





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DistilBertConfig, 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.models.distilbert.modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertModel,
		)






class        UpperCAmelCase     :
	def __init__(    self				:str						,	lowercase_				:str						,	)->							str:
					A__    =      parent
					A__    =      13
					A__    =      7
					A__    =      True
					A__    =      True
					A__    =      False
					A__    =      True
					A__    =      99
					A__    =      32
					A__    =      2
					A__    =      4
					A__    =      37
					A__    =      "gelu"
					A__    =      0.1
					A__    =      0.1
					A__    =      5_12
					A__    =      16
					A__    =      2
					A__    =      0.0_2
					A__    =      3
					A__    =      4
					A__    =      None
	def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							int:
					A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

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

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

					A__    =      DistilBertConfig(
					    vocab_size=self.vocab_size						,	dim=self.hidden_size						,	n_layers=self.num_hidden_layers						,	n_heads=self.num_attention_heads						,	hidden_dim=self.intermediate_size						,	hidden_act=self.hidden_act						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	)

					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
	def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[int]						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:str							)->							List[str]:
					A__    =      TFDistilBertModel(config=lowercase_							)
					A__    =      {"input_ids": input_ids, "attention_mask": input_mask}

					A__    =      model(lowercase_							)

					A__    =      [input_ids, input_mask]

					A__    =      model(lowercase_							)

					self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
	def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
					A__    =      TFDistilBertForMaskedLM(config=lowercase_							)
					A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
					A__    =      model(lowercase_							)
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
	def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:Optional[int]						,	lowercase_				:str						,	lowercase_				:List[Any]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
					A__    =      TFDistilBertForQuestionAnswering(config=lowercase_							)
					A__    =      {
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					}
					A__    =      model(lowercase_							)
					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				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Any						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]							)->							Any:
					A__    =      self.num_labels
					A__    =      TFDistilBertForSequenceClassification(lowercase_							)
					A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
					A__    =      model(lowercase_							)
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)							)
	def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:int						,	lowercase_				:Union[str, Any]							)->							str:
					A__    =      self.num_choices
					A__    =      TFDistilBertForMultipleChoice(lowercase_							)
					A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
					A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
					A__    =      {
					    "input_ids": multiple_choice_inputs_ids,
					    "attention_mask": multiple_choice_input_mask,
					}
					A__    =      model(lowercase_							)
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)							)
	def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Any						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:int						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Tuple:
					A__    =      self.num_labels
					A__    =      TFDistilBertForTokenClassification(lowercase_							)
					A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
					A__    =      model(lowercase_							)
					self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)							)



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






@require_tf
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
	__lowercase       		=    (
	    (
	        TFDistilBertModel,
	        TFDistilBertForMaskedLM,
	        TFDistilBertForQuestionAnswering,
	        TFDistilBertForSequenceClassification,
	        TFDistilBertForTokenClassification,
	        TFDistilBertForMultipleChoice,
	    )
	    if is_tf_available()
	    else None
	)
	__lowercase       		=    (
	    {
	        """feature-extraction""": TFDistilBertModel,
	        """fill-mask""": TFDistilBertForMaskedLM,
	        """question-answering""": TFDistilBertForQuestionAnswering,
	        """text-classification""": TFDistilBertForSequenceClassification,
	        """token-classification""": TFDistilBertForTokenClassification,
	        """zero-shot""": TFDistilBertForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	__lowercase       		=    False
	__lowercase       		=    False
	def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
					A__    =      TFDistilBertModelTester(self							)
					A__    =      ConfigTester(self						,	config_class=lowercase_						,	dim=37							)
	def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
					self.config_tester.run_common_tests()
	def 					UpperCAmelCase_							(    self				:int							)->							Tuple:
					A__    =      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_model(*lowercase_							)
	def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
					A__    =      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase_							)
	def 					UpperCAmelCase_							(    self				:str							)->							str:
					A__    =      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase_							)
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
					A__    =      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase_							)
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:
					A__    =      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase_							)
	def 					UpperCAmelCase_							(    self				:str							)->							int:
					A__    =      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase_							)



	@slow
	def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
					for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
									A__    =      TFDistilBertModel.from_pretrained(lowercase_							)
									self.assertIsNotNone(lowercase_							)






@require_tf
class        UpperCAmelCase     (	unittest.TestCase    ):
	@slow
	def 					UpperCAmelCase_							(    self				:List[Any]							)->							Any:
					A__    =      TFDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					A__    =      tf.constant([[0, 1, 2, 3, 4, 5]]							)
					A__    =      model(lowercase_							)[0]

					A__    =      [1, 6, 7_68]
					self.assertEqual(output.shape						,	lowercase_							)

					A__    =      tf.constant(
					    [
					        [
					            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
					            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
					            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
					        ]
					    ]							)
					tf.debugging.assert_near(output[:, :3, :3]						,	lowercase_						,	atol=1E-4							)










'''simple docstring'''

import qiskit





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : int	):
    A__    =      qiskit.Aer.get_backend("aer_simulator"	)

    # Create a Quantum Circuit acting on the q register
    A__    =      qiskit.QuantumCircuit(_lowerCamelCase						,	_lowerCamelCase	)

    # Map the quantum measurement to the classical bits
    circuit.measure([0]						,	[0]	)

    # Execute the circuit on the simulator
    A__    =      qiskit.execute(_lowerCamelCase						,	_lowerCamelCase						,	shots=10_00	)

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


if __name__ == "__main__":
  print(f"""Total count for various states are: {single_qubit_measure(1, 1)}""")



