


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase									=				logging.get_logger(__name__)

__lowerCAmelCase									=				{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							int:
       for attribute in key.split('.'	):
              lowercase__:  Optional[Any]         =  getattr(snake_case     ,				snake_case	)

       if weight_type is not None:
              lowercase__:  Union[str, Any]         =  getattr(snake_case     ,				snake_case	).shape
       else:
              lowercase__:  Optional[Any]         =  hf_pointer.shape

       assert hf_shape == value.shape, (
           f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
           f' {value.shape} for {full_name}'
       )

       if weight_type == "weight":
              lowercase__:  Optional[Any]         =  value
       elif weight_type == "weight_g":
              lowercase__:  Optional[Any]         =  value
       elif weight_type == "weight_v":
              lowercase__:  Union[str, Any]         =  value
       elif weight_type == "bias":
              lowercase__:  List[Any]         =  value
       else:
              lowercase__:  List[Any]         =  value

       logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'	)




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case	)    ->							Optional[int]:
       lowercase__:  List[Any]         =  []
       lowercase__:  List[Any]         =  fairseq_model.state_dict()

       lowercase__:  int         =  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__:  Dict         =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                     lowercase__:  Optional[Any]         =  True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__:  Union[str, Any]         =  'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                            if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
                                   lowercase__:  str         =  True
                                   if "*" in mapped_key:
                                          lowercase__:  Union[str, Any]         =  name.split(snake_case	)[0].split('.'	)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          lowercase__:  Union[str, Any]         =  mapped_key.replace('*'     ,				snake_case	)
                                   if "weight_g" in name:
                                          lowercase__:  Any         =  'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__:  str         =  'weight_v'
                                   elif "weight" in name:
                                          lowercase__:  List[str]         =  'weight'
                                   elif "bias" in name:
                                          lowercase__:  Optional[int]         =  'bias'
                                   else:
                                          lowercase__:  Dict         =  None
                                   set_recursively(snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)
                            continue
              if not is_used:
                     unused_weights.append(snake_case	)

       logger.warning(f'Unused weights: {unused_weights}'	)




def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							Optional[Any]:
       lowercase__:  Optional[int]         =  full_name.split('conv_layers.'	)[-1]
       lowercase__:  Optional[Any]         =  name.split('.'	)
       lowercase__:  int         =  int(items[0]	)
       lowercase__:  Any         =  int(items[1]	)

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         f'{full_name} has size {value.shape}, but'
                         f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
                     )
                     lowercase__:  str         =  value
                     logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'	)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         f'{full_name} has size {value.shape}, but'
                         f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
                     )
                     lowercase__:  Optional[Any]         =  value
                     logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'	)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                         " found."
                     )
                     lowercase__:  List[Any]         =  value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         f'{full_name} has size {value.shape}, but'
                         f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
                     )
                     lowercase__:  Optional[Any]         =  value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
       else:
              unused_weights.append(snake_case	)




def   snake_case_	(    snake_case     ,				snake_case	)    ->							Optional[int]:
       lowercase__:  List[Any]         =  SEWDConfig()
       if is_finetuned:
              lowercase__:  Any         =  model.wav_encoder.wav_model.cfg
       else:
              lowercase__:  Optional[int]         =  model.cfg

