
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     __A(				a    ):




				def __init__(  self	,  _snake_case	,  )					->   List[Any]:


								'''simple docstring'''




								__a    							=					parent
								__a    							=					13
								__a    							=					7
								__a    							=					True
								__a    							=					True
								__a    							=					True
								__a    							=					True
								__a    							=					True
								__a    							=					99
								__a    							=					32
								__a    							=					2
								__a    							=					4
								__a    							=					37
								__a    							=					'''gelu'''
								__a    							=					0.1
								__a    							=					0.1
								__a    							=					512
								__a    							=					16
								__a    							=					2
								__a    							=					0.02
								__a    							=					3
								__a    							=					4
								__a    							=					None
								__a    							=					self.vocab_size - 1




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__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
								if self.use_token_type_ids:
												__a    							=					ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size       )

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

								__a    							=					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        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  *_snake_case       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					TFCTRLModel(config=_snake_case       )
								__a    							=					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								__a    							=					model(_snake_case       )

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

								__a    							=					model(_snake_case       )

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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  *_snake_case       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					TFCTRLLMHeadModel(config=_snake_case       )
								__a    							=					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
								__a    							=					model(_snake_case       )
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size)       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  *_snake_case       )					->   Tuple:


								'''simple docstring'''




								__a    							=					self.num_labels
								__a    							=					ids_tensor([self.batch_size]	,  self.type_sequence_label_size       )
								__a    							=					{
								    '''input_ids''': input_ids,
								    '''token_type_ids''': token_type_ids,
								    '''labels''': sequence_labels,
								}
								__a    							=					TFCTRLForSequenceClassification(_snake_case       )
								__a    							=					model(_snake_case       )
								self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								__a    							=					self.prepare_config_and_inputs()

								(
								    (
								    __a
								)							,  (
								    __a
								)							,  (
								    __a
								)							,  (
								    __a
								)							,  (
								    __a
								)							,  (
								    __a
								)							,  (
								    __a
								)							,  (
								    __a
								)							,  (
								    __a
								)							,  
								)    							=					config_and_inputs

								__a    							=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict







@require_tf
class     __A(				a						,     a						,     unittest.TestCase    ):
				snake_case_		     =					(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
				snake_case_		     =					(TFCTRLLMHeadModel,) if is_tf_available() else ()
				snake_case_		     =					(
				    {
				        '''feature-extraction''': TFCTRLModel,
				        '''text-classification''': TFCTRLForSequenceClassification,
				        '''text-generation''': TFCTRLLMHeadModel,
				        '''zero-shot''': TFCTRLForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				snake_case_		     =					False
				snake_case_		     =					False




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )					->   Union[str, Any]:


								'''simple docstring'''




								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        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					TFCTRLModelTester(self       )
								__a    							=					ConfigTester(self	,  config_class=_snake_case	,  n_embd=37       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								self.config_tester.run_common_tests()




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_lm_head(*_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								__a    							=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_for_sequence_classification(*_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a							,  __a    							=					self.model_tester.prepare_config_and_inputs_for_common()
								__a    							=					[TFCTRLLMHeadModel]
								__a    							=					[TFCTRLForSequenceClassification]

								for model_class in self.all_model_classes:
												__a    							=					model_class(_snake_case       )
												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:
																__a    							=					model.get_output_embeddings()
																assert isinstance(_snake_case	,  tf.keras.layers.Layer       )
																__a    							=					model.get_bias()
																assert isinstance(_snake_case	,  _snake_case       )
																for k, v in name.items():
																				assert isinstance(_snake_case	,  tf.Variable       )
												elif model_class in list_other_models_with_output_ebd:
																__a    							=					model.get_output_embeddings()
																assert isinstance(_snake_case	,  tf.keras.layers.Layer       )
																__a    							=					model.get_bias()
																assert name is None
												else:
																__a    							=					model.get_output_embeddings()
																assert x is None
																__a    							=					model.get_bias()
																assert name is None




				@slow
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__a    							=					TFCTRLModel.from_pretrained(_snake_case       )
												self.assertIsNotNone(_snake_case       )







@require_tf
class     __A(				unittest.TestCase    ):




				@slow
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					TFCTRLLMHeadModel.from_pretrained('''ctrl'''       )
								__a    							=					tf.convert_to_tensor([[11_859, 0, 1_611, 8]]	,  dtype=tf.intaa       )  # Legal the president is
								__a    							=					[
								    11_859,
								    0,
								    1_611,
								    8,
								    5,
								    150,
								    26_449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2_595,
								    48,
								    20_740,
								    246_533,
								    246_533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

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

from itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()