lowercase__     =  scheduler.step_prk(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample
               lowercase__     =  new_scheduler.step_prk(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"

               lowercase__     =  scheduler.step_plms(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample
               lowercase__     =  new_scheduler.step_plms(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample

               assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"


   def 			UpperCAmelCase		(						self  :			str			,				**lowerCAmelCase  :			Tuple)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(**UpperCamelCase_)
         lowercase__     =  scheduler_class(**UpperCamelCase_)

         lowercase__     =  10
         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         scheduler.set_timesteps(UpperCamelCase_)

         for i, t in enumerate(scheduler.prk_timesteps):
               lowercase__     =  model(UpperCamelCase_			,				UpperCamelCase_)
               lowercase__     =  scheduler.step_prk(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps):
               lowercase__     =  model(UpperCamelCase_			,				UpperCamelCase_)
               lowercase__     =  scheduler.step_plms(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_).prev_sample

         return sample


   def 			UpperCAmelCase		(						self  :			Dict)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  dict(self.forward_default_kwargs)

         lowercase__     =  kwargs.pop('num_inference_steps'			,				UpperCamelCase_)

         for scheduler_class in self.scheduler_classes:
               lowercase__     =  self.get_scheduler_config()
               lowercase__     =  scheduler_class(**UpperCamelCase_)

               lowercase__     =  self.dummy_sample
               lowercase__     =  0.1 * sample

               if num_inference_steps is not None and hasattr(UpperCamelCase_			,				'set_timesteps'):
                     scheduler.set_timesteps(UpperCamelCase_)
               elif num_inference_steps is not None and not hasattr(UpperCamelCase_			,				'set_timesteps'):
                     lowercase__     =  num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               lowercase__     =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               lowercase__     =  dummy_past_residuals[:]

               lowercase__     =  scheduler.step_prk(UpperCamelCase_			,				0			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample
               lowercase__     =  scheduler.step_prk(UpperCamelCase_			,				1			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample

               self.assertEqual(output_a.shape			,				sample.shape)
               self.assertEqual(output_a.shape			,				output_a.shape)

               lowercase__     =  scheduler.step_plms(UpperCamelCase_			,				0			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample
               lowercase__     =  scheduler.step_plms(UpperCamelCase_			,				1			,				UpperCamelCase_			,				**UpperCamelCase_).prev_sample

               self.assertEqual(output_a.shape			,				sample.shape)
               self.assertEqual(output_a.shape			,				output_a.shape)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

         for timesteps in [1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCamelCase_)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				List[Any]:




         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=UpperCamelCase_)

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(steps_offset=1)
         lowercase__     =  scheduler_class(**UpperCamelCase_)
         scheduler.set_timesteps(10)
         assert torch.equal(
             scheduler.timesteps			,				torch.LongTensor(
                 [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1])			,				)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Tuple:




         """simple docstring"""

         for beta_start, beta_end in zip([0.00_01, 0.0_01]			,				[0.0_02, 0.02]):
               self.check_over_configs(beta_start=UpperCamelCase_			,				beta_end=UpperCamelCase_)


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




         """simple docstring"""

         for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=UpperCamelCase_)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Optional[Any]:




         """simple docstring"""

         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=UpperCamelCase_)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Tuple:




         """simple docstring"""

         for t in [1, 5, 10]:
               self.check_over_forward(time_step=UpperCamelCase_)


   def 			UpperCAmelCase		(						self  :			Dict)	->				Optional[int]:




         """simple docstring"""

         for t, num_inference_steps in zip([1, 5, 10]			,				[10, 50, 1_00]):
               self.check_over_forward(num_inference_steps=UpperCamelCase_)


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




         """simple docstring"""

         lowercase__     =  27

         for scheduler_class in self.scheduler_classes:
               lowercase__     =  self.dummy_sample
               lowercase__     =  0.1 * sample

               lowercase__     =  self.get_scheduler_config()
               lowercase__     =  scheduler_class(**UpperCamelCase_)

               scheduler.set_timesteps(UpperCamelCase_)

               # before power of 3 fix, would error on first step, so we only need to do two
               for i, t in enumerate(scheduler.prk_timesteps[:2]):
                     lowercase__     =  scheduler.step_prk(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_).prev_sample


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				int:




         """simple docstring"""

         with self.assertRaises(UpperCamelCase_):
               lowercase__     =  self.scheduler_classes[0]
               lowercase__     =  self.get_scheduler_config()
               lowercase__     =  scheduler_class(**UpperCamelCase_)

               scheduler.step_plms(self.dummy_sample			,				1			,				self.dummy_sample).prev_sample


