
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__a         =   logging.getLogger(__name__)
@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
	a     :Optional[str]       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,		)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether tp freeze the encoder.'}				)
	a     :bool       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Whether  to freeze the embeddings.'}				)

@dataclass
class 					lowercase__:



	"""simple docstring"""




	a     :str       	=      field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
	a     :Optional[str]       	=      field(
	    default='summarization'					,		metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}					,		)
	a     :Optional[int]       	=      field(
	    default=1_024					,		metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=128					,		metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded. '
	            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
	            'during ``evaluate`` and ``predict``.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(
	    default=142					,		metadata={
	        'help': (
	            'The maximum total sequence length for test target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }					,		)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# training examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# validation examples. -1 means use all.'}				)
	a     :Optional[int]       	=      field(default=-1					,		metadata={'help': '# test examples. -1 means use all.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Source language id for translation.'}				)
	a     :Optional[str]       	=      field(default=UpperCAmelCase					,		metadata={'help': 'Target language id for translation.'}				)
	a     :Optional[int]       	=      field(default=UpperCAmelCase					,		metadata={'help': '# num_beams to use for evaluation.'}				)
	a     :bool       	=      field(
	    default=UpperCAmelCase					,		metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}					,		)




def 							a		(				snake_case__:   Dict		,  snake_case__:   Optional[int]		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				logger.info(F'''***** {split} metrics *****'''   )
				for key in sorted(metrics.keys()   ):
								logger.info(F'''  {key} = {metrics[key]}'''   )
				save_json(snake_case__		,  os.path.join(snake_case__		,  F'''{split}_results.json'''   )   )




def 							a		(				):


				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowercase_	    =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

				if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								lowercase_    ,      lowercase_    ,      lowercase_	    =   parser.parse_args_into_dataclasses()

				check_output_dir(snake_case__   )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,  datefmt='''%m/%d/%Y %H:%M:%S'''		,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,  )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,  training_args.local_rank		,  training_args.device		,  training_args.n_gpu		,  bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )		,  training_args.fpaa		,  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank   ):
								transformers.utils.logging.set_verbosity_info()
				logger.info('''Training/evaluation parameters %s'''		,  snake_case__   )

				# Set seed
				set_seed(training_args.seed   )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowercase_	    =   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )

				lowercase_	    =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
				for p in extra_model_params:
								if getattr(snake_case__		,  snake_case__		,  snake_case__   ):
												assert hasattr(snake_case__		,  snake_case__   ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
												setattr(snake_case__		,  snake_case__		,  getattr(snake_case__		,  snake_case__   )   )

				lowercase_	    =   AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,  cache_dir=model_args.cache_dir		,  )
				lowercase_	    =   AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path		,  from_tf='''.ckpt''' in model_args.model_name_or_path		,  config=snake_case__		,  cache_dir=model_args.cache_dir		,  )

				# use task specific params
				use_task_specific_params(snake_case__		,  data_args.task   )

				# set num_beams for evaluation
				if data_args.eval_beams is None:
								lowercase_	    =   model.config.num_beams

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(snake_case__		,  (MBartTokenizer, MBartTokenizerFast)   ):
								assert (
								    data_args.tgt_lang is not None and data_args.src_lang is not None
								), "mBart requires --tgt_lang and --src_lang"
								if isinstance(snake_case__		,  snake_case__   ):
												lowercase_	    =   tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												lowercase_	    =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

				if model_args.freeze_embeds:
								freeze_embeds(snake_case__   )
				if model_args.freeze_encoder:
								freeze_params(model.get_encoder()   )
								assert_all_frozen(model.get_encoder()   )

				lowercase_	    =   SeqaSeqDataset

				# Get datasets
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''train'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_train		,  max_target_length=data_args.max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_train
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''val'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_val		,  max_target_length=data_args.val_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
				    else None
				)
				lowercase_	    =   (
				    dataset_class(
				        snake_case__		,  type_path='''test'''		,  data_dir=data_args.data_dir		,  n_obs=data_args.n_test		,  max_target_length=data_args.test_max_target_length		,  max_source_length=data_args.max_source_length		,  prefix=model.config.prefix or ''''''		,  )
				    if training_args.do_predict
				    else None
				)

				# Initialize our Trainer
				lowercase_	    =   (
				    build_compute_metrics_fn(data_args.task		,  snake_case__   ) if training_args.predict_with_generate else None
				)
				lowercase_	    =   SeqaSeqTrainer(
				    model=snake_case__		,  args=snake_case__		,  data_args=snake_case__		,  train_dataset=snake_case__		,  eval_dataset=snake_case__		,  data_collator=SeqaSeqDataCollator(
				        snake_case__		,  snake_case__		,  model.config.decoder_start_token_id		,  training_args.tpu_num_cores   )		,  compute_metrics=snake_case__		,  tokenizer=snake_case__		,  )

				lowercase_	    =   {}
				# Training
				if training_args.do_train:
								logger.info('''*** Train ***'''   )

								lowercase_	    =   trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
								lowercase_	    =   train_result.metrics
								lowercase_	    =   data_args.n_train

								trainer.save_model()  # this also saves the tokenizer

								if trainer.is_world_process_zero():
												handle_metrics('''train'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
												trainer.state.save_to_json(os.path.join(training_args.output_dir		,  '''trainer_state.json'''   )   )

