
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 UpperCAmelCase   :





   '''simple docstring'''


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

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


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  Tuple					=       ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size     )

      _A:  Any					=       None
      if self.use_input_mask:
         _A:  Optional[int]					=       random_attention_mask([self.batch_size, self.seq_length]     )

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

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

      _A:  int					=       self.get_config()

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


   def  __magic_name__				(					self     :							str     ):

      """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=lowerCAmelCase_			,			initializer_range=self.initializer_range			,			)


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  str					=       self.get_config()
      _A:  Optional[int]					=       3_0_0
      return config


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      (
          (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  
      ):  Tuple					=       self.prepare_config_and_inputs()

      _A:  Tuple					=       True
      _A:  Optional[Any]					=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
      _A:  Tuple					=       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     :							str			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							List[Any]     ):

      """simple docstring"""
      _A:  List[Any]					=       MraModel(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  Any					=       model(lowerCAmelCase_			,			attention_mask=lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_     )
      _A:  Dict					=       model(lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_     )
      _A:  Dict					=       model(lowerCAmelCase_     )
      self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)     )


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

      """simple docstring"""
      _A:  List[Any]					=       True
      _A:  Dict					=       MraModel(lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  Union[str, Any]					=       model(
          lowerCAmelCase_			,			attention_mask=lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_			,			encoder_hidden_states=lowerCAmelCase_			,			encoder_attention_mask=lowerCAmelCase_			,			)
      _A:  str					=       model(
          lowerCAmelCase_			,			attention_mask=lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_			,			encoder_hidden_states=lowerCAmelCase_			,			)
      _A:  Optional[Any]					=       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  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      _A:  List[Any]					=       MraForMaskedLM(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  List[str]					=       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  __magic_name__				(					self     :							Any			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Optional[Any]					=       MraForQuestionAnswering(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  str					=       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  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A:  Optional[int]					=       self.num_labels
      _A:  Optional[int]					=       MraForSequenceClassification(lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  Optional[Any]					=       model(lowerCAmelCase_			,			attention_mask=lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_			,			labels=lowerCAmelCase_     )
      self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)     )


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

      """simple docstring"""
      _A:  int					=       self.num_labels
      _A:  List[str]					=       MraForTokenClassification(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  int					=       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  __magic_name__				(					self     :							List[Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Tuple					=       self.num_choices
      _A:  List[str]					=       MraForMultipleChoice(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  int					=       input_ids.unsqueeze(1     ).expand(-1			,			self.num_choices			,			-1     ).contiguous()
      _A:  List[str]					=       token_type_ids.unsqueeze(1     ).expand(-1			,			self.num_choices			,			-1     ).contiguous()
      _A:  List[Any]					=       input_mask.unsqueeze(1     ).expand(-1			,			self.num_choices			,			-1     ).contiguous()
      _A:  Dict					=       model(
          lowerCAmelCase_			,			attention_mask=lowerCAmelCase_			,			token_type_ids=lowerCAmelCase_			,			labels=lowerCAmelCase_			,			)
      self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_choices)     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       self.prepare_config_and_inputs()
      (
          (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  (
          _A
      )			,  
      ):  str					=       config_and_inputs
      _A:  Any					=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Union[str, Any] 				=    (
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __UpperCamelCase		:    Optional[int] 				=    False
   __UpperCamelCase		:    Union[str, Any] 				=    False
   __UpperCamelCase		:    int 				=    False
   __UpperCamelCase		:    List[Any] 				=    False

   __UpperCamelCase		:    Any 				=    ()


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

      """simple docstring"""
      _A:  Tuple					=       MraModelTester(self     )
      _A:  List[str]					=       ConfigTester(self			,			config_class=lowerCAmelCase_			,			hidden_size=3_7     )


   def  __magic_name__				(					self     :							int     ):

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


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  List[Any]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Tuple					=       self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         _A:  List[str]					=       type
         self.model_tester.create_and_check_model(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  List[Any]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  List[Any]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  Any					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  List[str]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_     )


   @slow
   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _A:  int					=       MraModel.from_pretrained(lowerCAmelCase_     )
         self.assertIsNotNone(lowerCAmelCase_     )


   @unittest.skip(reason='''MRA does not output attentions'''     )
   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      return






@require_torch
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


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

      """simple docstring"""
      _A:  Dict					=       MraModel.from_pretrained('''uw-madison/mra-base-512-4'''     )
      _A:  int					=       torch.arange(2_5_6     ).unsqueeze(0     )

      with torch.no_grad():
         _A:  Tuple					=       model(lowerCAmelCase_     )[0]

      _A:  Tuple					=       torch.Size((1, 2_5_6, 7_6_8)     )
      self.assertEqual(output.shape			,			lowerCAmelCase_     )

      _A:  Union[str, Any]					=       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]			,			lowerCAmelCase_			,			atol=1e-4     )     )


   @slow
   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''     )
      _A:  List[str]					=       torch.arange(2_5_6     ).unsqueeze(0     )

      with torch.no_grad():
         _A:  Tuple					=       model(lowerCAmelCase_     )[0]

      _A:  Dict					=       5_0_2_6_5

      _A:  Optional[Any]					=       torch.Size((1, 2_5_6, vocab_size)     )
      self.assertEqual(output.shape			,			lowerCAmelCase_     )

      _A:  Dict					=       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]			,			lowerCAmelCase_			,			atol=1e-4     )     )


   @slow
   def  __magic_name__				(					self     :							int     ):

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

      with torch.no_grad():
         _A:  Tuple					=       model(lowerCAmelCase_     )[0]

      _A:  List[Any]					=       5_0_2_6_5

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

      _A:  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]			,			lowerCAmelCase_			,			atol=1e-4     )     )





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser






def   lowerCamelCase__							(					a=None      )      ->   int:
   _A:  Union[str, Any]					=       argparse.ArgumentParser(add_help=a		,		allow_abbrev=a      )
   # The main config parser
   _A:  str					=       config_command_parser(a      )
   # The subparser to add commands to
   _A:  str					=       config_parser.add_subparsers(title='''subcommands'''		,		dest='''subcommand'''      )

   # Then add other parsers with the parent parser
   default_command_parser(a		,		parents=[parent_parser]      )
   update_command_parser(a		,		parents=[parent_parser]      )

   return config_parser






def   lowerCamelCase__							(					)      ->   Union[str, Any]:
   _A:  Any					=       get_config_parser()
   _A:  Tuple					=       config_parser.parse_args()

   if not hasattr(a		,		'''func'''      ):
      config_parser.print_help()
      exit(1      )

   # Run
   args.func(a      )


if __name__ == "__main__":
   main()



