'''simple docstring'''





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
    from transformers.models.esm.modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmEmbeddings,
        create_position_ids_from_input_ids,
    )




class        __A      :



    def __init__(self    :    str   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[Any]=13   ,			__a    :    Optional[Any]=7   ,			__a    :    Dict=False   ,			__a    :    List[str]=True   ,			__a    :    List[str]=False   ,			__a    :    Any=True   ,			__a    :    Optional[Any]=33   ,			__a    :    Union[str, Any]=32   ,			__a    :    str=5   ,			__a    :    List[Any]=4   ,			__a    :    str=37   ,			__a    :    Dict="gelu"   ,			__a    :    Optional[Any]=0.1   ,			__a    :    List[Any]=0.1   ,			__a    :    Tuple=512   ,			__a    :    int=16   ,			__a    :    str=2   ,			__a    :    Any=0.02   ,			__a    :    Union[str, Any]=3   ,			__a    :    List[Any]=4   ,			__a    :    Optional[Any]=None   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   seq_length
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_input_mask
         UpperCAmelCase_           =   use_token_type_ids
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   type_vocab_size
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   num_labels
         UpperCAmelCase_           =   num_choices
         UpperCAmelCase_           =   scope



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size )

         UpperCAmelCase_           =   None
         if self.use_input_mask:
              UpperCAmelCase_           =   random_attention_mask([self.batch_size, self.seq_length] )

         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None
         if self.use_labels:
              UpperCAmelCase_           =   ids_tensor([self.batch_size]   ,			self.type_sequence_label_size )
              UpperCAmelCase_           =   ids_tensor([self.batch_size, self.seq_length]   ,			self.num_labels )
              UpperCAmelCase_           =   ids_tensor([self.batch_size]   ,			self.num_choices )

         UpperCAmelCase_           =   self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



    def 					_lowercase						(self    :    Any ):
         return EsmConfig(
             vocab_size=self.vocab_size   ,			hidden_size=self.hidden_size   ,			pad_token_id=1   ,			num_hidden_layers=self.num_hidden_layers   ,			num_attention_heads=self.num_attention_heads   ,			intermediate_size=self.intermediate_size   ,			hidden_act=self.hidden_act   ,			hidden_dropout_prob=self.hidden_dropout_prob   ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,			max_position_embeddings=self.max_position_embeddings   ,			type_vocab_size=self.type_vocab_size   ,			initializer_range=self.initializer_range   ,			)



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Tuple   ,			__a    :    List[str]   ,			__a    :    Optional[int]   ,			__a    :    List[Any]   ,			__a    :    List[str]   ,			__a    :    int ):
         UpperCAmelCase_           =   EsmModel(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a )
         UpperCAmelCase_           =   model(__a )
         UpperCAmelCase_           =   model(__a )

         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, self.seq_length, self.hidden_size) )
         self.parent.assertEqual(result.pooler_output.shape   ,			(self.batch_size, self.hidden_size) )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    int   ,			__a    :    Optional[Any]   ,			__a    :    List[str]   ,			__a    :    Dict   ,			__a    :    Tuple   ,			__a    :    str ):
         UpperCAmelCase_           =   EsmForMaskedLM(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a   ,			labels=__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.seq_length, self.vocab_size) )



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Dict   ,			__a    :    Dict   ,			__a    :    List[str]   ,			__a    :    List[str]   ,			__a    :    Any   ,			__a    :    int ):
         UpperCAmelCase_           =   self.num_labels
         UpperCAmelCase_           =   EsmForTokenClassification(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a   ,			labels=__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.seq_length, self.num_labels) )






    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict




@require_torch
class        __A      ( UpperCamelCase__		,	UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Union[str, Any]          =  False

    a__       :						Tuple          =  (
        (
            EsmForMaskedLM,
            EsmModel,
            EsmForSequenceClassification,
            EsmForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    a__       :						Any          =  ()
    a__       :						Union[str, Any]          =  (
        {
            """feature-extraction""": EsmModel,
            """fill-mask""": EsmForMaskedLM,
            """text-classification""": EsmForSequenceClassification,
            """token-classification""": EsmForTokenClassification,
            """zero-shot""": EsmForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__       :						List[Any]          =  True



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   EsmModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    List[Any] ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase_           =   type
              self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__a )



    @slow
    def 					_lowercase						(self    :    Dict ):
         for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_           =   EsmModel.from_pretrained(__a )
              self.assertIsNotNone(__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()[0]
         UpperCAmelCase_           =   EsmEmbeddings(config=__a )

         UpperCAmelCase_           =   torch.as_tensor([[12, 31, 13, model.padding_idx]] )
         UpperCAmelCase_           =   torch.as_tensor(
             [
                 [
                     0 + model.padding_idx + 1,
                     1 + model.padding_idx + 1,
                     2 + model.padding_idx + 1,
                     model.padding_idx,
                 ]
             ] )
         UpperCAmelCase_           =   create_position_ids_from_input_ids(__a   ,			model.padding_idx )
         self.assertEqual(position_ids.shape   ,			expected_positions.shape )
         self.assertTrue(torch.all(torch.eq(__a   ,			__a ) ) )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()[0]
         UpperCAmelCase_           =   EsmEmbeddings(config=__a )

         UpperCAmelCase_           =   torch.empty(2   ,			4   ,			30 )
         UpperCAmelCase_           =   [
             0 + embeddings.padding_idx + 1,
             1 + embeddings.padding_idx + 1,
             2 + embeddings.padding_idx + 1,
             3 + embeddings.padding_idx + 1,
         ]
         UpperCAmelCase_           =   torch.as_tensor([expected_single_positions, expected_single_positions] )
         UpperCAmelCase_           =   embeddings.create_position_ids_from_inputs_embeds(__a )
         self.assertEqual(position_ids.shape   ,			expected_positions.shape )
         self.assertTrue(torch.all(torch.eq(__a   ,			__a ) ) )



