






"""simple docstring"""





import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
    raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
    raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
UpperCamelCase_	:				Any										= logging.get_logger(__name__)

UpperCamelCase_	:				Any										= """The Nymphenburg Palace is a beautiful palace in Munich!"""



def     __lowercase					(    a :				str       ,		a :				str )					->					Union[str, Any]:
      __snake_case      :    str  			={
          '''attention_cell''': '''multi_head''',
          '''num_layers''': 4,
          '''units''': 1_024,
          '''hidden_size''': 768,
          '''max_length''': 512,
          '''num_heads''': 8,
          '''scaled''': True,
          '''dropout''': 0.1,
          '''use_residual''': True,
          '''embed_size''': 1_024,
          '''embed_dropout''': 0.1,
          '''word_embed''': None,
          '''layer_norm_eps''': 1e-5,
          '''token_type_vocab_size''': 2,
      }

      __snake_case      :    Union[str, Any]  			=bort_4_8_768_1024_hparams

      # Let's construct the original Bort model here
      # Taken from official BERT implementation, see:
      # https://github.com/alexa/bort/blob/master/bort/bort.py
      __snake_case      :    Optional[int]  			=BERTEncoder(
          attention_cell=predefined_args['''attention_cell''']       ,		num_layers=predefined_args['''num_layers''']       ,		units=predefined_args['''units''']       ,		hidden_size=predefined_args['''hidden_size''']       ,		max_length=predefined_args['''max_length''']       ,		num_heads=predefined_args['''num_heads''']       ,		scaled=predefined_args['''scaled''']       ,		dropout=predefined_args['''dropout''']       ,		output_attention=a       ,		output_all_encodings=a       ,		use_residual=predefined_args['''use_residual''']       ,		activation=predefined_args.get('''activation'''       ,		'''gelu''' )       ,		layer_norm_eps=predefined_args.get('''layer_norm_eps'''       ,		a )       ,		)

      # Vocab information needs to be fetched first
      # It's the same as RoBERTa, so RobertaTokenizer can be used later
      __snake_case      :    Union[str, Any]  			='''openwebtext_ccnews_stories_books_cased'''

      # Specify download folder to Gluonnlp's vocab
      __snake_case      :    str  			=os.path.join(get_home_dir()       ,		'''models''' )
      __snake_case      :    List[Any]  			=_load_vocab(a       ,		a       ,		a       ,		cls=a )

      __snake_case      :    Optional[int]  			=nlp.model.BERTModel(
          a       ,		len(a )       ,		units=predefined_args['''units''']       ,		embed_size=predefined_args['''embed_size''']       ,		embed_dropout=predefined_args['''embed_dropout''']       ,		word_embed=predefined_args['''word_embed''']       ,		use_pooler=a       ,		use_token_type_embed=a       ,		token_type_vocab_size=predefined_args['''token_type_vocab_size''']       ,		use_classifier=a       ,		use_decoder=a       ,		)

      original_bort.load_parameters(a       ,		cast_dtype=a       ,		ignore_extra=a )
      __snake_case      :    int  			=original_bort._collect_params_with_prefix()

      # Build our config 🤗
      __snake_case      :    str  			={
          '''architectures''': ['''BertForMaskedLM'''],
          '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
          '''hidden_act''': '''gelu''',
          '''hidden_dropout_prob''': predefined_args['''dropout'''],
          '''hidden_size''': predefined_args['''embed_size'''],
          '''initializer_range''': 0.0_2,
          '''intermediate_size''': predefined_args['''hidden_size'''],
          '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
          '''max_position_embeddings''': predefined_args['''max_length'''],
          '''model_type''': '''bort''',
          '''num_attention_heads''': predefined_args['''num_heads'''],
          '''num_hidden_layers''': predefined_args['''num_layers'''],
          '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
          '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
          '''vocab_size''': len(a ),
      }

      __snake_case      :    Optional[Any]  			=BertConfig.from_dict(a )
      __snake_case      :    List[str]  			=BertForMaskedLM(a )
      hf_bort_model.eval()

