"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import CTRLConfig, 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.ctrl.modeling_tf_ctrl import (
	    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFCTRLForSequenceClassification,
	    TFCTRLLMHeadModel,
	    TFCTRLModel,
	)






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, ):
										lowerCamelCase__					=parent
										lowerCamelCase__					=13
										lowerCamelCase__					=7
										lowerCamelCase__					=True
										lowerCamelCase__					=True
										lowerCamelCase__					=True
										lowerCamelCase__					=True
										lowerCamelCase__					=True
										lowerCamelCase__					=99
										lowerCamelCase__					=32
										lowerCamelCase__					=2
										lowerCamelCase__					=4
										lowerCamelCase__					=37
										lowerCamelCase__					="gelu"
										lowerCamelCase__					=0.1
										lowerCamelCase__					=0.1
										lowerCamelCase__					=512
										lowerCamelCase__					=16
										lowerCamelCase__					=2
										lowerCamelCase__					=0.0_2
										lowerCamelCase__					=3
										lowerCamelCase__					=4
										lowerCamelCase__					=None
										lowerCamelCase__					=self.vocab_size - 1

				def 		_a	(      self      ):
										lowerCamelCase__					=ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size      )

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

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

										lowerCamelCase__					=None
										if self.use_mc_token_ids:
																lowerCamelCase__					=ids_tensor([self.batch_size, self.num_choices]	, self.seq_length      )

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

										lowerCamelCase__					=CTRLConfig(
										    vocab_size=self.vocab_size	, n_embd=self.hidden_size	, n_layer=self.num_hidden_layers	, n_head=self.num_attention_heads	, n_positions=self.max_position_embeddings	, pad_token_id=self.pad_token_id	, )

										lowerCamelCase__					=ids_tensor([self.num_hidden_layers, self.num_attention_heads]	, 2      )

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										    token_type_ids,
										    mc_token_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase      ):
										lowerCamelCase__					=TFCTRLModel(config=_lowerCamelCase      )
										lowerCamelCase__					={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										lowerCamelCase__					=model(_lowerCamelCase      )

										lowerCamelCase__					=[input_ids, None, input_mask]  # None is the input for 'past'
										lowerCamelCase__					=model(_lowerCamelCase      )

										lowerCamelCase__					=model(_lowerCamelCase      )

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

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase      ):
										lowerCamelCase__					=TFCTRLLMHeadModel(config=_lowerCamelCase      )
										lowerCamelCase__					={"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
										lowerCamelCase__					=model(_lowerCamelCase      )
										self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)      )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase      ):
										lowerCamelCase__					=self.num_labels
										lowerCamelCase__					=ids_tensor([self.batch_size]	, self.type_sequence_label_size      )
										lowerCamelCase__					={
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										    "labels": sequence_labels,
										}
										lowerCamelCase__					=TFCTRLForSequenceClassification(_lowerCamelCase      )
										lowerCamelCase__					=model(_lowerCamelCase      )
										self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels)      )






				def 		_a	(      self      ):
										lowerCamelCase__					=self.prepare_config_and_inputs()

										(
										    (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      (
										    lowerCamelCase__
										)					,      
										)					=config_and_inputs

										lowerCamelCase__					={"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	):
				A__      :   Union[str, Any]  							=       (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
				A__      :   Any  							=       (TFCTRLLMHeadModel,) if is_tf_available() else ()
				A__      :   Any  							=       (
				    {
				        '''feature-extraction''': TFCTRLModel,
				        '''text-classification''': TFCTRLForSequenceClassification,
				        '''text-generation''': TFCTRLLMHeadModel,
				        '''zero-shot''': TFCTRLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				A__      :   Any  							=       False
				A__      :   Union[str, Any]  							=       False

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
																# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
																# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
																# config could not be created.
																return True

										return False

				def 		_a	(      self      ):
										lowerCamelCase__					=TFCTRLModelTester(self      )
										lowerCamelCase__					=ConfigTester(self	, config_class=_lowerCamelCase	, n_embd=37      )

				def 		_a	(      self      ):
										self.config_tester.run_common_tests()

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_model(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_lm_head(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_for_sequence_classification(*_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					,      lowerCamelCase__					=self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__					=[TFCTRLLMHeadModel]
										lowerCamelCase__					=[TFCTRLForSequenceClassification]

										for model_class in self.all_model_classes:
																lowerCamelCase__					=model_class(_lowerCamelCase      )
																model.build()  # may be needed for the get_bias() call below
																assert isinstance(model.get_input_embeddings()	, tf.keras.layers.Layer      )

																if model_class in list_lm_models:
																						lowerCamelCase__					=model.get_output_embeddings()
																						assert isinstance(_lowerCamelCase	, tf.keras.layers.Layer      )
																						lowerCamelCase__					=model.get_bias()
																						assert isinstance(_lowerCamelCase	, _lowerCamelCase      )
																						for k, v in name.items():
																												assert isinstance(_lowerCamelCase	, tf.Variable      )
																elif model_class in list_other_models_with_output_ebd:
																						lowerCamelCase__					=model.get_output_embeddings()
																						assert isinstance(_lowerCamelCase	, tf.keras.layers.Layer      )
																						lowerCamelCase__					=model.get_bias()
																						assert name is None
																else:
																						lowerCamelCase__					=model.get_output_embeddings()
																						assert x is None
																						lowerCamelCase__					=model.get_bias()
																						assert name is None






				@slow
				def 		_a	(      self      ):
										for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowerCamelCase__					=TFCTRLModel.from_pretrained(_lowerCamelCase      )
																self.assertIsNotNone(_lowerCamelCase      )






@require_tf
class    __UpperCAmelCase						(	unittest.TestCase	):

				@slow
				def 		_a	(      self      ):
										lowerCamelCase__					=TFCTRLLMHeadModel.from_pretrained("ctrl"      )
										lowerCamelCase__					=tf.convert_to_tensor([[11859, 0, 1611, 8]]	, dtype=tf.intaa      )  # Legal the president is
										lowerCamelCase__					=[
										    11859,
										    0,
										    1611,
										    8,
										    5,
										    150,
										    26449,
										    2,
										    19,
										    348,
										    469,
										    3,
										    2595,
										    48,
										    20740,
										    246533,
										    246533,
										    19,
										    30,
										    5,
										]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

										lowerCamelCase__					=model.generate(_lowerCamelCase	, do_sample=_lowerCamelCase      )
										self.assertListEqual(output_ids[0].numpy().tolist()	, _lowerCamelCase      )









"""simple docstring"""





def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       len(lowercase_		)
 for i in range(length - 1		):
  __SCREAMING_SNAKE_CASE :			str												=       False
  for j in range(length - 1 - i		):
   if collection[j] > collection[j + 1]:
    __SCREAMING_SNAKE_CASE :			Any												=       True
    __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       collection[j + 1], collection[j]
  if not swapped:
   break  # Stop iteration if the collection is sorted.
 return collection


if __name__ == "__main__":
    import doctest
    import time

    doctest.testmod()

    _lowerCamelCase       =	input('''Enter numbers separated by a comma:''').strip()
    _lowerCamelCase       =	[int(item) for item in user_input.split(''',''')]
    _lowerCamelCase       =	time.process_time()
    print(*bubble_sort(unsorted), sep=''',''')
    print(f'Processing time: {(time.process_time() - start)%1e9 + 7}')

