




from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
 import pyximport

 pyximport.install(setup_args={'include_dirs': np.get_include()})
 from . import algos_graphormer  # noqa E402
def 				SCREAMING_SNAKE_CASE_				(							__A       :				List[str] ,  __A       :				Dict = 5_12						)				->				int:
   _SCREAMING_SNAKE_CASE =   x.shape[1] if len(x.shape						) > 1 else 1
   _SCREAMING_SNAKE_CASE =   1 + np.arange(0 ,  feature_num * offset ,  __A ,  dtype=np.intaa						)
   _SCREAMING_SNAKE_CASE =   x + feature_offset
   return x
def 				SCREAMING_SNAKE_CASE_				(							__A       :				str ,  __A       :				int=True						)				->				Optional[Any]:
   requires_backends(__A ,  ["cython"]						)

   if keep_features and "edge_attr" in item.keys():  # edge_attr
      _SCREAMING_SNAKE_CASE =   np.asarray(item["edge_attr"] ,  dtype=np.intaa						)
   else:
      _SCREAMING_SNAKE_CASE =   np.ones((len(item["edge_index"][0]						), 1) ,  dtype=np.intaa						)  # same embedding for all

   if keep_features and "node_feat" in item.keys():  # input_nodes
      _SCREAMING_SNAKE_CASE =   np.asarray(item["node_feat"] ,  dtype=np.intaa						)
   else:
      _SCREAMING_SNAKE_CASE =   np.ones((item["num_nodes"], 1) ,  dtype=np.intaa						)  # same embedding for all

   _SCREAMING_SNAKE_CASE =   np.asarray(item["edge_index"] ,  dtype=np.intaa						)

   _SCREAMING_SNAKE_CASE =   convert_to_single_emb(__A						) + 1
   _SCREAMING_SNAKE_CASE =   item["""num_nodes"""]

   if len(edge_attr.shape						) == 1:
      _SCREAMING_SNAKE_CASE =   edge_attr[:, None]
   _SCREAMING_SNAKE_CASE =   np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,  dtype=np.intaa						)
   _SCREAMING_SNAKE_CASE =   convert_to_single_emb(__A						) + 1

   # node adj matrix [num_nodes, num_nodes] bool
   _SCREAMING_SNAKE_CASE =   np.zeros([num_nodes, num_nodes] ,  dtype=__A						)
   _SCREAMING_SNAKE_CASE =   True

   _SCREAMING_SNAKE_CASE =   algos_graphormer.floyd_warshall(__A						)
   _SCREAMING_SNAKE_CASE =   np.amax(__A						)

   _SCREAMING_SNAKE_CASE =   algos_graphormer.gen_edge_input(__A ,  __A ,  __A						)
   _SCREAMING_SNAKE_CASE =   np.zeros([num_nodes + 1, num_nodes + 1] ,  dtype=np.single						)  # with graph token

   # combine
   _SCREAMING_SNAKE_CASE =   input_nodes + 1  # we shift all indices by one for padding
   _SCREAMING_SNAKE_CASE =   attn_bias
   _SCREAMING_SNAKE_CASE =   attn_edge_type
   _SCREAMING_SNAKE_CASE =   shortest_path_result.astype(np.intaa						) + 1  # we shift all indices by one for padding
   _SCREAMING_SNAKE_CASE =   np.sum(__A ,  axis=1						).reshape(-1						) + 1  # we shift all indices by one for padding
   _SCREAMING_SNAKE_CASE =   item["""in_degree"""]  # for undirected graph
   _SCREAMING_SNAKE_CASE =   input_edges + 1  # we shift all indices by one for padding
   if "labels" not in item:
      _SCREAMING_SNAKE_CASE =   item["""y"""]

   return item







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			Optional[int]						,   __lowerCamelCase      :			Union[str, Any]=2_0						,   __lowerCamelCase      :			int=False					):

      """simple docstring"""

      if not is_cython_available():
         raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

      _SCREAMING_SNAKE_CASE =   spatial_pos_max
      _SCREAMING_SNAKE_CASE =   on_the_fly_processing



   def __call__(					self      :			Union[str, Any]						,   __lowerCamelCase      :			Optional[int]					):

