





import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
    )
    from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST



class 					A__   :





      def __init__( self       ,							A_       ,							A_=13       ,							A_=7       ,							A_=True       ,							A_=True       ,							A_=True       ,							A_=True       ,							A_=99       ,							A_=32       ,							A_=5       ,							A_=4       ,							A_=37       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=128       ,							A_=32       ,							A_=16       ,							A_=2       ,							A_=0.02       ,							A_=3       ,							A_=4       ,							A_=None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   parent
       UpperCamelCase	:					Any       	=   batch_size
       UpperCamelCase	:					Any       	=   seq_length
       UpperCamelCase	:					Union[str, Any]       	=   is_training
       UpperCamelCase	:					Tuple       	=   use_input_mask
       UpperCamelCase	:					List[str]       	=   use_token_type_ids
       UpperCamelCase	:					Any       	=   use_labels
       UpperCamelCase	:					Dict       	=   vocab_size
       UpperCamelCase	:					str       	=   hidden_size
       UpperCamelCase	:					Dict       	=   num_hidden_layers
       UpperCamelCase	:					int       	=   num_attention_heads
       UpperCamelCase	:					List[Any]       	=   intermediate_size
       UpperCamelCase	:					Optional[int]       	=   hidden_act
       UpperCamelCase	:					Tuple       	=   hidden_dropout_prob
       UpperCamelCase	:					str       	=   attention_probs_dropout_prob
       UpperCamelCase	:					Dict       	=   max_position_embeddings
       UpperCamelCase	:					Optional[int]       	=   type_vocab_size
       UpperCamelCase	:					int       	=   type_sequence_label_size
       UpperCamelCase	:					List[Any]       	=   initializer_range
       UpperCamelCase	:					Dict       	=   num_labels
       UpperCamelCase	:					Tuple       	=   num_choices
       UpperCamelCase	:					int       	=   scope





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size   )

       UpperCamelCase	:					List[str]       	=   None
       if self.use_input_mask:
        UpperCamelCase	:					Union[str, Any]       	=   random_attention_mask([self.batch_size, self.seq_length]   )

       UpperCamelCase	:					Optional[Any]       	=   None
       if self.use_token_type_ids:
        UpperCamelCase	:					List[Any]       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.type_vocab_size   )

       UpperCamelCase	:					Tuple       	=   None
       UpperCamelCase	:					List[Any]       	=   None
       UpperCamelCase	:					Tuple       	=   None
       if self.use_labels:
        UpperCamelCase	:					List[str]       	=   ids_tensor([self.batch_size]       ,							self.type_sequence_label_size   )
        UpperCamelCase	:					List[str]       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.num_labels   )
        UpperCamelCase	:					Optional[int]       	=   ids_tensor([self.batch_size]       ,							self.num_choices   )

       UpperCamelCase	:					str       	=   self.get_config()

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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return NezhaConfig(
           vocab_size=self.vocab_size       ,							hidden_size=self.hidden_size       ,							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       ,							is_decoder=__a       ,							initializer_range=self.initializer_range       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       (
           (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,     
       )	:					Union[str, Any]       	=   self.prepare_config_and_inputs()

       UpperCamelCase	:					Dict       	=   True
       UpperCamelCase	:					List[Any]       	=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
       UpperCamelCase	:					Dict       	=   ids_tensor([self.batch_size, self.seq_length]       ,							vocab_size=2   )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   NezhaModel(config=__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					Dict       	=   model(__a       ,							attention_mask=__a       ,							token_type_ids=__a   )
       UpperCamelCase	:					Dict       	=   model(__a       ,							token_type_ids=__a   )
       UpperCamelCase	:					Tuple       	=   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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   True
       UpperCamelCase	:					Optional[int]       	=   NezhaModel(__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					str       	=   model(
           __a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							encoder_hidden_states=__a       ,							encoder_attention_mask=__a       ,							)
       UpperCamelCase	:					str       	=   model(
           __a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							encoder_hidden_states=__a       ,							)
       UpperCamelCase	:					Tuple       	=   model(__a       ,							attention_mask=__a       ,							token_type_ids=__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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   NezhaForMaskedLM(config=__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					str       	=   model(__a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							labels=__a   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.vocab_size)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   NezhaForNextSentencePrediction(config=__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					Union[str, Any]       	=   model(
           __a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							labels=__a       ,							)
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, 2)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   NezhaForPreTraining(config=__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					Optional[Any]       	=   model(
           __a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							labels=__a       ,							next_sentence_label=__a       ,							)
       self.parent.assertEqual(result.prediction_logits.shape       ,							(self.batch_size, self.seq_length, self.vocab_size)   )
       self.parent.assertEqual(result.seq_relationship_logits.shape       ,							(self.batch_size, 2)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   NezhaForQuestionAnswering(config=__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					Any       	=   model(
           __a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							start_positions=__a       ,							end_positions=__a       ,							)
       self.parent.assertEqual(result.start_logits.shape       ,							(self.batch_size, self.seq_length)   )
       self.parent.assertEqual(result.end_logits.shape       ,							(self.batch_size, self.seq_length)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.num_labels
       UpperCamelCase	:					Optional[int]       	=   NezhaForSequenceClassification(__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					List[str]       	=   model(__a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							labels=__a   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.num_labels
       UpperCamelCase	:					List[Any]       	=   NezhaForTokenClassification(config=__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					Tuple       	=   model(__a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							labels=__a   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.num_labels)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   self.num_choices
       UpperCamelCase	:					List[str]       	=   NezhaForMultipleChoice(config=__a   )
       model.to(__a   )
       model.eval()
       UpperCamelCase	:					Union[str, Any]       	=   input_ids.unsqueeze(1   ).expand(-1       ,							self.num_choices       ,							-1   ).contiguous()
       UpperCamelCase	:					List[str]       	=   token_type_ids.unsqueeze(1   ).expand(-1       ,							self.num_choices       ,							-1   ).contiguous()
       UpperCamelCase	:					Dict       	=   input_mask.unsqueeze(1   ).expand(-1       ,							self.num_choices       ,							-1   ).contiguous()
       UpperCamelCase	:					Union[str, Any]       	=   model(
           __a       ,							attention_mask=__a       ,							token_type_ids=__a       ,							labels=__a       ,							)
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_choices)   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,     
       )	:					Optional[int]       	=   config_and_inputs
       UpperCamelCase	:					Union[str, Any]       	=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict



@require_torch
class 					A__   (       UpperCamelCase__       ,   UpperCamelCase__       ,   UpperCamelCase__       ,   unittest.TestCase			):
      _UpperCAmelCase	:Any						    =   (
          (
              NezhaModel,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _UpperCAmelCase	:List[Any]						    =   (
          {
              """feature-extraction""": NezhaModel,
              """fill-mask""": NezhaForMaskedLM,
              """question-answering""": NezhaForQuestionAnswering,
              """text-classification""": NezhaForSequenceClassification,
              """token-classification""": NezhaForTokenClassification,
              """zero-shot""": NezhaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _UpperCAmelCase	:int						    =   True





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_=False   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   super()._prepare_for_class(__a       ,							__a       ,							return_labels=__a   )

       if return_labels:
        if model_class in get_values(__a   ):
         UpperCamelCase	:					str       	=   torch.zeros(
             (self.model_tester.batch_size, self.model_tester.seq_length)       ,							dtype=torch.long       ,							device=__a   )
         UpperCamelCase	:					List[str]       	=   torch.zeros(
             self.model_tester.batch_size       ,							dtype=torch.long       ,							device=__a   )
       return inputs_dict





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   NezhaModelTester(self   )
       UpperCamelCase	:					Optional[Any]       	=   ConfigTester(self       ,							config_class=__a       ,							hidden_size=37   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       (
           (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,    (
           UpperCamelCase 
       )	,     
       )	:					Dict       	=   self.model_tester.prepare_config_and_inputs_for_decoder()

       UpperCamelCase	:					Dict       	=   None

       self.model_tester.create_and_check_model_as_decoder(
           __a       ,							__a       ,							__a       ,							__a       ,							__a       ,							__a       ,							__a       ,							__a       ,							__a       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_next_sequence_prediction(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__a   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__a   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase	:					str       	=   NezhaModel.from_pretrained(__a   )
        self.assertIsNotNone(__a   )





      @slow
      @require_torch_gpu
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	,    UpperCamelCase	:					Dict       	=   self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
        # NezhaForMultipleChoice behaves incorrectly in JIT environments.
        if model_class == NezhaForMultipleChoice:
         return

        UpperCamelCase	:					List[str]       	=   True
        UpperCamelCase	:					List[str]       	=   model_class(config=__a   )

        UpperCamelCase	:					int       	=   self._prepare_for_class(__a       ,							__a   )
        UpperCamelCase	:					List[Any]       	=   torch.jit.trace(
            __a       ,							(inputs_dict["input_ids"].to("cpu"   ), inputs_dict["attention_mask"].to("cpu"   ))   )

        with tempfile.TemporaryDirectory() as tmp:
         torch.jit.save(__a       ,							os.path.join(__a       ,							"bert.pt"   )   )
         UpperCamelCase	:					Optional[int]       	=   torch.jit.load(os.path.join(__a       ,							"bert.pt"   )       ,							map_location=__a   )
         loaded(inputs_dict["input_ids"].to(__a   )       ,							inputs_dict["attention_mask"].to(__a   )   )



@require_torch
class 					A__   (       unittest.TestCase			):





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   NezhaModel.from_pretrained("sijunhe/nezha-cn-base"   )
       UpperCamelCase	:					List[str]       	=   torch.tensor([[0, 1, 2, 3, 4, 5]]   )
       UpperCamelCase	:					Union[str, Any]       	=   torch.tensor([[0, 1, 1, 1, 1, 1]]   )
       with torch.no_grad():
        UpperCamelCase	:					str       	=   model(__a       ,							attention_mask=__a   )[0]
       UpperCamelCase	:					Optional[Any]       	=   torch.Size((1, 6, 768)   )
       self.assertEqual(output.shape       ,							__a   )
       UpperCamelCase	:					Optional[int]       	=   torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]]   )

       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,							__a       ,							atol=1e-4   )   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"   )
       UpperCamelCase	:					List[Any]       	=   torch.tensor([[0, 1, 2, 3, 4, 5]]   )
       UpperCamelCase	:					int       	=   torch.tensor([[1, 1, 1, 1, 1, 1]]   )
       with torch.no_grad():
        UpperCamelCase	:					Union[str, Any]       	=   model(__a       ,							attention_mask=__a   )[0]
       UpperCamelCase	:					Union[str, Any]       	=   torch.Size((1, 6, 2_1128)   )
       self.assertEqual(output.shape       ,							__a   )
       UpperCamelCase	:					Tuple       	=   torch.tensor(
           [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]]   )

       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,							__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) )