       lowercase__:  List[Any]         =  fs_config.conv_bias
       lowercase__:  Optional[Any]         =  eval(fs_config.conv_feature_layers	)
       lowercase__:  Optional[int]         =  [x[0] for x in conv_layers]
       lowercase__:  Tuple         =  [x[1] for x in conv_layers]
       lowercase__:  Any         =  [x[2] for x in conv_layers]
       lowercase__:  Any         =  'gelu'
       lowercase__:  Dict         =  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       lowercase__:  Union[str, Any]         =  0.0
       lowercase__:  Tuple         =  fs_config.activation_fn.name
       lowercase__:  Dict         =  fs_config.encoder_embed_dim
       lowercase__:  Optional[int]         =  0.0_2
       lowercase__:  Optional[Any]         =  fs_config.encoder_ffn_embed_dim
       lowercase__:  Union[str, Any]         =  1e-5
       lowercase__:  Tuple         =  fs_config.encoder_layerdrop
       lowercase__:  Dict         =  fs_config.encoder_attention_heads
       lowercase__:  str         =  fs_config.conv_pos_groups
       lowercase__:  str         =  fs_config.conv_pos
       lowercase__:  str         =  len(snake_case	)
       lowercase__:  Dict         =  fs_config.encoder_layers
       lowercase__:  List[str]         =  fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       lowercase__:  Optional[Any]         =  fs_config.max_position_embeddings
       lowercase__:  List[str]         =  fs_config.position_buckets
       lowercase__:  List[str]         =  fs_config.share_att_key
       lowercase__:  int         =  fs_config.relative_attention
       lowercase__:  Optional[Any]         =  fs_config.position_biased_input
       lowercase__:  List[str]         =  tuple(fs_config.pos_att_type.split('|'	)	)
       lowercase__:  Optional[int]         =  fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              lowercase__:  str         =  model.cfg
              lowercase__:  Tuple         =  fs_config.final_dropout
              lowercase__:  Dict         =  fs_config.layerdrop
       lowercase__:  Dict         =  fs_config.activation_dropout
       lowercase__:  Any         =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowercase__:  int         =  fs_config.attention_dropout
       lowercase__:  str         =  fs_config.dropout_input
       lowercase__:  List[Any]         =  fs_config.dropout
       lowercase__:  List[str]         =  fs_config.mask_channel_length
       lowercase__:  List[str]         =  fs_config.mask_channel_prob
       lowercase__:  Optional[Any]         =  fs_config.mask_length
       lowercase__:  Union[str, Any]         =  fs_config.mask_prob

       lowercase__:  Dict         =  'Wav2Vec2FeatureExtractor'
       lowercase__:  List[str]         =  'Wav2Vec2CTCTokenizer'

       return config




@torch.no_grad()
def   snake_case_	(    snake_case     ,				snake_case     ,				snake_case=None     ,				snake_case=None     ,				snake_case=True	)    ->							int:

       if is_finetuned:
              lowercase__     ,		lowercase__     ,		lowercase__:  int         =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
       else:
              lowercase__     ,		lowercase__     ,		lowercase__:  Dict         =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

       if config_path is not None:
              lowercase__:  Tuple         =  SEWDConfig.from_pretrained(snake_case	)
       else:
              lowercase__:  Optional[int]         =  convert_config(model[0]     ,				snake_case	)
       lowercase__:  List[str]         =  model[0].eval()

       lowercase__:  str         =  True if config.feat_extract_norm == 'layer' else False
       lowercase__:  int         =  WavaVecaFeatureExtractor(
           feature_size=1     ,				sampling_rate=1_60_00     ,				padding_value=0     ,				do_normalize=snake_case     ,				return_attention_mask=snake_case     ,				)

       if is_finetuned:
              if dict_path:
                     lowercase__:  Tuple         =  Dictionary.load(snake_case	)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowercase__:  List[Any]         =  target_dict.pad_index
                     lowercase__:  Union[str, Any]         =  target_dict.bos_index
                     lowercase__:  int         =  target_dict.pad_index
                     lowercase__:  Optional[Any]         =  target_dict.bos_index
                     lowercase__:  Tuple         =  target_dict.eos_index
                     lowercase__:  Dict         =  len(target_dict.symbols	)
                     lowercase__:  Optional[Any]         =  os.path.join(snake_case     ,				'vocab.json'	)
                     if not os.path.isdir(snake_case	):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case	)	)
                            return
                     os.makedirs(snake_case     ,				exist_ok=snake_case	)
                     with open(snake_case     ,				'w'     ,				encoding='utf-8'	) as vocab_handle:
                            json.dump(target_dict.indices     ,				snake_case	)
                     lowercase__:  List[Any]         =  WavaVecaCTCTokenizer(
                         snake_case     ,				unk_token=target_dict.unk_word     ,				pad_token=target_dict.pad_word     ,				bos_token=target_dict.bos_word     ,				eos_token=target_dict.eos_word     ,				word_delimiter_token='|'     ,				do_lower_case=snake_case     ,				)
                     lowercase__:  List[str]         =  WavaVecaProcessor(feature_extractor=snake_case     ,				tokenizer=snake_case	)
                     processor.save_pretrained(snake_case	)

