






'''simple docstring'''







def 		_UpperCAmelCase (				__A				: list[int]    ,		__A				: list[int]   ):
				a_		:	int									=   len(__A   )
				print('''The following activities are selected:'''   )

				# The first activity is always selected
				a_		:	Optional[int]									=   0
				print(__A    ,		end=''','''   )

				# Consider rest of the activities
				for j in range(__A   ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(__A    ,		end=''','''   )
												a_		:	Any									=   j


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__lowerCAmelCase									=   [1, 3, 0, 5, 8, 5]
		__lowerCAmelCase									=   [2, 4, 6, 7, 9, 9]
		print_max_activities(start, finish)












'''simple docstring'''







from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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 (
		    TFConvBertForMaskedLM,
		    TFConvBertForMultipleChoice,
		    TFConvBertForQuestionAnswering,
		    TFConvBertForSequenceClassification,
		    TFConvBertForTokenClassification,
		    TFConvBertModel,
		)





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      str=13	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=7	,    __SCREAMING_SNAKE_CASE   :      List[str]=True	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=True	,    __SCREAMING_SNAKE_CASE   :      Any=True	,    __SCREAMING_SNAKE_CASE   :      int=True	,    __SCREAMING_SNAKE_CASE   :      List[str]=99	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=32	,    __SCREAMING_SNAKE_CASE   :      Tuple=2	,    __SCREAMING_SNAKE_CASE   :      str=4	,    __SCREAMING_SNAKE_CASE   :      int=37	,    __SCREAMING_SNAKE_CASE   :      Any="gelu"	,    __SCREAMING_SNAKE_CASE   :      List[Any]=0.1	,    __SCREAMING_SNAKE_CASE   :      List[Any]=0.1	,    __SCREAMING_SNAKE_CASE   :      Tuple=512	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=16	,    __SCREAMING_SNAKE_CASE   :      Dict=2	,    __SCREAMING_SNAKE_CASE   :      List[Any]=0.02	,    __SCREAMING_SNAKE_CASE   :      List[Any]=3	,    __SCREAMING_SNAKE_CASE   :      List[Any]=4	,    __SCREAMING_SNAKE_CASE   :      Dict=None	,    )      ->      Tuple:
							a_		:	Any									=   parent
							a_		:	List[str]									=   13
							a_		:	Dict									=   7
							a_		:	List[str]									=   True
							a_		:	str									=   True
							a_		:	List[str]									=   True
							a_		:	Any									=   True
							a_		:	List[Any]									=   99
							a_		:	Optional[Any]									=   384
							a_		:	List[str]									=   2
							a_		:	Optional[int]									=   4
							a_		:	Optional[int]									=   37
							a_		:	Union[str, Any]									=   '''gelu'''
							a_		:	List[str]									=   0.1
							a_		:	str									=   0.1
							a_		:	Optional[Any]									=   512
							a_		:	Union[str, Any]									=   16
							a_		:	Optional[Any]									=   2
							a_		:	Optional[int]									=   0.02
							a_		:	Union[str, Any]									=   3
							a_		:	Union[str, Any]									=   4
							a_		:	List[Any]									=   128
							a_		:	Optional[Any]									=   2
							a_		:	Any									=   9
							a_		:	Optional[int]									=   1
							a_		:	int									=   None






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Union[str, Any]:
							a_		:	Dict									=   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size       )

							a_		:	int									=   None
							if self.use_input_mask:
											a_		:	Dict									=   random_attention_mask([self.batch_size, self.seq_length]       )

							a_		:	Optional[int]									=   None
							if self.use_token_type_ids:
											a_		:	Any									=   ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size       )

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

							a_		:	Union[str, Any]									=   ConvBertConfig(
							    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	,    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       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      Union[str, Any]:
							a_		:	Tuple									=   TFConvBertModel(config=__SCREAMING_SNAKE_CASE       )
							a_		:	str									=   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

							a_		:	Optional[Any]									=   [input_ids, input_mask]
							a_		:	Tuple									=   model(__SCREAMING_SNAKE_CASE       )

