




from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TFDebertaVaForMaskedLM,
							    TFDebertaVaForQuestionAnswering,
							    TFDebertaVaForSequenceClassification,
							    TFDebertaVaForTokenClassification,
							    TFDebertaVaModel,
							)




class 		__A   :

						def __init__(		self :List[str]     ,			__snake_case :Optional[int]     ,			__snake_case :str=13     ,			__snake_case :int=7     ,			__snake_case :List[str]=True     ,			__snake_case :Tuple=True     ,			__snake_case :List[str]=True     ,			__snake_case :Optional[int]=True     ,			__snake_case :Tuple=99     ,			__snake_case :Tuple=32     ,			__snake_case :Optional[int]=2     ,			__snake_case :str=4     ,			__snake_case :List[Any]=37     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :str=0.1     ,			__snake_case :List[str]=0.1     ,			__snake_case :Tuple=5_12     ,			__snake_case :List[Any]=16     ,			__snake_case :Union[str, Any]=2     ,			__snake_case :Tuple=0.02     ,			__snake_case :int=False     ,			__snake_case :Optional[Any]=True     ,			__snake_case :Union[str, Any]="None"     ,			__snake_case :str=3     ,			__snake_case :Any=4     ,			__snake_case :Dict=None     ,			):
										'''simple docstring'''

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

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

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

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

										__magic_name__						:						Union[str, Any]										=None
										if self.use_token_type_ids:
														__magic_name__						:						Any										=ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size			)

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

										__magic_name__						:						str										=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     ,			relative_attention=self.relative_attention     ,			position_biased_input=self.position_biased_input     ,			initializer_range=self.initializer_range     ,			return_dict=__snake_case     ,			)

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

						def      A__    (		self :int     ,			__snake_case :Any     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[Any]     ,			__snake_case :Tuple     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]     ,			__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=TFDebertaVaModel(config=__snake_case			)
										__magic_name__						:						Tuple										={"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

										__magic_name__						:						List[str]										=[input_ids, input_mask]
										__magic_name__						:						List[str]										=model(__snake_case			)

										__magic_name__						:						Union[str, Any]										=model(__snake_case			)

										self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)			)

						def      A__    (		self :List[Any]     ,			__snake_case :List[str]     ,			__snake_case :List[str]     ,			__snake_case :Union[str, Any]     ,			__snake_case :List[str]     ,			__snake_case :Optional[Any]     ,			__snake_case :Optional[Any]     ,			__snake_case :Tuple			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=TFDebertaVaForMaskedLM(config=__snake_case			)
										__magic_name__						:						Optional[int]										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__magic_name__						:						int										=model(__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)			)

						def      A__    (		self :Any     ,			__snake_case :Tuple     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :Any     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.num_labels
										__magic_name__						:						Union[str, Any]										=TFDebertaVaForSequenceClassification(config=__snake_case			)
										__magic_name__						:						Tuple										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__magic_name__						:						int										=model(__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)			)

						def      A__    (		self :Dict     ,			__snake_case :List[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :str     ,			__snake_case :Dict     ,			__snake_case :int     ,			__snake_case :str     ,			__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.num_labels
										__magic_name__						:						Optional[Any]										=TFDebertaVaForTokenClassification(config=__snake_case			)
										__magic_name__						:						Optional[Any]										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										__magic_name__						:						int										=model(__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.num_labels)			)

						def      A__    (		self :List[str]     ,			__snake_case :str     ,			__snake_case :Dict     ,			__snake_case :int     ,			__snake_case :Dict     ,			__snake_case :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=TFDebertaVaForQuestionAnswering(config=__snake_case			)
										__magic_name__						:						str										={
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										__magic_name__						:						List[Any]										=model(__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      A__    (		self :int			):
										'''simple docstring'''

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




@require_tf
class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       (
						    (
						        TFDebertaVaModel,
						        TFDebertaVaForMaskedLM,
						        TFDebertaVaForQuestionAnswering,
						        TFDebertaVaForSequenceClassification,
						        TFDebertaVaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCamelCase      =       (
						    {
						        """feature-extraction""": TFDebertaVaModel,
						        """fill-mask""": TFDebertaVaForMaskedLM,
						        """question-answering""": TFDebertaVaForQuestionAnswering,
						        """text-classification""": TFDebertaVaForSequenceClassification,
						        """token-classification""": TFDebertaVaForTokenClassification,
						        """zero-shot""": TFDebertaVaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCamelCase      =       False
						UpperCamelCase      =       False

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=TFDebertaVaModelTester(self			)
										__magic_name__						:						Any										=ConfigTester(self     ,			config_class=__snake_case     ,			hidden_size=37			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Any										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						str										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__snake_case			)

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						str										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__snake_case			)

						def      A__    (		self :int			):
										'''simple docstring'''

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

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__snake_case			)


						@slow
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Any										=TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""			)
										self.assertIsNotNone(__snake_case			)




@require_tf
class 		__A   (      unittest.TestCase      ):

						@unittest.skip(reason="""Model not available yet"""			)
						def      A__    (		self :str			):
										'''simple docstring'''

										pass


						@slow
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""			)
										__magic_name__						:						str										=tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			)
										__magic_name__						:						List[str]										=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
										__magic_name__						:						Tuple										=model(__snake_case     ,			attention_mask=__snake_case			)[0]

										__magic_name__						:						Any										=tf.constant(
										    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]			)
										tf.debugging.assert_near(output[:, 1:4, 1:4]     ,			__snake_case     ,			atol=1E-4			)






"""simple docstring"""




from argparse import ArgumentParser

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


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     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     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				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=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       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=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

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