      """simple docstring"""

      if self.on_the_fly_processing:
         _SCREAMING_SNAKE_CASE =   [preprocess_item(_lowerCamelCase					) for i in features]

      if not isinstance(features[0]						,   _lowerCamelCase					):
         _SCREAMING_SNAKE_CASE =   [vars(_lowerCamelCase					) for f in features]
      _SCREAMING_SNAKE_CASE =   {}

      _SCREAMING_SNAKE_CASE =   max(len(i["input_nodes"]					) for i in features					)
      _SCREAMING_SNAKE_CASE =   len(features[0]["input_nodes"][0]					)
      _SCREAMING_SNAKE_CASE =   len(features[0]["attn_edge_type"][0][0]					)
      _SCREAMING_SNAKE_CASE =   max(len(i["input_edges"][0][0]					) for i in features					)
      _SCREAMING_SNAKE_CASE =   len(features[0]["input_edges"][0][0][0]					)
      _SCREAMING_SNAKE_CASE =   len(_lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.zeros(_lowerCamelCase						,   max_node_num + 1						,   max_node_num + 1						,   dtype=torch.float					)
      _SCREAMING_SNAKE_CASE =   torch.zeros(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   dtype=torch.long					)
      _SCREAMING_SNAKE_CASE =   torch.zeros(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   dtype=torch.long					)
      _SCREAMING_SNAKE_CASE =   torch.zeros(_lowerCamelCase						,   _lowerCamelCase						,   dtype=torch.long					)
      _SCREAMING_SNAKE_CASE =   torch.zeros(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   dtype=torch.long					)
      _SCREAMING_SNAKE_CASE =   torch.zeros(
          _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   dtype=torch.long					)

      for ix, f in enumerate(_lowerCamelCase					):
         for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
            _SCREAMING_SNAKE_CASE =   torch.tensor(f[k]					)

         if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
            _SCREAMING_SNAKE_CASE =   float("-inf"					)

         _SCREAMING_SNAKE_CASE =   f["""attn_bias"""]
         _SCREAMING_SNAKE_CASE =   f[
             """attn_edge_type"""
         ]
         _SCREAMING_SNAKE_CASE =   f["""spatial_pos"""]
         _SCREAMING_SNAKE_CASE =   f["""in_degree"""]
         _SCREAMING_SNAKE_CASE =   f["""input_nodes"""]
         _SCREAMING_SNAKE_CASE =   f["""input_edges"""]

      _SCREAMING_SNAKE_CASE =   batch["""in_degree"""]

      _SCREAMING_SNAKE_CASE =   features[0]["""labels"""]
      if len(_lowerCamelCase					) == 1:  # one task
         if isinstance(sample[0]						,   _lowerCamelCase					):  # regression
            _SCREAMING_SNAKE_CASE =   torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
         else:  # binary classification
            _SCREAMING_SNAKE_CASE =   torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
      else:  # multi task classification, left to float to keep the NaNs
         _SCREAMING_SNAKE_CASE =   torch.from_numpy(np.stack([i["labels"] for i in features]						,   axis=0					)					)

      return batch







'''simple docstring'''



