

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     lowercase_       :



      def __init__(     self :							str     ,   A__ :							Any     ,   A__ :							Optional[Any]=13     ,   A__ :							Union[str, Any]=7     ,   A__ :							Any=True     ,   A__ :							List[str]=True     ,   A__ :							List[str]=True     ,   A__ :							List[Any]=99     ,   A__ :							Tuple=32     ,   A__ :							int=5     ,   A__ :							Union[str, Any]=4     ,   A__ :							Optional[int]=37     ,   A__ :							Optional[Any]="gelu"     ,   A__ :							Union[str, Any]=0.1     ,   A__ :							str=0.1     ,   A__ :							Optional[Any]=512     ,   A__ :							Any=16     ,   A__ :							Union[str, Any]=2     ,   A__ :							Optional[Any]=0.02     ,   A__ :							int=3     ,   A__ :							Any=4     ,   A__ :							List[str]=None     ,   )       ->	Any:
        _snake_case		=				parent
        _snake_case		=				batch_size
        _snake_case		=				seq_length
        _snake_case		=				is_training
        _snake_case		=				use_input_mask
        _snake_case		=				use_labels
        _snake_case		=				vocab_size
        _snake_case		=				hidden_size
        _snake_case		=				num_hidden_layers
        _snake_case		=				num_attention_heads
        _snake_case		=				intermediate_size
        _snake_case		=				hidden_act
        _snake_case		=				hidden_dropout_prob
        _snake_case		=				attention_probs_dropout_prob
        _snake_case		=				max_position_embeddings
        _snake_case		=				type_vocab_size
        _snake_case		=				type_sequence_label_size
        _snake_case		=				initializer_range
        _snake_case		=				num_labels
        _snake_case		=				num_choices
        _snake_case		=				scope



      def 		UpperCamelCase_						(     self :							int						)       ->	int:
        _snake_case		=				ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size						)

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

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

        _snake_case		=				self.get_config()

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



      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	str:
        _snake_case		=				ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size						)

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

        return config, input_ids, input_mask



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	Optional[int]:
        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 		UpperCamelCase_						(     self :							Optional[Any]     ,   A__ :							List[Any]     ,   A__ :							List[str]     ,   A__ :							str     ,   A__ :							Tuple     ,   A__ :							str     ,   A__ :							Union[str, Any]						)       ->	str:
        _snake_case		=				ErnieMModel(config=__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        _snake_case		=				model(__SCREAMING_SNAKE_CASE     ,   return_dict=__SCREAMING_SNAKE_CASE						)
        self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)						)



      def 		UpperCamelCase_						(     self :							Dict     ,   A__ :							Any     ,   A__ :							List[str]     ,   A__ :							Optional[Any]     ,   A__ :							Union[str, Any]     ,   A__ :							List[Any]     ,   A__ :							int						)       ->	List[Any]:
        _snake_case		=				ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        _snake_case		=				model(
            __SCREAMING_SNAKE_CASE     ,   attention_mask=__SCREAMING_SNAKE_CASE     ,   start_positions=__SCREAMING_SNAKE_CASE     ,   end_positions=__SCREAMING_SNAKE_CASE     ,   )
        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 :							Optional[int]     ,   A__ :							Optional[int]     ,   A__ :							Optional[int]     ,   A__ :							List[str]     ,   A__ :							str     ,   A__ :							str     ,   A__ :							str						)       ->	Dict:
        _snake_case		=				ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        _snake_case		=				torch.ones_like(__SCREAMING_SNAKE_CASE     ,   dtype=torch.floataa						)
        _snake_case		=				model(
            __SCREAMING_SNAKE_CASE     ,   attention_mask=__SCREAMING_SNAKE_CASE     ,   start_positions=__SCREAMING_SNAKE_CASE     ,   end_positions=__SCREAMING_SNAKE_CASE     ,   )
        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__ :							Union[str, Any]     ,   A__ :							Optional[Any]     ,   A__ :							Dict     ,   A__ :							int     ,   A__ :							List[Any]     ,   A__ :							Optional[Any]						)       ->	List[str]:
        _snake_case		=				self.num_labels
        _snake_case		=				ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        _snake_case		=				model(__SCREAMING_SNAKE_CASE     ,   attention_mask=__SCREAMING_SNAKE_CASE     ,   labels=__SCREAMING_SNAKE_CASE						)
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)						)



