

"""simple docstring"""


import os
from typing import List, Optional, Union

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


a					:Optional[Any]    =       logging.get_logger(__name__)

a					:int    =       {"vocab_file": "vocab.txt"}

a					:Optional[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					:Optional[int]    =       {
    "facebook/esm2_t6_8M_UR50D": 1_024,
    "facebook/esm2_t12_35M_UR50D": 1_024,
}




def  _lowercase					(    __lowerCAmelCase							)       ->				List[Any]:

      with open(SCREAMING_SNAKE_CASE_  ,					"""r"""							) as f:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	f.read().splitlines()
            return [l.strip() for l in lines]



class      __a							(_UpperCAmelCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:Optional[int]       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:Any       = ["""input_ids""", """attention_mask"""]

     def __init__(						self	,    _a	,    _a="<unk>"	,    _a="<cls>"	,    _a="<pad>"	,    _a="<mask>"	,    _a="<eos>"	,    **_a	,    )  ->      Tuple:


           """simple docstring"""


           super().__init__(**lowercase__  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	load_vocab_file(lowercase__  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dict(enumerate(self.all_tokens  )  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{tok: ind for ind, tok in enumerate(self.all_tokens  )}
           SCREAMING_SNAKE_CASE__   :      Any      		=	unk_token
           SCREAMING_SNAKE_CASE__   :      str      		=	cls_token
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pad_token
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	mask_token
           SCREAMING_SNAKE_CASE__   :      Any      		=	eos_token
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.all_tokens
           self._create_trie(self.unique_no_split_tokens  )

     def 					_a			(						self	,    _a  )  ->      str:


           """simple docstring"""


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

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


           """simple docstring"""


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

     def 					_a			(						self	,    _a	,    **_a  )  ->      Any:


           """simple docstring"""


           return text.split()

     def 					_a			(						self	,    _a=False  )  ->      Tuple:


           """simple docstring"""


           return len(self._id_to_token  )

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


           """simple docstring"""


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

     def 					_a			(						self	,    _a  )  ->      Any:


           """simple docstring"""


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

     def 					_a			(						self	,    _a  )  ->      List[Any]:


           """simple docstring"""


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

     def 					_a			(						self	,    _a	,    _a = None  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	[self.cls_token_id]
           SCREAMING_SNAKE_CASE__   :      Dict      		=	[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 					_a			(						self	,    _a	,    _a = None	,    _a = False  )  ->      Optional[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]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	[1] + ([0] * len(lowercase__  )) + [1]
           if token_ids_a is not None:
                 mask += [0] * len(lowercase__  ) + [1]
           return mask

     def 					_a			(						self	,    _a	,    _a  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	os.path.join(lowercase__	,    (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""  )
           with open(lowercase__	,    """w"""  ) as f:
                 f.write("""\n""".join(self.all_tokens  )  )
           return (vocab_file,)

     @property
     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           return self.get_vocab_size(with_added_tokens=lowercase__  )





     def 					_a			(						self	,    _a	,    _a = False  )  ->      List[Any]:


           """simple docstring"""


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







"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DDPMScheduler,)

     def 					_a			(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """variance_type""": """fixed_small""",
               """clip_sample""": True,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]	,    [0.002, 0.02, 0.2, 2]  ):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a  )

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


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a  )

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


           """simple docstring"""


           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a  )

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           self.check_over_configs(thresholding=_a  )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.00_979  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      str      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 258.9_606  ) < 1E-2
           assert abs(result_mean.item() - 0.3_372  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 202.0_296  ) < 1E-2
           assert abs(result_mean.item() - 0.2_631  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler.timesteps

           for i, timestep in enumerate(_a  ):
                 if i == len(_a  ) - 1:
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	-1
                 else:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	timesteps[i + 1]

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.previous_timestep(_a  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	prev_t.item()

                 self.assertEqual(_a	,    _a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[100, 87, 50, 51, 0]

           with self.assertRaises(_a	,    msg="""`custom_timesteps` must be in descending order."""  ):
                 scheduler.set_timesteps(timesteps=_a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[100, 87, 50, 1, 0]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(_a  )

           with self.assertRaises(_a	,    msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""  ):
                 scheduler.set_timesteps(num_inference_steps=_a	,    timesteps=_a  )





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               _a	,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""	,    ):
                 scheduler.set_timesteps(timesteps=_a  )




