





'''simple docstring'''

import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam







class __a			( datasets.BeamBasedBuilder      ):





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      return datasets.DatasetInfo(
          features=datasets.Features({"""content""": datasets.Value("""string"""     )}     )					,supervised_keys=lowerCamelCase					,)





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,gen_kwargs={"""examples""": get_test_dummy_examples()}     )]





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      import apache_beam as beam

      return pipeline | "Load Examples" >> beam.Create(lowerCamelCase     )







class __a			( datasets.BeamBasedBuilder      ):





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return datasets.DatasetInfo(
          features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""     )}     )}     )					,supervised_keys=lowerCamelCase					,)





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      return [
          datasets.SplitGenerator(name=datasets.Split.TRAIN					,gen_kwargs={"""examples""": get_test_nested_examples()}     )
      ]





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      import apache_beam as beam

      return pipeline | "Load Examples" >> beam.Create(lowerCamelCase     )





def     __magic_name__							(				)    ->       Dict:

   '''simple docstring'''







   return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]   )]

def     __magic_name__							(				)    ->       int:

   '''simple docstring'''







   return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]   )]







class __a			( _snake_case      ):





   @require_beam
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							len(get_test_dummy_examples()     )
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __SCREAMING_SNAKE_CASE   				=							DummyBeamDataset(cache_dir=lowerCamelCase					,beam_runner="""DirectRunner"""     )
         builder.download_and_prepare()
         self.assertTrue(
             os.path.exists(
                 os.path.join(lowerCamelCase					,builder.name					,"""default"""					,"""0.0.0"""					,f"""{builder.name}-train.arrow"""     )     )     )
         self.assertDictEqual(builder.info.features					,datasets.Features({"""content""": datasets.Value("""string"""     )}     )     )
         __SCREAMING_SNAKE_CASE   				=							builder.as_dataset()
         self.assertEqual(dset["""train"""].num_rows					,lowerCamelCase     )
         self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					,lowerCamelCase     )
         self.assertDictEqual(dset["""train"""][0]					,get_test_dummy_examples()[0][1]     )
         self.assertDictEqual(
             dset["""train"""][expected_num_examples - 1]					,get_test_dummy_examples()[expected_num_examples - 1][1]     )
         self.assertTrue(
             os.path.exists(os.path.join(lowerCamelCase					,builder.name					,"""default"""					,"""0.0.0"""					,"""dataset_info.json"""     )     )     )
         del dset





   @require_beam
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      import apache_beam as beam

      __SCREAMING_SNAKE_CASE   				=							beam.io.parquetio.WriteToParquet

      __SCREAMING_SNAKE_CASE   				=							len(get_test_dummy_examples()     )
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __SCREAMING_SNAKE_CASE   				=							DummyBeamDataset(cache_dir=lowerCamelCase					,beam_runner="""DirectRunner"""     )
         with patch("""apache_beam.io.parquetio.WriteToParquet"""     ) as write_parquet_mock:
            __SCREAMING_SNAKE_CASE   				=							partial(lowerCamelCase					,num_shards=2     )
            builder.download_and_prepare()
         self.assertTrue(
             os.path.exists(
                 os.path.join(
                     lowerCamelCase					,builder.name					,"""default"""					,"""0.0.0"""					,f"""{builder.name}-train-00000-of-00002.arrow"""     )     )     )
         self.assertTrue(
             os.path.exists(
                 os.path.join(
                     lowerCamelCase					,builder.name					,"""default"""					,"""0.0.0"""					,f"""{builder.name}-train-00000-of-00002.arrow"""     )     )     )
         self.assertDictEqual(builder.info.features					,datasets.Features({"""content""": datasets.Value("""string"""     )}     )     )
         __SCREAMING_SNAKE_CASE   				=							builder.as_dataset()
         self.assertEqual(dset["""train"""].num_rows					,lowerCamelCase     )
         self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					,lowerCamelCase     )
         # Order is not preserved when sharding, so we just check that all the elements are there
         self.assertListEqual(sorted(dset["""train"""]["""content"""]     )					,sorted(["""foo""", """bar""", """foobar"""]     )     )
         self.assertTrue(
             os.path.exists(os.path.join(lowerCamelCase					,builder.name					,"""default"""					,"""0.0.0"""					,"""dataset_info.json"""     )     )     )
         del dset





   @require_beam
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __SCREAMING_SNAKE_CASE   				=							DummyBeamDataset(cache_dir=lowerCamelCase     )
         self.assertRaises(datasets.builder.MissingBeamOptions					,builder.download_and_prepare     )





   @require_beam
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							len(get_test_nested_examples()     )
      with tempfile.TemporaryDirectory() as tmp_cache_dir:
         __SCREAMING_SNAKE_CASE   				=							NestedBeamDataset(cache_dir=lowerCamelCase					,beam_runner="""DirectRunner"""     )
         builder.download_and_prepare()
         self.assertTrue(
             os.path.exists(
                 os.path.join(lowerCamelCase					,builder.name					,"""default"""					,"""0.0.0"""					,f"""{builder.name}-train.arrow"""     )     )     )
         self.assertDictEqual(
             builder.info.features					,datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""     )}     )}     )     )
         __SCREAMING_SNAKE_CASE   				=							builder.as_dataset()
         self.assertEqual(dset["""train"""].num_rows					,lowerCamelCase     )
         self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					,lowerCamelCase     )
         self.assertDictEqual(dset["""train"""][0]					,get_test_nested_examples()[0][1]     )
         self.assertDictEqual(
             dset["""train"""][expected_num_examples - 1]					,get_test_nested_examples()[expected_num_examples - 1][1]     )
         self.assertTrue(
             os.path.exists(os.path.join(lowerCamelCase					,builder.name					,"""default"""					,"""0.0.0"""					,"""dataset_info.json"""     )     )     )
         del dset












