
'''simple docstring'''



import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


UpperCamelCase__ :						str    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Any    							=					[
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]

class 				_a			(A__):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					RetriBertConfig
       SCREAMING_SNAKE_CASE =					None
       SCREAMING_SNAKE_CASE =					'retribert'






       def     UpperCamelCase			(							self    ,							A__ )      ->     Optional[Any]:
          if isinstance(snake_case_    ,							nn.Linear ):
             module.weight.data.normal_(mean=0.0    ,							std=self.config.initializer_range )
             if module.bias is not None:
                module.bias.data.zero_()
          elif isinstance(snake_case_    ,							nn.Embedding ):
             module.weight.data.normal_(mean=0.0    ,							std=self.config.initializer_range )
             if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
          elif isinstance(snake_case_    ,							nn.LayerNorm ):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0 )




UpperCamelCase__ :						List[str]    							=					R"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

@add_start_docstrings(
    'Bert Based model to embed queries or document for document retrieval.'					,   A__					,   )
class 				_a			(A__):





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     None:
          super().__init__(snake_case_ )
          _SCREAMING_SNAKE_CASE       	=			config.projection_dim

          _SCREAMING_SNAKE_CASE       	=			BertModel(snake_case_ )
          _SCREAMING_SNAKE_CASE       	=			None if config.share_encoders else BertModel(snake_case_ )
          _SCREAMING_SNAKE_CASE       	=			nn.Dropout(config.hidden_dropout_prob )
          _SCREAMING_SNAKE_CASE       	=			nn.Linear(config.hidden_size    ,							config.projection_dim    ,							bias=snake_case_ )
          _SCREAMING_SNAKE_CASE       	=			nn.Linear(config.hidden_size    ,							config.projection_dim    ,							bias=snake_case_ )

          _SCREAMING_SNAKE_CASE       	=			nn.CrossEntropyLoss(reduction="""mean""" )

          # Initialize weights and apply final processing
          self.post_init()






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__=-1    ,							)      ->     Tuple:
          # reproduces BERT forward pass with checkpointing
          if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
             return sent_encoder(snake_case_    ,							attention_mask=snake_case_ )[1]
          else:
             # prepare implicit variables
             _SCREAMING_SNAKE_CASE       	=			input_ids.device
             _SCREAMING_SNAKE_CASE       	=			input_ids.size()
             _SCREAMING_SNAKE_CASE       	=			torch.zeros(snake_case_    ,							dtype=torch.long    ,							device=snake_case_ )
             _SCREAMING_SNAKE_CASE       	=			[None] * sent_encoder.config.num_hidden_layers
             _SCREAMING_SNAKE_CASE       	=			sent_encoder.get_extended_attention_mask(
                 snake_case_    ,							snake_case_ )

             # define function for checkpointing
             def partial_encode(*A__ ):
                _SCREAMING_SNAKE_CASE       	=			sent_encoder.encoder(
                    inputs[0]    ,							attention_mask=inputs[1]    ,							head_mask=snake_case_    ,							)
                _SCREAMING_SNAKE_CASE       	=			encoder_outputs[0]
                _SCREAMING_SNAKE_CASE       	=			sent_encoder.pooler(snake_case_ )
                return pooled_output

             # run embedding layer on everything at once
             _SCREAMING_SNAKE_CASE       	=			sent_encoder.embeddings(
                 input_ids=snake_case_    ,							position_ids=snake_case_    ,							token_type_ids=snake_case_    ,							inputs_embeds=snake_case_ )
             # run encoding and pooling on one mini-batch at a time
             _SCREAMING_SNAKE_CASE       	=			[]
             for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size ) ):
                _SCREAMING_SNAKE_CASE       	=			embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                _SCREAMING_SNAKE_CASE       	=			extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
                _SCREAMING_SNAKE_CASE       	=			checkpoint.checkpoint(snake_case_    ,							snake_case_    ,							snake_case_ )
                pooled_output_list.append(snake_case_ )
             return torch.cat(snake_case_    ,							dim=0 )






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None    ,							A__=-1    ,							)      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.embed_sentences_checkpointed(
              snake_case_    ,							snake_case_    ,							self.bert_query    ,							snake_case_    ,							)
          return self.project_query(snake_case_ )






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None    ,							A__=-1    ,							)      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.embed_sentences_checkpointed(
              snake_case_    ,							snake_case_    ,							self.bert_query if self.bert_doc is None else self.bert_doc    ,							snake_case_    ,							)
          return self.project_doc(snake_case_ )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ = -1    ,							)      ->     torch.FloatTensor:
          _SCREAMING_SNAKE_CASE       	=			input_ids_query.device
          _SCREAMING_SNAKE_CASE       	=			self.embed_questions(snake_case_    ,							snake_case_    ,							snake_case_ )
          _SCREAMING_SNAKE_CASE       	=			self.embed_answers(snake_case_    ,							snake_case_    ,							snake_case_ )
          _SCREAMING_SNAKE_CASE       	=			torch.mm(snake_case_    ,							a_reps.t() )
          _SCREAMING_SNAKE_CASE       	=			self.ce_loss(snake_case_    ,							torch.arange(compare_scores.shape[1] ).to(snake_case_ ) )
          _SCREAMING_SNAKE_CASE       	=			self.ce_loss(compare_scores.t()    ,							torch.arange(compare_scores.shape[0] ).to(snake_case_ ) )
          _SCREAMING_SNAKE_CASE       	=			(loss_qa + loss_aq) / 2
          return loss








'''simple docstring'''



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(DDIMScheduler,)
       SCREAMING_SNAKE_CASE =					(('eta', 0.0), ('num_inference_steps', 50))






       def     UpperCamelCase			(							self    ,							**A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			{
              """num_train_timesteps""": 10_00,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """clip_sample""": True,
          }

          config.update(**A__ )
          return config






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(**A__ )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			10, 0.0

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter

          scheduler.set_timesteps(A__ )

          for t in scheduler.timesteps:
             _SCREAMING_SNAKE_CASE       	=			model(A__    ,							A__ )
             _SCREAMING_SNAKE_CASE       	=			scheduler.step(A__    ,							A__    ,							A__    ,							A__ ).prev_sample

          return sample






       def     UpperCamelCase			(							self )      ->     str:
          for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A__ )

          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(steps_offset=1 )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,							torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2] ):
             self.check_over_configs(beta_start=A__    ,							beta_end=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          self.check_over_configs(thresholding=A__ )
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=A__    ,							prediction_type=A__    ,							sample_max_value=A__    ,							)






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          for t in [1, 10, 49]:
             self.check_over_forward(time_step=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for t, num_inference_steps in zip([1, 10, 50]    ,							[10, 50, 5_00] ):
             self.check_over_forward(time_step=A__    ,							num_inference_steps=A__ )






       def     UpperCamelCase			(							self )      ->     str:
          for t, eta in zip([1, 10, 49]    ,							[0.0, 0.5, 1.0] ):
             self.check_over_forward(time_step=A__    ,							eta=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,							4_00 ) - 0.1_4771 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,							9_60 ) - 0.3_2460 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,							4_86 ) - 0.0_0979 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,							9_98 ) - 0.02 ) ) < 1E-5






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop()

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1E-2
          assert abs(result_mean.item() - 0.22_3967 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(prediction_type="""v_prediction""" )

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1E-2
          assert abs(result_mean.item() - 0.0684 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     List[Any]:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1E-2
          assert abs(result_mean.item() - 0.1951 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     Any:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1E-2
          assert abs(result_mean.item() - 0.1941 ) < 1E-3