												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''   )

								lowercase_	    =   trainer.evaluate(metric_key_prefix='''val'''   )
								lowercase_	    =   data_args.n_val
								lowercase_	    =   round(metrics['''val_loss''']		,  4   )

								if trainer.is_world_process_zero():
												handle_metrics('''val'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

				if training_args.do_predict:
								logger.info('''*** Predict ***'''   )

								lowercase_	    =   trainer.predict(test_dataset=snake_case__		,  metric_key_prefix='''test'''   )
								lowercase_	    =   test_output.metrics
								lowercase_	    =   data_args.n_test

								if trainer.is_world_process_zero():
												lowercase_	    =   round(metrics['''test_loss''']		,  4   )
												handle_metrics('''test'''		,  snake_case__		,  training_args.output_dir   )
												all_metrics.update(snake_case__   )

												if training_args.predict_with_generate:
																lowercase_	    =   tokenizer.batch_decode(
																    test_output.predictions		,  skip_special_tokens=snake_case__		,  clean_up_tokenization_spaces=snake_case__   )
																lowercase_	    =   lmap(str.strip		,  snake_case__   )
																write_txt_file(snake_case__		,  os.path.join(training_args.output_dir		,  '''test_generations.txt'''   )   )

				if trainer.is_world_process_zero():
								save_json(snake_case__		,  os.path.join(training_args.output_dir		,  '''all_results.json'''   )   )

				return all_metrics




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()





"""simple docstring"""


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					:


    def __init__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	str=2 ,					__lowerCAmelCase						:	List[Any]=8 ,					__lowerCAmelCase						:	Dict=True ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	str=True ,					__lowerCAmelCase						:	Optional[Any]=99 ,					__lowerCAmelCase						:	Optional[Any]=16 ,					__lowerCAmelCase						:	Optional[int]=5 ,					__lowerCAmelCase						:	Optional[int]=2 ,					__lowerCAmelCase						:	Optional[int]=36 ,					__lowerCAmelCase						:	Any="gelu" ,					__lowerCAmelCase						:	Any=0.0 ,					__lowerCAmelCase						:	int=0.0 ,					__lowerCAmelCase						:	List[str]=512 ,					__lowerCAmelCase						:	List[str]=16 ,					__lowerCAmelCase						:	int=2 ,					__lowerCAmelCase						:	Any=0.02 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	str=4 ,					__lowerCAmelCase						:	Dict=None ,					):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   seq_length
      _UpperCAmelCase    			=   is_training
      _UpperCAmelCase    			=   use_input_mask
      _UpperCAmelCase    			=   use_token_type_ids
      _UpperCAmelCase    			=   use_labels
      _UpperCAmelCase    			=   vocab_size
      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   max_position_embeddings
      _UpperCAmelCase    			=   type_vocab_size
      _UpperCAmelCase    			=   type_sequence_label_size
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   num_labels
      _UpperCAmelCase    			=   num_choices
      _UpperCAmelCase    			=   scope


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size       )

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

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

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

      _UpperCAmelCase    			=   self.get_config()

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


    def   lowerCAmelCase_	(       self						:	Any       ):
      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   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   self.get_config()
      _UpperCAmelCase    			=   300
      return config


    def   lowerCAmelCase_	(       self						:	Any       ):
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   self.prepare_config_and_inputs()

      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      _UpperCAmelCase    			=   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						:	List[str] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Tuple       ):
      _UpperCAmelCase    			=   MraModel(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase       )
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					):
      _UpperCAmelCase    			=   True
      _UpperCAmelCase    			=   MraModel(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					encoder_attention_mask=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   model(
          __lowerCAmelCase ,					attention_mask=__lowerCAmelCase ,					token_type_ids=__lowerCAmelCase ,					encoder_hidden_states=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   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						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   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						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   MraForQuestionAnswering(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   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						:	List[Any] ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForSequenceClassification(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   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						:	List[str] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int]       ):
      _UpperCAmelCase    			=   self.num_labels
      _UpperCAmelCase    			=   MraForTokenClassification(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   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[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.num_choices
      _UpperCAmelCase    			=   MraForMultipleChoice(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   input_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   token_type_ids.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   input_mask.unsqueeze(1       ).expand(-1 ,					self.num_choices ,					-1       ).contiguous()
      _UpperCAmelCase    			=   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						:	Tuple       ):
      _UpperCAmelCase    			=   self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					(
          _UpperCAmelCase
      )			,					
      )    			=   config_and_inputs
      _UpperCAmelCase    			=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	str  				=				(
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _snake_case  :	Dict  				=				False
    _snake_case  :	Any  				=				False
    _snake_case  :	List[str]  				=				False
    _snake_case  :	Tuple  				=				False

    _snake_case  :	int  				=				()


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   MraModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(self ,					config_class=__lowerCAmelCase ,					hidden_size=37       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      self.config_tester.run_common_tests()


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        _UpperCAmelCase    			=   type
        self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase       )


    @slow
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase    			=   MraModel.from_pretrained(__lowerCAmelCase       )
        self.assertIsNotNone(__lowerCAmelCase       )


    @unittest.skip(reason="""MRA does not output attentions"""       )
    def   lowerCAmelCase_	(       self						:	str       ):
      return



@require_torch
class 							a					(		unittest.TestCase ):


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraModel.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   torch.Size((1, 256, 768)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]       )

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


    @slow
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""       )
      _UpperCAmelCase    			=   torch.arange(256       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 256, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]       )

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


    @slow
    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""       )
      _UpperCAmelCase    			=   torch.arange(4096       ).unsqueeze(0       )

      with torch.no_grad():
        _UpperCAmelCase    			=   model(__lowerCAmelCase       )[0]

      _UpperCAmelCase    			=   5_0265

      _UpperCAmelCase    			=   torch.Size((1, 4096, vocab_size)       )
      self.assertEqual(output.shape ,					__lowerCAmelCase       )

      _UpperCAmelCase    			=   torch.tensor(
          [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]       )

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