






'''simple docstring'''

import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          DebertaVaForMaskedLM,
          DebertaVaForMultipleChoice,
          DebertaVaForQuestionAnswering,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaModel,
      )
      from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST




class     A    (      _snake_case						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=13			,     __lowerCAmelCase=7			,     __lowerCAmelCase=True			,     __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=5_12			,     __lowerCAmelCase=16			,     __lowerCAmelCase=2			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=False			,     __lowerCAmelCase=True			,     __lowerCAmelCase="None"			,     __lowerCAmelCase=3			,     __lowerCAmelCase=4			,     __lowerCAmelCase=None			,     ):
       UpperCamelCase_				:   List[str]					    =							parent
       UpperCamelCase_				:   List[str]					    =							batch_size
       UpperCamelCase_				:   List[Any]					    =							seq_length
       UpperCamelCase_				:   List[Any]					    =							is_training
       UpperCamelCase_				:   Dict					    =							use_input_mask
       UpperCamelCase_				:   Tuple					    =							use_token_type_ids
       UpperCamelCase_				:   Optional[Any]					    =							use_labels
       UpperCamelCase_				:   Optional[Any]					    =							vocab_size
       UpperCamelCase_				:   List[str]					    =							hidden_size
       UpperCamelCase_				:   int					    =							num_hidden_layers
       UpperCamelCase_				:   Union[str, Any]					    =							num_attention_heads
       UpperCamelCase_				:   Optional[Any]					    =							intermediate_size
       UpperCamelCase_				:   Union[str, Any]					    =							hidden_act
       UpperCamelCase_				:   str					    =							hidden_dropout_prob
       UpperCamelCase_				:   Dict					    =							attention_probs_dropout_prob
       UpperCamelCase_				:   Tuple					    =							max_position_embeddings
       UpperCamelCase_				:   List[Any]					    =							type_vocab_size
       UpperCamelCase_				:   Optional[Any]					    =							type_sequence_label_size
       UpperCamelCase_				:   Union[str, Any]					    =							initializer_range
       UpperCamelCase_				:   int					    =							num_labels
       UpperCamelCase_				:   List[Any]					    =							num_choices
       UpperCamelCase_				:   Any					    =							relative_attention
       UpperCamelCase_				:   Any					    =							position_biased_input
       UpperCamelCase_				:   Optional[int]					    =							pos_att_type
       UpperCamelCase_				:   str					    =							scope
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size )

       UpperCamelCase_				:   List[str]					    =							None
       if self.use_input_mask:
             UpperCamelCase_				:   Any					    =							ids_tensor([self.batch_size, self.seq_length]			,     vocab_size=2 )

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

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

       UpperCamelCase_				:   Dict					    =							self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def   _UpperCAmelCase       (							self ):
       return DebertaVaConfig(
           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			,     relative_attention=self.relative_attention			,     position_biased_input=self.position_biased_input			,     pos_att_type=self.pos_att_type			,     )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       self.parent.assertListEqual(list(result.loss.size() )			,     [] )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							DebertaVaModel(config=__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()
       UpperCamelCase_				:   str					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase			,     token_type_ids=__lowerCAmelCase )[0]
       UpperCamelCase_				:   Optional[int]					    =							model(__lowerCAmelCase			,     token_type_ids=__lowerCAmelCase )[0]
       UpperCamelCase_				:   Optional[int]					    =							model(__lowerCAmelCase )[0]

       self.parent.assertListEqual(list(sequence_output.size() )			,     [self.batch_size, self.seq_length, self.hidden_size] )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							DebertaVaForMaskedLM(config=__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()
       UpperCamelCase_				:   Tuple					    =							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.num_labels
       UpperCamelCase_				:   Union[str, Any]					    =							DebertaVaForSequenceClassification(__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()
       UpperCamelCase_				:   Optional[Any]					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase			,     token_type_ids=__lowerCAmelCase			,     labels=__lowerCAmelCase )
       self.parent.assertListEqual(list(result.logits.size() )			,     [self.batch_size, self.num_labels] )
       self.check_loss_output(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.num_labels
       UpperCamelCase_				:   Any					    =							DebertaVaForTokenClassification(config=__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()
       UpperCamelCase_				:   Union[str, Any]					    =							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							DebertaVaForQuestionAnswering(config=__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()
       UpperCamelCase_				:   Optional[Any]					    =							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							DebertaVaForMultipleChoice(config=__lowerCAmelCase )
       model.to(__lowerCAmelCase )
       model.eval()
       UpperCamelCase_				:   Union[str, Any]					    =							input_ids.unsqueeze(1 ).expand(-1			,     self.num_choices			,     -1 ).contiguous()
       UpperCamelCase_				:   Tuple					    =							token_type_ids.unsqueeze(1 ).expand(-1			,     self.num_choices			,     -1 ).contiguous()
       UpperCamelCase_				:   int					    =							input_mask.unsqueeze(1 ).expand(-1			,     self.num_choices			,     -1 ).contiguous()
       UpperCamelCase_				:   Union[str, Any]					    =							model(
           __lowerCAmelCase			,     attention_mask=__lowerCAmelCase			,     token_type_ids=__lowerCAmelCase			,     labels=__lowerCAmelCase			,     )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_choices) )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Tuple					    =							self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase_     
       )    ,			(
           UpperCamelCase_     
       )    ,			(
           UpperCamelCase_     
       )    ,			(
           UpperCamelCase_     
       )    ,			(
           UpperCamelCase_     
       )    ,			(
           UpperCamelCase_     
       )    ,			(
           UpperCamelCase_     
       )    ,			     
       )				:   int					    =							config_and_inputs
       UpperCamelCase_				:   Any					    =							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict






@require_torch
class     A    (      _snake_case,   _snake_case,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Any      =						(
     (
         DebertaVaModel,
         DebertaVaForMaskedLM,
         DebertaVaForSequenceClassification,
         DebertaVaForTokenClassification,
         DebertaVaForQuestionAnswering,
         DebertaVaForMultipleChoice,
     )
     if is_torch_available()
     else ()
 )
 __a			:						Dict      =						(
     {
         'feature-extraction': DebertaVaModel,
         'fill-mask': DebertaVaForMaskedLM,
         'question-answering': DebertaVaForQuestionAnswering,
         'text-classification': DebertaVaForSequenceClassification,
         'token-classification': DebertaVaForTokenClassification,
         'zero-shot': DebertaVaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 __a			:						Optional[Any]      =						True
 __a			:						str      =						False
 __a			:						Dict      =						False
 __a			:						int      =						False
 __a			:						Union[str, Any]      =						False
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							DebertaVaModelTester(self )
       UpperCamelCase_				:   Dict					    =							ConfigTester(self			,     config_class=__lowerCAmelCase			,     hidden_size=37 )
 def   _UpperCAmelCase       (							self ):
       self.config_tester.run_common_tests()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Any					    =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*__lowerCAmelCase )





 @slow
 def   _UpperCAmelCase       (							self ):

       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_				:   Tuple					    =							DebertaVaModel.from_pretrained(__lowerCAmelCase )
             self.assertIsNotNone(__lowerCAmelCase )






@require_torch
@require_sentencepiece
@require_tokenizers
class     A    (      unittest.TestCase						):






 """simple docstring"""





 @unittest.skip(reason="""Model not available yet""" )
 def   _UpperCAmelCase       (							self ):
       pass





 @slow
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge""" )

       UpperCamelCase_				:   List[str]					    =							torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
       UpperCamelCase_				:   Optional[int]					    =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
       with torch.no_grad():
             UpperCamelCase_				:   List[Any]					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase )[0]
       # compare the actual values for a slice.
       UpperCamelCase_				:   List[str]					    =							torch.tensor(
           [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,     __lowerCAmelCase			,     atol=1E-4 )			,     F"{output[:, 1:4, 1:4]}" )








'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase							=logging.get_logger(__name__)  # pylint: disable=invalid-name



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(a_	):
                  return ext

      raise Exception(
          f"Unable to determine file format from file extension {path}. "
          f"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"	)



def 							snake_case					(     a_							: List[Any]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							pipeline(
          task=args.task    ,   model=args.model if args.model else None    ,   config=args.config    ,   tokenizer=args.tokenizer    ,   device=args.device    ,   )
      UpperCamelCase_				:   List[Any]					    =							try_infer_format_from_ext(args.input	) if args.format == """infer""" else args.format
      UpperCamelCase_				:   Tuple					    =							PipelineDataFormat.from_str(
          format=a_    ,   output_path=args.output    ,   input_path=args.input    ,   column=args.column if args.column else nlp.default_input_names    ,   overwrite=args.overwrite    ,   )
      return RunCommand(a_    ,   a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Union[str, Any]					    =							nlp
       UpperCamelCase_				:   Optional[Any]					    =							reader
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							parser.add_parser("""run"""			,     help="""Run a pipeline through the CLI""" )
       run_parser.add_argument("""--task"""			,     choices=get_supported_tasks()			,     help="""Task to run""" )
       run_parser.add_argument("""--input"""			,     type=__lowerCAmelCase			,     help="""Path to the file to use for inference""" )
       run_parser.add_argument("""--output"""			,     type=__lowerCAmelCase			,     help="""Path to the file that will be used post to write results.""" )
       run_parser.add_argument("""--model"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model to instantiate.""" )
       run_parser.add_argument("""--config"""			,     type=__lowerCAmelCase			,     help="""Name or path to the model's config to instantiate.""" )
       run_parser.add_argument(
           """--tokenizer"""			,     type=__lowerCAmelCase			,     help="""Name of the tokenizer to use. (default: same as the model name)""" )
       run_parser.add_argument(
           """--column"""			,     type=__lowerCAmelCase			,     help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""			,     )
       run_parser.add_argument(
           """--format"""			,     type=__lowerCAmelCase			,     default="""infer"""			,     choices=PipelineDataFormat.SUPPORTED_FORMATS			,     help="""Input format to read from"""			,     )
       run_parser.add_argument(
           """--device"""			,     type=__lowerCAmelCase			,     default=-1			,     help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""			,     )
       run_parser.add_argument("""--overwrite"""			,     action="""store_true"""			,     help="""Allow overwriting the output file.""" )
       run_parser.set_defaults(func=__lowerCAmelCase )





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self._nlp, []

       for entry in self._reader:
             UpperCamelCase_				:   List[Any]					    =							nlp(**__lowerCAmelCase ) if self._reader.is_multi_columns else nlp(__lowerCAmelCase )
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   outputs.append(__lowerCAmelCase )
             else:
                   outputs += output

        # Saving data
       if self._nlp.binary_output:
             UpperCamelCase_				:   int					    =							self._reader.save_binary(__lowerCAmelCase )
             logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}" )
       else:
             self._reader.save(__lowerCAmelCase )