'''simple docstring'''

from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a   	=    logging.get_logger(__name__)

# TODO Update this
a   	=    {
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class __a			( _snake_case      ):
   __UpperCamelCase :			str						   =						'esm'





   def __init__(       self					:      Tuple					,lowerCamelCase					:      List[Any]=None					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Any=None					,lowerCamelCase					:      Union[str, Any]=768					,lowerCamelCase					:      Tuple=12					,lowerCamelCase					:      int=12					,lowerCamelCase					:      Optional[int]=3072					,lowerCamelCase					:      List[Any]=0.1					,lowerCamelCase					:      Optional[int]=0.1					,lowerCamelCase					:      Any=1026					,lowerCamelCase					:      str=0.02					,lowerCamelCase					:      int=1E-1_2					,lowerCamelCase					:      Union[str, Any]="absolute"					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      str=None					,lowerCamelCase					:      Optional[int]=False					,lowerCamelCase					:      int=False					,lowerCamelCase					:      Union[str, Any]=None					,lowerCamelCase					:      Any=None					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      super().__init__(pad_token_id=lowerCamelCase					,mask_token_id=lowerCamelCase					,**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							position_embedding_type
      __SCREAMING_SNAKE_CASE   				=							use_cache
      __SCREAMING_SNAKE_CASE   				=							emb_layer_norm_before
      __SCREAMING_SNAKE_CASE   				=							token_dropout
      __SCREAMING_SNAKE_CASE   				=							is_folding_model
      if is_folding_model:
         if esmfold_config is None:
            logger.info("""No esmfold_config supplied for folding model, using default values."""     )
            __SCREAMING_SNAKE_CASE   				=							EsmFoldConfig()
         elif isinstance(lowerCamelCase					,lowerCamelCase     ):
            __SCREAMING_SNAKE_CASE   				=							EsmFoldConfig(**lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							esmfold_config
         if vocab_list is None:
            logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"""     )
            __SCREAMING_SNAKE_CASE   				=							get_default_vocab_list()
         else:
            __SCREAMING_SNAKE_CASE   				=							vocab_list
      else:
         __SCREAMING_SNAKE_CASE   				=							None
         __SCREAMING_SNAKE_CASE   				=							None
      if self.esmfold_config is not None and getattr(self.esmfold_config					,"""use_esm_attn_map"""					,lowerCamelCase     ):
         raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super().to_dict()
      if isinstance(self.esmfold_config					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							self.esmfold_config.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			str						   =						None
   __UpperCamelCase :			bool						   =						True
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			float						   =						0

   __UpperCamelCase :			bool						   =						True
   __UpperCamelCase :			bool						   =						False

   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			"TrunkConfig"						   =						None





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      if self.trunk is None:
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig()
      elif isinstance(self.trunk					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							TrunkConfig(**self.trunk     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.trunk.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						48
   __UpperCamelCase :			int						   =						1024
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			int						   =						32
   __UpperCamelCase :			float						   =						0
   __UpperCamelCase :			float						   =						0
   __UpperCamelCase :			bool						   =						False
   __UpperCamelCase :			int						   =						4
   __UpperCamelCase :			Optional[int]						   =						128
   __UpperCamelCase :			"StructureModuleConfig"						   =						None





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      if self.structure_module is None:
         __SCREAMING_SNAKE_CASE   				=							StructureModuleConfig()
      elif isinstance(self.structure_module					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							StructureModuleConfig(**self.structure_module     )

      if self.max_recycles <= 0:
         raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}."""     )
      if self.sequence_state_dim % self.sequence_state_dim != 0:
         raise ValueError(
             """`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
             f""" {self.sequence_state_dim} and {self.sequence_state_dim}."""     )
      if self.pairwise_state_dim % self.pairwise_state_dim != 0:
         raise ValueError(
             """`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
             f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""     )

      __SCREAMING_SNAKE_CASE   				=							self.sequence_state_dim // self.sequence_head_width
      __SCREAMING_SNAKE_CASE   				=							self.pairwise_state_dim // self.pairwise_head_width

      if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
         raise ValueError(
             """`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
             f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""     )
      if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
         raise ValueError(
             """`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
             f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""     )
      if self.pairwise_state_dim % 2 != 0:
         raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""     )

      if self.dropout >= 0.4:
         raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}."""     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							asdict(self     )
      __SCREAMING_SNAKE_CASE   				=							self.structure_module.to_dict()
      return output







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						384
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						16
   __UpperCamelCase :			int						   =						128
   __UpperCamelCase :			int						   =						12
   __UpperCamelCase :			int						   =						4
   __UpperCamelCase :			int						   =						8
   __UpperCamelCase :			float						   =						0.1
   __UpperCamelCase :			int						   =						8
   __UpperCamelCase :			int						   =						1
   __UpperCamelCase :			int						   =						2
   __UpperCamelCase :			int						   =						7
   __UpperCamelCase :			int						   =						10
   __UpperCamelCase :			float						   =						1E-8
   __UpperCamelCase :			float						   =						1E5





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return asdict(self     )





def     __magic_name__							(				)    ->       Dict:

   '''simple docstring'''







   return (
       "<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>",
   )





