





from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
         TFDebertaForMaskedLM,
         TFDebertaForQuestionAnswering,
         TFDebertaForSequenceClassification,
         TFDebertaForTokenClassification,
         TFDebertaModel,
     )



class 				_UpperCamelCase       :

       def __init__(		self:			Any       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			List[str]=13       , _SCREAMING_SNAKE_CASE:			Optional[Any]=7       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			Optional[Any]=99       , _SCREAMING_SNAKE_CASE:			Tuple=32       , _SCREAMING_SNAKE_CASE:			Optional[int]=2       , _SCREAMING_SNAKE_CASE:			List[Any]=4       , _SCREAMING_SNAKE_CASE:			Tuple=37       , _SCREAMING_SNAKE_CASE:			int="gelu"       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0.1       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=512       , _SCREAMING_SNAKE_CASE:			int=16       , _SCREAMING_SNAKE_CASE:			Tuple=2       , _SCREAMING_SNAKE_CASE:			Tuple=0.02       , _SCREAMING_SNAKE_CASE:			List[str]=3       , _SCREAMING_SNAKE_CASE:			Optional[int]=4       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , )							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	parent
              UpperCamelCase_ 							=	13
              UpperCamelCase_ 							=	7
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	99
              UpperCamelCase_ 							=	32
              UpperCamelCase_ 							=	2
              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	37
              UpperCamelCase_ 							=	"gelu"
              UpperCamelCase_ 							=	0.1
              UpperCamelCase_ 							=	0.1
              UpperCamelCase_ 							=	512
              UpperCamelCase_ 							=	16
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	-1
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	2
              UpperCamelCase_ 							=	0.02
              UpperCamelCase_ 							=	3
              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	None

       def lowercase			(		self:			str		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size		)

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

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

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

              UpperCamelCase_ 							=	DebertaConfig(
                  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       , max_relative_positions=self.max_relative_positions       , position_biased_input=self.position_biased_input       , initializer_range=self.initializer_range       , return_dict=_SCREAMING_SNAKE_CASE       , )

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

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			str		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	TFDebertaModel(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

              UpperCamelCase_ 							=	[input_ids, input_mask]
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

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

       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			int		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFDebertaForMaskedLM(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)		)

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.num_labels
              UpperCamelCase_ 							=	TFDebertaForSequenceClassification(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }

              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels)		)

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.num_labels
              UpperCamelCase_ 							=	TFDebertaForTokenClassification(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels)		)

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	TFDebertaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }

              UpperCamelCase_ 							=	model(_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 lowercase			(		self:			List[str]		)							->			List[str]:




              """simple docstring"""


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



@require_tf
class 				_UpperCamelCase       (						lowerCAmelCase_	,    lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       Tuple       = (
           (
               TFDebertaModel,
               TFDebertaForMaskedLM,
               TFDebertaForQuestionAnswering,
               TFDebertaForSequenceClassification,
               TFDebertaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       _UpperCamelCase					:       str       = (
           {
               '''feature-extraction''': TFDebertaModel,
               '''fill-mask''': TFDebertaForMaskedLM,
               '''question-answering''': TFDebertaForQuestionAnswering,
               '''text-classification''': TFDebertaForSequenceClassification,
               '''token-classification''': TFDebertaForTokenClassification,
               '''zero-shot''': TFDebertaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       _UpperCamelCase					:       Tuple       = False
       _UpperCamelCase					:       int       = False

       def lowercase			(		self:			str		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	TFDebertaModelTester(self		)
              UpperCamelCase_ 							=	ConfigTester(self       , config_class=_SCREAMING_SNAKE_CASE       , hidden_size=37		)

       def lowercase			(		self:			Any		)							->			List[str]:




              """simple docstring"""


              self.config_tester.run_common_tests()

       def lowercase			(		self:			List[Any]		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Dict		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Any		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Dict		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase			(		self:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFDebertaModel.from_pretrained("kamalkraj/deberta-base"		)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)



@require_tf
class 				_UpperCamelCase       (						unittest.TestCase  ):

       @unittest.skip(reason="Model not available yet"		)
       def lowercase			(		self:			Union[str, Any]		)							->			Optional[Any]:




              """simple docstring"""


              pass



       @slow
       def lowercase			(		self:			Union[str, Any]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	TFDebertaModel.from_pretrained("kamalkraj/deberta-base"		)
              UpperCamelCase_ 							=	tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
              UpperCamelCase_ 							=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , attention_mask=_SCREAMING_SNAKE_CASE		)[0]

              UpperCamelCase_ 							=	tf.constant(
                  [
                      [
                          [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                          [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                          [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
                      ]
                  ]		)
              tf.debugging.assert_near(output[:, 1:4, 1:4]       , _SCREAMING_SNAKE_CASE       , atol=1e-4		)



import numpy as np
import qiskit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    List[Any]						     =			qiskit.QuantumCircuit(num_qubits + 1 )

   # First, let's deal with the case in which oracle is balanced
   if case == "balanced":
      # First generate a random number that tells us which CNOTs to
      # wrap in X-gates:
      _A:    Any						     =			np.random.randint(1						,						2**num_qubits )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      _A:    int						     =			format(a						,						f"""0{num_qubits}b""" )
      # Next, we place the first X-gates. Each digit in our binary string
      # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
      # we apply an X-gate to that qubit:
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
      for index in range(a ):
         oracle_qc.cx(a						,						a )
      # Next, place the final X-gates
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )

    # Case in which oracle is constant
   if case == "constant":
      # First decide what the fixed output of the oracle will be
      # (either always 0 or always 1)
      _A:    int						     =			np.random.randint(2 )
      if output == 1:
         oracle_qc.x(a )

   _A:    int						     =			oracle_qc.to_gate()
   _A:    str						     =			'''Oracle'''  # To show when we display the circuit
   return oracle_gate






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    Optional[int]						     =			qiskit.QuantumCircuit(num_qubits + 1						,						a )
   # Set up the output qubit:
   dj_circuit.x(a )
   dj_circuit.h(a )
   # And set up the input register:
   for qubit in range(a ):
      dj_circuit.h(a )
   # Let's append the oracle gate to our circuit:
   dj_circuit.append(a						,						range(num_qubits + 1 ) )
   # Finally, perform the H-gates again and measure:
   for qubit in range(a ):
      dj_circuit.h(a )

   for i in range(a ):
      dj_circuit.measure(a						,						a )

   return dj_circuit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.result.counts.Counts:
   _A:    int						     =			qiskit.Aer.get_backend('''aer_simulator''' )

   _A:    Optional[Any]						     =			dj_oracle(a						,						a )
   _A:    int						     =			dj_algorithm(a						,						a )

   # Execute the circuit on the simulator
   _A:    int						     =			qiskit.execute(a						,						a						,						shots=10_00 )

   # Return the histogram data of the results of the experiment.
   return job.result().get_counts(a )


if __name__ == "__main__":
   print(F"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}""")
   print(F"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}""")