    @unittest.skip("Esm does not support embedding resizing" )
    def 					_lowercase						(self    :    List[str] ):
         pass



    @unittest.skip("Esm does not support embedding resizing" )
    def 					_lowercase						(self    :    str ):
         pass






    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
    def 					_lowercase						(self    :    Tuple ):
         pass




@require_torch
class        __A      ( UpperCamelCase__	):



    @slow
    def 					_lowercase						(self    :    Any ):
         with torch.no_grad():
              UpperCAmelCase_           =   EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
              model.eval()
              UpperCAmelCase_           =   torch.tensor([[0, 1, 2, 3, 4, 5]] )
              UpperCAmelCase_           =   model(__a )[0]

              UpperCAmelCase_           =   33

              UpperCAmelCase_           =   torch.Size((1, 6, vocab_size) )
              self.assertEqual(output.shape   ,			__a )

              UpperCAmelCase_           =   torch.tensor(
                  [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]] )
              self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__a   ,			atol=1E-4 ) )






    @slow
    def 					_lowercase						(self    :    List[Any] ):
         with torch.no_grad():
              UpperCAmelCase_           =   EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
              model.eval()

              UpperCAmelCase_           =   torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
              UpperCAmelCase_           =   model(__a )[0]
              # compare the actual values for a slice.
              UpperCAmelCase_           =   torch.tensor(
                  [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]] )
              self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__a   ,			atol=1E-4 ) )





'''simple docstring'''





from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class        __A      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			__a    :    Distribution   ,			__a    :    Dict=None   ,			__a    :    int=None   ,			__a    :    Any=0 ):
         UpperCAmelCase_           =   1.0 if scale is None else scale
         UpperCAmelCase_           =   0.0 if loc is None else loc

         super().__init__(__a   ,			[AffineTransform(loc=self.loc   ,			scale=self.scale   ,			event_dim=__a )] )



    @property
    def 					_lowercase						(self    :    Union[str, Any] ):
         return self.base_dist.mean * self.scale + self.loc



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.base_dist.variance * self.scale**2






    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.variance.sqrt()




class        __A      ( nn.Module	):



    def __init__(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    Dict[str, int]   ,			__a    :    Callable[..., Tuple[torch.Tensor]]   ,			**__a    :    List[str] ):
         super().__init__(**__a )
         UpperCAmelCase_           =   args_dim
         UpperCAmelCase_           =   nn.ModuleList([nn.Linear(__a   ,			__a ) for dim in args_dim.values()] )
         UpperCAmelCase_           =   domain_map






    def 					_lowercase						(self    :    List[str]   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   [proj(__a ) for proj in self.proj]

         return self.domain_map(*__a )




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    List[str] ):
         super().__init__()
         UpperCAmelCase_           =   function






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			*__a    :    Optional[int] ):
         return self.function(__a   ,			*__a )




class        __A      :
    a__       :						type
    a__       :						int
    a__       :						Dict[str, int]



    def __init__(self    :    List[Any]   ,			__a    :    int = 1 ):
         UpperCAmelCase_           =   dim
         UpperCAmelCase_           =   {k: dim * self.args_dim[k] for k in self.args_dim}



    def 					_lowercase						(self    :    Any   ,			__a    :    Any ):
         if self.dim == 1:
              return self.distribution_class(*__a )
         else:
              return Independent(self.distribution_class(*__a )   ,			1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None   ,			):
         UpperCAmelCase_           =   self._base_distribution(__a )
         if loc is None and scale is None:
              return distr
         else:
              return AffineTransformed(__a   ,			loc=__a   ,			scale=__a   ,			event_dim=self.event_dim )



    @property
    def 					_lowercase						(self    :    Any ):
         return () if self.dim == 1 else (self.dim,)



    @property
    def 					_lowercase						(self    :    Dict ):
         return len(self.event_shape )



    @property
    def 					_lowercase						(self    :    Tuple ):
         return 0.0



    def 					_lowercase						(self    :    List[str]   ,			__a    :    int ):
         return ParameterProjection(
             in_features=__a   ,			args_dim=self.args_dim   ,			domain_map=LambdaLayer(self.domain_map )   ,			)



    def 					_lowercase						(self    :    Optional[int]   ,			*__a    :    torch.Tensor ):
         raise NotImplementedError()






    @staticmethod
    def 					_lowercase						(__a    :    torch.Tensor ):
         return (x + torch.sqrt(torch.square(__a ) + 4.0 )) / 2.0




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"df": 1, "loc": 1, "scale": 1}
    a__       :						type          =  StudentT



    @classmethod
    def 					_lowercase						(cls    :    Union[str, Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         UpperCAmelCase_           =   2.0 + cls.squareplus(__a )
         return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"loc": 1, "scale": 1}
    a__       :						type          =  Normal



    @classmethod
    def 					_lowercase						(cls    :    Tuple   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         return loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"total_count": 1, "logits": 1}
    a__       :						type          =  NegativeBinomial



    @classmethod
    def 					_lowercase						(cls    :    Optional[Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a )
         return total_count.squeeze(-1 ), logits.squeeze(-1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args
         if self.dim == 1:
              return self.distribution_class(total_count=__a   ,			logits=__a )
         else:
              return Independent(self.distribution_class(total_count=__a   ,			logits=__a )   ,			1 )






    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    int   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args

         if scale is not None:
              # See scaling property of Gamma.
              logits += scale.log()

         return self._base_distribution((total_count, logits) )