import unittest

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

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 (
     RemBertConfig,
     RemBertForCausalLM,
     RemBertForMaskedLM,
     RemBertForMultipleChoice,
     RemBertForQuestionAnswering,
     RemBertForSequenceClassification,
     RemBertForTokenClassification,
     RemBertModel,
 )
 from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			Any						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Union[str, Any]=1_3						,   __lowerCamelCase      :			str=7						,   __lowerCamelCase      :			Any=True						,   __lowerCamelCase      :			List[Any]=True						,   __lowerCamelCase      :			str=True						,   __lowerCamelCase      :			Union[str, Any]=True						,   __lowerCamelCase      :			List[Any]=9_9						,   __lowerCamelCase      :			List[str]=3_2						,   __lowerCamelCase      :			List[Any]=1_8						,   __lowerCamelCase      :			List[Any]=4_3						,   __lowerCamelCase      :			int=5						,   __lowerCamelCase      :			Any=4						,   __lowerCamelCase      :			List[str]=3_7						,   __lowerCamelCase      :			Any="gelu"						,   __lowerCamelCase      :			List[str]=0.1						,   __lowerCamelCase      :			List[Any]=0.1						,   __lowerCamelCase      :			Union[str, Any]=5_1_2						,   __lowerCamelCase      :			int=1_6						,   __lowerCamelCase      :			str=2						,   __lowerCamelCase      :			Optional[int]=0.0_2						,   __lowerCamelCase      :			Dict=3						,   __lowerCamelCase      :			str=4						,   __lowerCamelCase      :			Union[str, Any]=None						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   parent
      _SCREAMING_SNAKE_CASE =   batch_size
      _SCREAMING_SNAKE_CASE =   seq_length
      _SCREAMING_SNAKE_CASE =   is_training
      _SCREAMING_SNAKE_CASE =   use_input_mask
      _SCREAMING_SNAKE_CASE =   use_token_type_ids
      _SCREAMING_SNAKE_CASE =   use_labels
      _SCREAMING_SNAKE_CASE =   vocab_size
      _SCREAMING_SNAKE_CASE =   hidden_size
      _SCREAMING_SNAKE_CASE =   input_embedding_size
      _SCREAMING_SNAKE_CASE =   output_embedding_size
      _SCREAMING_SNAKE_CASE =   num_hidden_layers
      _SCREAMING_SNAKE_CASE =   num_attention_heads
      _SCREAMING_SNAKE_CASE =   intermediate_size
      _SCREAMING_SNAKE_CASE =   hidden_act
      _SCREAMING_SNAKE_CASE =   hidden_dropout_prob
      _SCREAMING_SNAKE_CASE =   attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE =   max_position_embeddings
      _SCREAMING_SNAKE_CASE =   type_vocab_size
      _SCREAMING_SNAKE_CASE =   type_sequence_label_size
      _SCREAMING_SNAKE_CASE =   initializer_range
      _SCREAMING_SNAKE_CASE =   num_labels
      _SCREAMING_SNAKE_CASE =   num_choices
      _SCREAMING_SNAKE_CASE =   scope



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size					)

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

      _SCREAMING_SNAKE_CASE =   None
      if self.use_token_type_ids:
         _SCREAMING_SNAKE_CASE =   ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size					)

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

      _SCREAMING_SNAKE_CASE =   RemBertConfig(
          vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   input_embedding_size=self.input_embedding_size						,   output_embedding_size=self.output_embedding_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=__lowerCamelCase						,   initializer_range=self.initializer_range						,   )

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



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   self.prepare_config_and_inputs()

      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertModel(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)					)



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   RemBertModel(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)					)



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			List[str]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForCausalLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForMaskedLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   RemBertForCausalLM(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()

      # first forward pass
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   use_cache=__lowerCamelCase						,   )
      _SCREAMING_SNAKE_CASE =   outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _SCREAMING_SNAKE_CASE =   ids_tensor((self.batch_size, 3)						,   config.vocab_size					)
      _SCREAMING_SNAKE_CASE =   ids_tensor((self.batch_size, 3)						,   vocab_size=2					)

      # append to next input_ids and
      _SCREAMING_SNAKE_CASE =   torch.cat([input_ids, next_tokens]						,   dim=-1					)
      _SCREAMING_SNAKE_CASE =   torch.cat([input_mask, next_mask]						,   dim=-1					)

      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase						,   )["hidden_states"][0]
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   encoder_hidden_states=__lowerCamelCase						,   encoder_attention_mask=__lowerCamelCase						,   past_key_values=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase						,   )["hidden_states"][0]

      # select random slice
      _SCREAMING_SNAKE_CASE =   ids_tensor((1,)						,   output_from_past.shape[-1]					).item()
      _SCREAMING_SNAKE_CASE =   output_from_no_past[:, -3:, random_slice_idx].detach()
      _SCREAMING_SNAKE_CASE =   output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__lowerCamelCase						,   __lowerCamelCase						,   atol=1e-3					)					)