              lowercase__:  int         =  SEWDForCTC(snake_case	)
       else:
              lowercase__:  str         =  SEWDModel(snake_case	)
              feature_extractor.save_pretrained(snake_case	)

       recursively_load_weights(snake_case     ,				snake_case     ,				snake_case	)

       hf_model.save_pretrained(snake_case	)


if __name__ == "__main__":
 __lowerCAmelCase									=				argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 __lowerCAmelCase									=				parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






import unittest

from transformers import ErnieMConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     ErnieMForInformationExtraction,
     ErnieMForMultipleChoice,
     ErnieMForQuestionAnswering,
     ErnieMForSequenceClassification,
     ErnieMForTokenClassification,
     ErnieMModel,
 )
 from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST





class     __a     :
 def __init__(  self							,					lowerCAmelCase__							,					lowerCAmelCase__=13							,					lowerCAmelCase__=7							,					lowerCAmelCase__=True							,					lowerCAmelCase__=True							,					lowerCAmelCase__=True							,					lowerCAmelCase__=99							,					lowerCAmelCase__=32							,					lowerCAmelCase__=5							,					lowerCAmelCase__=4							,					lowerCAmelCase__=37							,					lowerCAmelCase__="gelu"							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=0.1							,					lowerCAmelCase__=512							,					lowerCAmelCase__=16							,					lowerCAmelCase__=2							,					lowerCAmelCase__=0.0_2							,					lowerCAmelCase__=3							,					lowerCAmelCase__=4							,					lowerCAmelCase__=None							,					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Tuple         =  parent
        lowercase__:  Optional[int]         =  batch_size
        lowercase__:  Optional[Any]         =  seq_length
        lowercase__:  Dict         =  is_training
        lowercase__:  List[str]         =  use_input_mask
        lowercase__:  Tuple         =  use_labels
        lowercase__:  List[str]         =  vocab_size
        lowercase__:  Any         =  hidden_size
        lowercase__:  Optional[int]         =  num_hidden_layers
        lowercase__:  str         =  num_attention_heads
        lowercase__:  Any         =  intermediate_size
        lowercase__:  List[str]         =  hidden_act
        lowercase__:  str         =  hidden_dropout_prob
        lowercase__:  Optional[Any]         =  attention_probs_dropout_prob
        lowercase__:  Optional[Any]         =  max_position_embeddings
        lowercase__:  List[str]         =  type_vocab_size
        lowercase__:  List[Any]         =  type_sequence_label_size
        lowercase__:  Any         =  initializer_range
        lowercase__:  str         =  num_labels
        lowercase__:  Tuple         =  num_choices
        lowercase__:  str         =  scope
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  int         =  ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size					)

        lowercase__:  str         =  None
        if self.use_input_mask:
               lowercase__:  Dict         =  random_attention_mask([self.batch_size, self.seq_length]					)

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

        lowercase__:  int         =  self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




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

        lowercase__:  List[str]         =  None
        if self.use_input_mask:
               lowercase__:  Union[str, Any]         =  random_attention_mask([self.batch_size, self.seq_length]					)
        lowercase__:  Any         =  self.get_config()

        return config, input_ids, input_mask
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        return ErnieMConfig(
            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							,					initializer_range=self.initializer_range							,					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							str:




        '''simple docstring'''
        lowercase__:  str         =  ErnieMModel(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  List[Any]         =  model(lowerCAmelCase__							,					return_dict=lowerCAmelCase__					)
        self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ErnieMForQuestionAnswering(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  List[str]         =  model(
            lowerCAmelCase__							,					attention_mask=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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Dict         =  ErnieMForInformationExtraction(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Any         =  torch.ones_like(lowerCAmelCase__							,					dtype=torch.floataa					)
        lowercase__:  Tuple         =  model(
            lowerCAmelCase__							,					attention_mask=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 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.num_labels
        lowercase__:  str         =  ErnieMForSequenceClassification(lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Optional[int]         =  model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  self.num_labels
        lowercase__:  Dict         =  ErnieMForTokenClassification(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        input_ids.to(lowerCAmelCase__					)
        input_mask.to(lowerCAmelCase__					)
        token_labels.to(lowerCAmelCase__					)

        lowercase__:  Optional[Any]         =  model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__					)

        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  Dict         =  self.num_choices
        lowercase__:  Any         =  ErnieMForMultipleChoice(config=lowerCAmelCase__					)
        model.to(lowerCAmelCase__					)
        model.eval()
        lowercase__:  Optional[int]         =  input_ids.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase__:  Any         =  input_mask.unsqueeze(1					).expand(-1							,					self.num_choices							,					-1					).contiguous()
        lowercase__:  Union[str, Any]         =  model(
            lowerCAmelCase__							,					attention_mask=lowerCAmelCase__							,					labels=lowerCAmelCase__							,					)
        self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_choices)					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        lowercase__:  int         =  self.prepare_config_and_inputs()
        (
            (
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		(
            lowercase__
        )     ,		
        ):  Optional[Any]         =  config_and_inputs
        lowercase__:  int         =  {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict





@require_torch
class     __a     (       __UpperCamelCase			,			__UpperCamelCase			,			unittest.TestCase ):
 __lowercase					:				List[str]  	=					(
     (
         ErnieMModel,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowercase					:				List[str]  	=					()
 __lowercase					:				Optional[int]  	=					(
     {
         'feature-extraction': ErnieMModel,
         'question-answering': ErnieMForQuestionAnswering,
         'text-classification': ErnieMForSequenceClassification,
         'token-classification': ErnieMForTokenClassification,
         'zero-shot': ErnieMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowercase					:				Optional[Any]  	=					False
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							int:




        '''simple docstring'''
        if pipeline_test_casse_name == "QAPipelineTests":
               return True

        return False
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  ErnieMModelTester(self					)
        lowercase__:  List[str]         =  ConfigTester(self							,					config_class=lowerCAmelCase__							,					hidden_size=37					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  int         =  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
               lowercase__:  str         =  type
               self.model_tester.create_and_check_model(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Any         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Optional[int]         =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__					)
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Any:




        '''simple docstring'''
        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:  Tuple         =  ErnieMModel.from_pretrained(lowerCAmelCase__					)
               self.assertIsNotNone(lowerCAmelCase__					)





@require_torch
class     __a     (       unittest.TestCase ):
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Optional[Any]         =  ErnieMModel.from_pretrained('susnato/ernie-m-base_pytorch'					)
        model.eval()
        lowercase__:  Union[str, Any]         =  torch.tensor([[0, 1, 2, 3, 4, 5]]					)
        lowercase__:  str         =  model(lowerCAmelCase__					)[0]

        # TODO Replace vocab size
        lowercase__:  Union[str, Any]         =  768

        lowercase__:  Any         =  torch.Size((1, 6, hidden_size)					)
        self.assertEqual(output.shape							,					lowerCAmelCase__					)

        lowercase__:  str         =  torch.tensor(
            [[[-0.0_0_1_2, 0.1_2_4_5, -0.0_2_1_4], [-0.0_7_4_2, 0.0_2_4_4, -0.0_7_7_1], [-0.0_3_3_3, 0.1_1_6_4, -0.1_5_5_4]]]					)

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