      # Parameter mapping table (Gluonnlp to Transformers)
      # * denotes layer index
      #
      # | Gluon Parameter                                                | Transformers Parameter
      # | -------------------------------------------------------------- | ----------------------
      # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
      # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
      # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
      # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
      # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
      # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
      # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
      # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
      # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
      # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
      # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
      # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
      # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
      # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
      # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
      # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

      # Helper function to convert MXNET Arrays to PyTorch
      def to_torch(a :				int ) -> nn.Parameter:
            return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )

      # Check param shapes and map new HF param back
      def check_and_map_params(a :				List[Any]       ,		a :				Optional[int] ):
            __snake_case      :    str  			=hf_param.shape

            __snake_case      :    str  			=to_torch(params[gluon_param] )
            __snake_case      :    Tuple  			=gluon_param.shape

            assert (
                shape_hf == shape_gluon
            ), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

            return gluon_param

      __snake_case      :    Optional[int]  			=check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight       ,		'''word_embed.0.weight''' )
      __snake_case      :    Union[str, Any]  			=check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight       ,		'''encoder.position_weight''' )
      __snake_case      :    Union[str, Any]  			=check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias       ,		'''encoder.layer_norm.beta''' )
      __snake_case      :    List[Any]  			=check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.weight       ,		'''encoder.layer_norm.gamma''' )

      # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
      __snake_case      :    str  			=torch.zeros_like(
          hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

      for i in range(hf_bort_config.num_hidden_layers ):
            __snake_case      :    BertLayer  			=hf_bort_model.bert.encoder.layer[i]

            # self attention
            __snake_case      :    BertSelfAttention  			=layer.attention.self

            __snake_case      :    Optional[int]  			=check_and_map_params(
                self_attn.key.bias.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )

            __snake_case      :    List[Any]  			=check_and_map_params(
                self_attn.key.weight.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
            __snake_case      :    Tuple  			=check_and_map_params(
                self_attn.query.bias.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                self_attn.query.weight.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                self_attn.value.bias.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
            __snake_case      :    List[str]  			=check_and_map_params(
                self_attn.value.weight.data       ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )

            # self attention output
            __snake_case      :    BertSelfOutput  			=layer.attention.output

            __snake_case      :    Optional[int]  			=check_and_map_params(
                self_output.dense.bias       ,		f'''encoder.transformer_cells.{i}.proj.bias''' )
            __snake_case      :    Union[str, Any]  			=check_and_map_params(
                self_output.dense.weight       ,		f'''encoder.transformer_cells.{i}.proj.weight''' )
            __snake_case      :    Dict  			=check_and_map_params(
                self_output.LayerNorm.bias       ,		f'''encoder.transformer_cells.{i}.layer_norm.beta''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                self_output.LayerNorm.weight       ,		f'''encoder.transformer_cells.{i}.layer_norm.gamma''' )

            # intermediate
            __snake_case      :    BertIntermediate  			=layer.intermediate

            __snake_case      :    Dict  			=check_and_map_params(
                intermediate.dense.bias       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
            __snake_case      :    List[Any]  			=check_and_map_params(
                intermediate.dense.weight       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )

            # output
            __snake_case      :    BertOutput  			=layer.output

            __snake_case      :    Union[str, Any]  			=check_and_map_params(
                bert_output.dense.bias       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
            __snake_case      :    Optional[Any]  			=check_and_map_params(
                bert_output.dense.weight       ,		f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
            __snake_case      :    str  			=check_and_map_params(
                bert_output.LayerNorm.bias       ,		f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
            __snake_case      :    str  			=check_and_map_params(
                bert_output.LayerNorm.weight       ,		f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma''' )

      # Save space and energy 🎄
      hf_bort_model.half()

      # Compare output of both models
      __snake_case      :    str  			=RobertaTokenizer.from_pretrained('''roberta-base''' )