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertForQuestionAnswering(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   start_positions=__lowerCamelCase						,   end_positions=__lowerCamelCase						,   )
      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 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   RemBertForSequenceClassification(__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Tuple						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_labels
      _SCREAMING_SNAKE_CASE =   RemBertForTokenClassification(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase					)
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Union[str, Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.num_choices
      _SCREAMING_SNAKE_CASE =   RemBertForMultipleChoice(config=__lowerCamelCase					)
      model.to(__lowerCamelCase					)
      model.eval()
      _SCREAMING_SNAKE_CASE =   input_ids.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   token_type_ids.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   input_mask.unsqueeze(1					).expand(-1						,   self.num_choices						,   -1					).contiguous()
      _SCREAMING_SNAKE_CASE =   model(
          __lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   labels=__lowerCamelCase						,   )
      self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_choices)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.prepare_config_and_inputs()
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   config_and_inputs
      _SCREAMING_SNAKE_CASE =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_torch
class 	lowercase_					(				A  ,						A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		(
       (
           RemBertModel,
           RemBertForMaskedLM,
           RemBertForCausalLM,
           RemBertForMultipleChoice,
           RemBertForQuestionAnswering,
           RemBertForSequenceClassification,
           RemBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase_       						=		(RemBertForCausalLM,) if is_torch_available() else ()
   lowerCamelCase_       						=		(
       {
           '''feature-extraction''': RemBertModel,
           '''fill-mask''': RemBertForMaskedLM,
           '''question-answering''': RemBertForQuestionAnswering,
           '''text-classification''': RemBertForSequenceClassification,
           '''text-generation''': RemBertForCausalLM,
           '''token-classification''': RemBertForTokenClassification,
           '''zero-shot''': RemBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   RemBertModelTester(self					)
      _SCREAMING_SNAKE_CASE =   ConfigTester(self						,   config_class=__lowerCamelCase						,   hidden_size=3_7					)



   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      self.config_tester.run_common_tests()



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _SCREAMING_SNAKE_CASE =   type
         self.model_tester.create_and_check_model(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      # This regression test was failing with PyTorch < 1.3
      (
          (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), (
          _SCREAMING_SNAKE_CASE
      ), 
      ) =   self.model_tester.prepare_config_and_inputs_for_decoder()

      _SCREAMING_SNAKE_CASE =   None

      self.model_tester.create_and_check_model_as_decoder(
          __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   )



   @slow
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _SCREAMING_SNAKE_CASE =   RemBertModel.from_pretrained(__lowerCamelCase					)
         self.assertIsNotNone(__lowerCamelCase					)







@require_torch
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      # Test exact values at the last hidden layer
      _SCREAMING_SNAKE_CASE =   RemBertModel.from_pretrained("google/rembert"					)
      _SCREAMING_SNAKE_CASE =   torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]					)
      _SCREAMING_SNAKE_CASE =   torch.tensor([[0, 0, 0, 1, 1]]					)
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE =   model(__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   output_hidden_states=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   1_1_5_2

      _SCREAMING_SNAKE_CASE =   torch.Size((1, 5, hidden_size)					)
      self.assertEqual(output["last_hidden_state"].shape						,   __lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.tensor(
          [
              [
                  [0.0_7_5_4, -0.2_0_2_2, 0.1_9_0_4],
                  [-0.3_3_5_4, -0.3_6_9_2, -0.4_7_9_1],
                  [-0.2_3_1_4, -0.6_7_2_9, -0.0_7_4_9],
                  [-0.0_3_9_6, -0.3_1_0_5, -0.4_2_3_4],
                  [-0.1_5_7_1, -0.0_5_2_5, 0.5_3_5_3],
              ]
          ]					)

      # Running on the original tf implementation gives slightly different results here.
      # Not clear why this variations is present
      # TODO: Find reason for discrepancy
      # expected_original_implementation = [[
      #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
      #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
      #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
      #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
      #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
      # ]]

      self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3]						,   __lowerCamelCase						,   atol=1e-4					)					)