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Any     ,   A__ :							str     ,   A__ :							List[Any]     ,   A__ :							int     ,   A__ :							Tuple     ,   A__ :							Optional[Any]						)       ->	Tuple:
        _snake_case		=				self.num_labels
        _snake_case		=				ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        input_ids.to(__SCREAMING_SNAKE_CASE						)
        input_mask.to(__SCREAMING_SNAKE_CASE						)
        token_labels.to(__SCREAMING_SNAKE_CASE						)

        _snake_case		=				model(__SCREAMING_SNAKE_CASE     ,   attention_mask=__SCREAMING_SNAKE_CASE     ,   labels=__SCREAMING_SNAKE_CASE						)

        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.num_labels)						)



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Tuple     ,   A__ :							int     ,   A__ :							Dict     ,   A__ :							Dict     ,   A__ :							Any     ,   A__ :							Tuple						)       ->	Optional[Any]:
        _snake_case		=				self.num_choices
        _snake_case		=				ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE						)
        model.to(__SCREAMING_SNAKE_CASE						)
        model.eval()
        _snake_case		=				input_ids.unsqueeze(1						).expand(-1     ,   self.num_choices     ,   -1						).contiguous()
        _snake_case		=				input_mask.unsqueeze(1						).expand(-1     ,   self.num_choices     ,   -1						).contiguous()
        _snake_case		=				model(
            __SCREAMING_SNAKE_CASE     ,   attention_mask=__SCREAMING_SNAKE_CASE     ,   labels=__SCREAMING_SNAKE_CASE     ,   )
        self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_choices)						)







      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	Union[str, Any]:
        _snake_case		=				self.prepare_config_and_inputs()
        (
            _snake_case	
        )		=				config_and_inputs
        _snake_case		=				{"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict



@require_torch
class     lowercase_       (    snake_case__  ,				snake_case__  ,				unittest.TestCase	):
      UpperCamelCase_     :						List[Any]		 =							(
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      UpperCamelCase_     :						Dict		 =							()
      UpperCamelCase_     :						Union[str, Any]		 =							(
          {
              "feature-extraction": ErnieMModel,
              "question-answering": ErnieMForQuestionAnswering,
              "text-classification": ErnieMForSequenceClassification,
              "token-classification": ErnieMForTokenClassification,
              "zero-shot": ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      UpperCamelCase_     :						Union[str, Any]		 =							False



      def 		UpperCamelCase_						(     self :							Union[str, Any]     ,   A__ :							Optional[Any]     ,   A__ :							Union[str, Any]     ,   A__ :							List[Any]     ,   A__ :							Tuple     ,   A__ :							Optional[int]						)       ->	Optional[Any]:







        if pipeline_test_casse_name == "QAPipelineTests":
          return True

        return False



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Optional[int]:
        _snake_case		=				ErnieMModelTester(self						)
        _snake_case		=				ConfigTester(self     ,   config_class=__SCREAMING_SNAKE_CASE     ,   hidden_size=37						)



      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	List[Any]:
        self.config_tester.run_common_tests()



      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	List[str]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Union[str, Any]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          _snake_case		=				type
          self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							List[Any]						)       ->	List[str]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	Tuple:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	Any:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							Tuple						)       ->	List[str]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_information_extraction(*__SCREAMING_SNAKE_CASE						)



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Union[str, Any]:
        _snake_case		=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE						)







      @slow
      def 		UpperCamelCase_						(     self :							str						)       ->	Tuple:







        for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _snake_case		=				ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
          self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)



@require_torch
class     lowercase_       (    unittest.TestCase	):



      @slow
      def 		UpperCamelCase_						(     self :							int						)       ->	Any:
        _snake_case		=				ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''						)
        model.eval()
        _snake_case		=				torch.tensor([[0, 1, 2, 3, 4, 5]]						)
        _snake_case		=				model(__SCREAMING_SNAKE_CASE						)[0]

        # TODO Replace vocab size
        _snake_case		=				768

        _snake_case		=				torch.Size((1, 6, hidden_size)						)
        self.assertEqual(output.shape     ,   __SCREAMING_SNAKE_CASE						)

        _snake_case		=				torch.tensor(
            [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]						)

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





from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






   for _ in tqdm(range(0							,  args.n_examples							,  A_      )      ):
      yield [next(A_      )[args.text_column] for _ in range(A_      )]


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)