   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop()
         lowercase__     =  torch.sum(torch.abs(UpperCamelCase_))
         lowercase__     =  torch.mean(torch.abs(UpperCamelCase_))

         assert abs(result_sum.item() - 1_98.13_18) < 1E-2
         assert abs(result_mean.item() - 0.25_80) < 1E-3


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				str:




         """simple docstring"""

         lowercase__     =  self.full_loop(prediction_type='v_prediction')
         lowercase__     =  torch.sum(torch.abs(UpperCamelCase_))
         lowercase__     =  torch.mean(torch.abs(UpperCamelCase_))

         assert abs(result_sum.item() - 67.39_86) < 1E-2
         assert abs(result_mean.item() - 0.08_78) < 1E-3


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




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=UpperCamelCase_			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(UpperCamelCase_))
         lowercase__     =  torch.mean(torch.abs(UpperCamelCase_))

         assert abs(result_sum.item() - 2_30.03_99) < 1E-2
         assert abs(result_mean.item() - 0.29_95) < 1E-3



   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=UpperCamelCase_			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(UpperCamelCase_))
         lowercase__     =  torch.mean(torch.abs(UpperCamelCase_))

         assert abs(result_sum.item() - 1_86.94_82) < 1E-2
         assert abs(result_mean.item() - 0.24_34) < 1E-3





import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


a__      :				int   	=			logging.get_logger(__name__)

a__      :				Tuple   	=			{"vocab_file": "vocab.txt"}

a__      :				int   	=			{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

a__      :				Dict   	=			{
    "facebook/esm2_t6_8M_UR50D": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}

def   _lowerCAmelCase   (						A__	):
      with open(A__						,  'r'	) as f:
            lowercase__     =  f.read().splitlines()
            return [l.strip() for l in lines]



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    VOCAB_FILES_NAMES
   A    :  str       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  List[Any]       				=    ["input_ids", "attention_mask"]


   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Optional[int]="<unk>"			,				lowerCAmelCase  :			Dict="<cls>"			,				lowerCAmelCase  :			List[str]="<pad>"			,				lowerCAmelCase  :			Union[str, Any]="<mask>"			,				lowerCAmelCase  :			Optional[Any]="<eos>"			,				**lowerCAmelCase  :			Any			,				)	->				Optional[Any]:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)
         lowercase__     =  load_vocab_file(lowerCAmelCase)
         lowercase__     =  dict(enumerate(self.all_tokens))
         lowercase__     =  {tok: ind for ind, tok in enumerate(self.all_tokens)}
         lowercase__     =  unk_token
         lowercase__     =  cls_token
         lowercase__     =  pad_token
         lowercase__     =  mask_token
         lowercase__     =  eos_token
         lowercase__     =  self.all_tokens
         self._create_trie(self.unique_no_split_tokens)


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




         """simple docstring"""

         return self._id_to_token.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         return self._token_to_id.get(lowerCAmelCase			,				self._token_to_id.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			str			,				**lowerCAmelCase  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return text.split()


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Any=False)	->				Union[str, Any]:




         """simple docstring"""

         return len(self._id_to_token)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         return {token: i for i, token in enumerate(self.all_tokens)}


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




         """simple docstring"""

         return self._token_to_id.get(lowerCAmelCase			,				self._token_to_id.get(self.unk_token))


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         return self._id_to_token.get(lowerCAmelCase			,				self.unk_token)


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			List[int]			,				lowerCAmelCase  :			Optional[List[int]] = None)	->				List[int]:




         """simple docstring"""

         lowercase__     =  [self.cls_token_id]
         lowercase__     =  [self.eos_token_id]  # No sep token in ESM vocabulary
         if token_ids_a is None:
               if self.eos_token_id is None:
                     return cls + token_ids_a
               else:
                     return cls + token_ids_a + sep
         elif self.eos_token_id is None:
               raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!')
         return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			List			,				lowerCAmelCase  :			Optional[List] = None			,				lowerCAmelCase  :			bool = False)	->				List[int]:




         """simple docstring"""

         if already_has_special_tokens:
               if token_ids_a is not None:
                     raise ValueError(
                         'You should not supply a second sequence if the provided sequence of '
                         'ids is already formatted with special tokens for the model.')

               return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
         lowercase__     =  [1] + ([0] * len(lowerCAmelCase)) + [1]
         if token_ids_a is not None:
               mask += [0] * len(lowerCAmelCase) + [1]
         return mask


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  os.path.join(lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt')
         with open(lowerCAmelCase			,				'w') as f:
               f.write('\n'.join(self.all_tokens))
         return (vocab_file,)


   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         return self.get_vocab_size(with_added_tokens=lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			Union[List[str], List[AddedToken]]			,				lowerCAmelCase  :			bool = False)	->				int:




         """simple docstring"""

         return super()._add_tokens(lowerCAmelCase			,				special_tokens=lowerCAmelCase)