      __snake_case      :    Dict  			=tokenizer.encode_plus(a )['''input_ids''']

      # Get gluon output
      __snake_case      :    Optional[Any]  			=mx.nd.array([input_ids] )
      __snake_case      :    Dict  			=original_bort(inputs=a       ,		token_types=[] )

      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(a )
      __snake_case      :    List[str]  			=BertModel.from_pretrained(a )
      hf_bort_model.eval()

      __snake_case      :    int  			=tokenizer.encode_plus(a       ,		return_tensors='''pt''' )
      __snake_case      :    Any  			=hf_bort_model(**a )[0]

      __snake_case      :    str  			=output_gluon[0].asnumpy()
      __snake_case      :    int  			=output_hf[0].detach().numpy()

      __snake_case      :    int  			=np.max(np.abs(hf_layer - gluon_layer ) ).item()
      __snake_case      :    Tuple  			=np.allclose(a       ,		a       ,		atol=1e-3 )

      if success:
            print('''✔️ Both model do output the same tensors''' )
      else:
            print('''❌ Both model do **NOT** output the same tensors''' )
            print('''Absolute difference is:'''       ,		a )


if __name__ == "__main__":
    UpperCamelCase_	:				int										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    UpperCamelCase_	:				List[str]										= parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)










"""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      _lowercase :


      def __init__(    self				:	str							,   a				:	List[str]							,   a				:	Any=2							,   a				:	int=8							,   a				:	List[str]=True							,   a				:	Union[str, Any]=True							,   a				:	Union[str, Any]=True							,   a				:	Optional[int]=True							,   a				:	Union[str, Any]=9_9							,   a				:	Dict=1_6							,   a				:	Union[str, Any]=5							,   a				:	int=2							,   a				:	List[str]=3_6							,   a				:	int="gelu"							,   a				:	Optional[int]=0.0							,   a				:	Any=0.0							,   a				:	Optional[Any]=5_1_2							,   a				:	Tuple=1_6							,   a				:	Dict=2							,   a				:	Union[str, Any]=0.0_2							,   a				:	Dict=3							,   a				:	Union[str, Any]=4							,   a				:	Optional[int]=None							,   ):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size						)

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

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

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

            __snake_case      :    List[str]  			=self.get_config()

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


      def _UpperCamelCase						(    self				:	List[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 _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    int  			=self.get_config()
            __snake_case      :    Optional[Any]  			=3_0_0
            return config


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            (
                (
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				
            )      :    Tuple  			=self.prepare_config_and_inputs()

            __snake_case      :    Dict  			=True
            __snake_case      :    Any  			=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
            __snake_case      :    Dict  			=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 _UpperCamelCase						(    self				:	List[str]							,   a				:	List[Any]							,   a				:	int							,   a				:	Optional[int]							,   a				:	List[Any]							,   a				:	Tuple							,   a				:	Tuple							,   a				:	List[Any]						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	str							,   a				:	Tuple							,   a				:	Any							,   a				:	int							,   a				:	Optional[int]							,   a				:	Union[str, Any]							,   a				:	int							,   a				:	Tuple							,   a				:	Optional[Any]							,   a				:	Optional[int]							,   ):





            """simple docstring"""







