

import unittest

from transformers import MraConfig, 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


if is_torch_available():
  import torch

  from transformers import (
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraModel,
  )
  from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST





class A__    :



 """simple docstring"""







 def __init__(		self :	Any     ,						A_ :	str     ,						A_ :	Dict=2     ,						A_ :	Union[str, Any]=8     ,						A_ :	int=True     ,						A_ :	List[Any]=True     ,						A_ :	List[str]=True     ,						A_ :	Optional[Any]=True     ,						A_ :	Optional[int]=9_9     ,						A_ :	str=1_6     ,						A_ :	Dict=5     ,						A_ :	Tuple=2     ,						A_ :	Any=3_6     ,						A_ :	str="gelu"     ,						A_ :	Tuple=0.0     ,						A_ :	int=0.0     ,						A_ :	Any=5_1_2     ,						A_ :	Union[str, Any]=1_6     ,						A_ :	Tuple=2     ,						A_ :	List[str]=0.02     ,						A_ :	Dict=3     ,						A_ :	Optional[int]=4     ,						A_ :	Union[str, Any]=None     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




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

      _lowerCAmelCase   :					List[Any]        =    None
      if self.use_input_mask:
           _lowerCAmelCase   :					Tuple        =    random_attention_mask([self.batch_size, self.seq_length]       )

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

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

      _lowerCAmelCase   :					int        =    self.get_config()

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

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return MraConfig(
          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    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.get_config()
      _lowerCAmelCase   :					str        =    3_0_0
      return config

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      (
          (
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			
      )   :					int        =    self.prepare_config_and_inputs()

      _lowerCAmelCase   :					List[str]        =    True
      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      _lowerCAmelCase   :					str        =    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    __magic_name__       (		self :	Dict     ,						A_ :	str     ,						A_ :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Tuple     ,						A_ :	List[str]     ,						A_ :	Any     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    MraModel(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[str]        =    model(A_     ,						attention_mask=A_     ,						token_type_ids=A_       )
      _lowerCAmelCase   :					List[Any]        =    model(A_     ,						token_type_ids=A_       )
      _lowerCAmelCase   :					Any        =    model(A_       )
      self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)       )

 def    __magic_name__       (		self :	int     ,						A_ :	Tuple     ,						A_ :	Any     ,						A_ :	Optional[Any]     ,						A_ :	List[Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Dict     ,						A_ :	Optional[Any]     ,						A_ :	str     ,						A_ :	Optional[Any]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    True
      _lowerCAmelCase   :					Optional[Any]        =    MraModel(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					Optional[int]        =    model(
          A_     ,						attention_mask=A_     ,						token_type_ids=A_     ,						encoder_hidden_states=A_     ,						encoder_attention_mask=A_     ,						)
      _lowerCAmelCase   :					Tuple        =    model(
          A_     ,						attention_mask=A_     ,						token_type_ids=A_     ,						encoder_hidden_states=A_     ,						)
      _lowerCAmelCase   :					Any        =    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)       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Optional[int]     ,						A_ :	str     ,						A_ :	Tuple     ,						A_ :	str     ,						A_ :	List[str]     ,						A_ :	Any     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    MraForMaskedLM(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					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.vocab_size)       )

 def    __magic_name__       (		self :	str     ,						A_ :	Optional[int]     ,						A_ :	Union[str, Any]     ,						A_ :	List[str]     ,						A_ :	Any     ,						A_ :	Optional[Any]     ,						A_ :	List[Any]     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    MraForQuestionAnswering(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					List[str]        =    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    __magic_name__       (		self :	List[str]     ,						A_ :	Optional[Any]     ,						A_ :	Tuple     ,						A_ :	Tuple     ,						A_ :	str     ,						A_ :	Any     ,						A_ :	Union[str, Any]     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.num_labels
      _lowerCAmelCase   :					str        =    MraForSequenceClassification(A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					Any        =    model(A_     ,						attention_mask=A_     ,						token_type_ids=A_     ,						labels=A_       )
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_labels)       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	List[str]     ,						A_ :	Optional[int]     ,						A_ :	List[Any]     ,						A_ :	Any     ,						A_ :	Any     ,						A_ :	str     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.num_labels
      _lowerCAmelCase   :					Union[str, Any]        =    MraForTokenClassification(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					Any        =    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    __magic_name__       (		self :	str     ,						A_ :	Any     ,						A_ :	List[str]     ,						A_ :	Optional[Any]     ,						A_ :	Dict     ,						A_ :	Tuple     ,						A_ :	int     ,						A_ :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.num_choices
      _lowerCAmelCase   :					Optional[int]        =    MraForMultipleChoice(config=A_       )
      model.to(A_       )
      model.eval()
      _lowerCAmelCase   :					Optional[int]        =    input_ids.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					Any        =    token_type_ids.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					Dict        =    input_mask.unsqueeze(1       ).expand(-1     ,						self.num_choices     ,						-1       ).contiguous()
      _lowerCAmelCase   :					Optional[int]        =    model(
          A_     ,						attention_mask=A_     ,						token_type_ids=A_     ,						labels=A_     ,						)
      self.parent.assertEqual(result.logits.shape     ,						(self.batch_size, self.num_choices)       )