            __snake_case      :    Any  			=True
            __snake_case      :    Union[str, Any]  			=MraModel(a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Union[str, Any]  			=model(
                a							,   attention_mask=a							,   token_type_ids=a							,   encoder_hidden_states=a							,   encoder_attention_mask=a							,   )
            __snake_case      :    List[Any]  			=model(
                a							,   attention_mask=a							,   token_type_ids=a							,   encoder_hidden_states=a							,   )
            __snake_case      :    List[str]  			=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 _UpperCamelCase						(    self				:	Tuple							,   a				:	Any							,   a				:	List[Any]							,   a				:	int							,   a				:	str							,   a				:	Optional[Any]							,   a				:	List[str]							,   a				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Tuple  			=MraForMaskedLM(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    str  			=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 _UpperCamelCase						(    self				:	List[str]							,   a				:	Optional[int]							,   a				:	int							,   a				:	List[str]							,   a				:	int							,   a				:	Any							,   a				:	Union[str, Any]							,   a				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=MraForQuestionAnswering(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Dict  			=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 _UpperCamelCase						(    self				:	Dict							,   a				:	Dict							,   a				:	Dict							,   a				:	Optional[Any]							,   a				:	int							,   a				:	int							,   a				:	List[str]							,   a				:	Any						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	Tuple							,   a				:	Any							,   a				:	Optional[int]							,   a				:	Any							,   a				:	int							,   a				:	str							,   a				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.num_labels
            __snake_case      :    Optional[int]  			=MraForTokenClassification(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    List[str]  			=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 _UpperCamelCase						(    self				:	str							,   a				:	Tuple							,   a				:	Tuple							,   a				:	Optional[Any]							,   a				:	Dict							,   a				:	Dict							,   a				:	Dict							,   a				:	str						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.num_choices
            __snake_case      :    Optional[int]  			=MraForMultipleChoice(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    List[str]  			=input_ids.unsqueeze(1						).expand(-1							,   self.num_choices							,   -1						).contiguous()
            __snake_case      :    Tuple  			=token_type_ids.unsqueeze(1						).expand(-1							,   self.num_choices							,   -1						).contiguous()
            __snake_case      :    Any  			=input_mask.unsqueeze(1						).expand(-1							,   self.num_choices							,   -1						).contiguous()
            __snake_case      :    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 _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.prepare_config_and_inputs()
            (
                (
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				
            )      :    Optional[int]  			=config_and_inputs
            __snake_case      :    Tuple  			={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
            return config, inputs_dict



@require_torch
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     Union[str, Any]								=   (
          (
              MraModel,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _a   :     int								=   False
      _a   :     Union[str, Any]								=   False
      _a   :     Dict								=   False
      _a   :     Dict								=   False

      _a   :     Any								=   ()


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=MraModelTester(self						)
            __snake_case      :    List[Any]  			=ConfigTester(self							,   config_class=a							,   hidden_size=3_7						)


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            self.config_tester.run_common_tests()


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a						)


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                  __snake_case      :    Any  			=type
                  self.model_tester.create_and_check_model(*a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*a						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*a						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*a						)


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    str  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*a						)


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*a						)


      @slow
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  __snake_case      :    Tuple  			=MraModel.from_pretrained(a						)
                  self.assertIsNotNone(a						)







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





            """simple docstring"""







            return



@require_torch
class      _lowercase (     unittest.TestCase						):


      @slow
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    str  			=MraModel.from_pretrained('''uw-madison/mra-base-512-4'''						)
            __snake_case      :    Optional[int]  			=torch.arange(2_5_6						).unsqueeze(0						)

            with torch.no_grad():
                  __snake_case      :    List[str]  			=model(a						)[0]

            __snake_case      :    Any  			=torch.Size((1, 2_5_6, 7_6_8)						)
            self.assertEqual(output.shape							,   a						)

            __snake_case      :    int  			=torch.tensor(
                [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]						)

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


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





            """simple docstring"""







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

            with torch.no_grad():
                  __snake_case      :    Optional[int]  			=model(a						)[0]

            __snake_case      :    Union[str, Any]  			=5_0_2_6_5

            __snake_case      :    List[str]  			=torch.Size((1, 2_5_6, vocab_size)						)
            self.assertEqual(output.shape							,   a						)

            __snake_case      :    str  			=torch.tensor(
                [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]						)

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







      @slow
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







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

            with torch.no_grad():
                  __snake_case      :    Tuple  			=model(a						)[0]

            __snake_case      :    Optional[int]  			=5_0_2_6_5

            __snake_case      :    Tuple  			=torch.Size((1, 4_0_9_6, vocab_size)						)
            self.assertEqual(output.shape							,   a						)

            __snake_case      :    List[str]  			=torch.tensor(
                [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]						)

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