 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.prepare_config_and_inputs()
      (
          (
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			(
          _lowerCAmelCase
      )      ,			
      )   :					List[Any]        =    config_and_inputs
      _lowerCAmelCase   :					Dict        =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict





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



 """simple docstring"""







 _lowercase     :					int      =						(
     (
         MraModel,
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 _lowercase     :					Optional[Any]      =						False
 _lowercase     :					Optional[int]      =						False
 _lowercase     :					Tuple      =						False
 _lowercase     :					Optional[int]      =						False

 _lowercase     :					Any      =						()

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    MraModelTester(self       )
      _lowerCAmelCase   :					Union[str, Any]        =    ConfigTester(self     ,						config_class=A_     ,						hidden_size=3_7       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _lowerCAmelCase   :					str        =    type
           self.model_tester.create_and_check_model(*A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A_       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A_       )

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






      '''simple docstring'''




      for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					Dict        =    MraModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )


 @unittest.skip(reason="MRA does not output attentions"       )
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return





@require_torch
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @slow
 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    MraModel.from_pretrained("uw-madison/mra-base-512-4"       )
      _lowerCAmelCase   :					List[str]        =    torch.arange(2_5_6       ).unsqueeze(0       )

      with torch.no_grad():
           _lowerCAmelCase   :					Optional[Any]        =    model(A_       )[0]

      _lowerCAmelCase   :					int        =    torch.Size((1, 2_5_6, 7_6_8)       )
      self.assertEqual(output.shape     ,						A_       )

      _lowerCAmelCase   :					Dict        =    torch.tensor(
          [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3]     ,						A_     ,						atol=1E-4       )       )

 @slow
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4"       )
      _lowerCAmelCase   :					int        =    torch.arange(2_5_6       ).unsqueeze(0       )

      with torch.no_grad():
           _lowerCAmelCase   :					Tuple        =    model(A_       )[0]

      _lowerCAmelCase   :					Optional[int]        =    5_0_2_6_5

      _lowerCAmelCase   :					Union[str, Any]        =    torch.Size((1, 2_5_6, vocab_size)       )
      self.assertEqual(output.shape     ,						A_       )

      _lowerCAmelCase   :					Any        =    torch.tensor(
          [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3]     ,						A_     ,						atol=1E-4       )       )


 @slow
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3"       )
      _lowerCAmelCase   :					List[Any]        =    torch.arange(4_0_9_6       ).unsqueeze(0       )

      with torch.no_grad():
           _lowerCAmelCase   :					List[str]        =    model(A_       )[0]

      _lowerCAmelCase   :					Tuple        =    5_0_2_6_5

      _lowerCAmelCase   :					Union[str, Any]        =    torch.Size((1, 4_0_9_6, vocab_size)       )
      self.assertEqual(output.shape     ,						A_       )

      _lowerCAmelCase   :					int        =    torch.tensor(
          [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]       )

      self.assertTrue(torch.allclose(output[:, :3, :3]     ,						A_     ,						atol=1E-4       )       )









# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__UpperCAmelCase    				=     TypeVar('T')





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	bool = True       ):






      '''simple docstring'''




      _lowerCAmelCase   :					dict[T, list[T]]        =    {}  # dictionary of lists
      _lowerCAmelCase   :					List[Any]        =    directed

 def    __magic_name__       (		self :	Any     ,						A_ :	T     ,						A_ :	T       ):






      '''simple docstring'''




      if not self.directed:  # For undirected graphs
           # if both source vertex and destination vertex are both present in the
           # adjacency list, add destination vertex to source vertex list of adjacent
           # vertices and add source vertex to destination vertex list of adjacent
           # vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                self.adj_list[destination_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination vertex
           # to source vertex list of adjacent vertices, then create a new vertex with
           # destination vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					List[Any]        =    [source_vertex]
           # if only destination vertex is present in adjacency list, add source vertex
           # to destination vertex list of adjacent vertices, then create a new vertex
           # with source vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(A_       )
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and assign a list
           # containing the destination vertex as it's first adjacent vertex also
           # create a new vertex with destination vertex as key and assign a list
           # containing the source vertex as it's first adjacent vertex.
           else:
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
                _lowerCAmelCase   :					Dict        =    [source_vertex]
      else:  # For directed graphs
           # if both source vertex and destination vertex are present in adjacency
           # list, add destination vertex to source vertex list of adjacent vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination
           # vertex to source vertex list of adjacent vertices and create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					Tuple        =    []
           # if only destination vertex is present in adjacency list, create a new
           # vertex with source vertex as key and assign a list containing destination
           # vertex as first adjacent vertex
           elif destination_vertex in self.adj_list:
                _lowerCAmelCase   :					Dict        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and a list containing
           # destination vertex as it's first adjacent vertex. Then create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           else:
                _lowerCAmelCase   :					Any        =    [destination_vertex]
                _lowerCAmelCase   :					Union[str, Any]        =    []

      return self


 def __repr__(		self :	List[str]       ):






      '''simple docstring'''




      return pformat(self.adj_list       )