							a_		:	int									=   model(__SCREAMING_SNAKE_CASE       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Dict       )      ->      str:
							a_		:	List[Any]									=   TFConvBertForMaskedLM(config=__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}
							a_		:	str									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      Any:
							a_		:	Union[str, Any]									=   self.num_labels
							a_		:	List[str]									=   TFConvBertForSequenceClassification(config=__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							a_		:	Tuple									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   self.num_choices
							a_		:	int									=   TFConvBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE       )
							a_		:	List[str]									=   tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE	,    1       )	,    (1, self.num_choices, 1)       )
							a_		:	Tuple									=   tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE	,    1       )	,    (1, self.num_choices, 1)       )
							a_		:	List[Any]									=   tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE	,    1       )	,    (1, self.num_choices, 1)       )
							a_		:	Union[str, Any]									=   {
							    '''input_ids''': multiple_choice_inputs_ids,
							    '''attention_mask''': multiple_choice_input_mask,
							    '''token_type_ids''': multiple_choice_token_type_ids,
							}
							a_		:	Optional[int]									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_choices)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							a_		:	Optional[Any]									=   self.num_labels
							a_		:	Optional[int]									=   TFConvBertForTokenClassification(config=__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}
							a_		:	Tuple									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Tuple:
							a_		:	List[Any]									=   TFConvBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							a_		:	Dict									=   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       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Any:
							a_		:	List[Any]									=   self.prepare_config_and_inputs()
							(
							    (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      (
							    a_
							)					,      
							)		:	List[Any]									=   config_and_inputs
							a_		:	Any									=   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict





@require_tf
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					(
			    (
			        TFConvBertModel,
			        TFConvBertForMaskedLM,
			        TFConvBertForQuestionAnswering,
			        TFConvBertForSequenceClassification,
			        TFConvBertForTokenClassification,
			        TFConvBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			snake_case__	=					(
			    {
			        "feature-extraction": TFConvBertModel,
			        "fill-mask": TFConvBertForMaskedLM,
			        "question-answering": TFConvBertForQuestionAnswering,
			        "text-classification": TFConvBertForSequenceClassification,
			        "token-classification": TFConvBertForTokenClassification,
			        "zero-shot": TFConvBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			snake_case__	=					False
			snake_case__	=					False
			snake_case__	=					False






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[Any]:
							a_		:	Optional[int]									=   TFConvBertModelTester(self       )
							a_		:	Any									=   ConfigTester(self	,    config_class=__SCREAMING_SNAKE_CASE	,    hidden_size=37       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							self.config_tester.run_common_tests()






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      str:
							a_		:	Any									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      List[str]:
							a_		:	str									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      str:
							a_		:	Dict									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Optional[Any]									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      str:
							a_		:	List[str]									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[int]:
							a_		:	Any									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE       )






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Union[str, Any]:
							a_					,      a_		:	Optional[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()
							a_		:	Any									=   True
							a_		:	Optional[int]									=   True

							if hasattr(__SCREAMING_SNAKE_CASE	,    '''use_cache'''       ):
											a_		:	Dict									=   True

							a_		:	Any									=   getattr(self.model_tester	,    '''encoder_seq_length'''	,    self.model_tester.seq_length       )
							a_		:	List[Any]									=   getattr(self.model_tester	,    '''key_length'''	,    __SCREAMING_SNAKE_CASE       )

							for model_class in self.all_model_classes:
											a_		:	str									=   self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
											a_		:	Optional[int]									=   model_class(__SCREAMING_SNAKE_CASE       )
											a_		:	Any									=   len(model(__SCREAMING_SNAKE_CASE       )       )

											with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__SCREAMING_SNAKE_CASE	,    saved_model=__SCREAMING_SNAKE_CASE       )
															a_		:	Union[str, Any]									=   os.path.join(__SCREAMING_SNAKE_CASE	,    '''saved_model'''	,    '''1'''       )
															a_		:	Optional[int]									=   tf.keras.models.load_model(__SCREAMING_SNAKE_CASE       )
															a_		:	Any									=   model(__SCREAMING_SNAKE_CASE       )

															if self.is_encoder_decoder:
																			a_		:	Optional[int]									=   outputs['''encoder_hidden_states''']
																			a_		:	Dict									=   outputs['''encoder_attentions''']
															else:
																			a_		:	Union[str, Any]									=   outputs['''hidden_states''']
																			a_		:	Tuple									=   outputs['''attentions''']

															self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    __SCREAMING_SNAKE_CASE       )

															a_		:	Union[str, Any]									=   getattr(
															    self.model_tester	,    '''expected_num_hidden_layers'''	,    self.model_tester.num_hidden_layers + 1       )

															self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    __SCREAMING_SNAKE_CASE       )
															self.assertListEqual(
															    list(output_hidden_states[0].shape[-2:]       )	,    [self.model_tester.seq_length, self.model_tester.hidden_size]	,    )

															self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    self.model_tester.num_hidden_layers       )
															self.assertListEqual(
															    list(output_attentions[0].shape[-3:]       )	,    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]	,    )






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[int]:
							a_		:	Tuple									=   TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''       )
							self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_					,      a_		:	Union[str, Any]									=   self.model_tester.prepare_config_and_inputs_for_common()
							a_		:	Optional[Any]									=   True
							a_		:	Dict									=   getattr(self.model_tester	,    '''decoder_seq_length'''	,    self.model_tester.seq_length       )
							a_		:	List[str]									=   getattr(self.model_tester	,    '''encoder_seq_length'''	,    self.model_tester.seq_length       )
							a_		:	Dict									=   getattr(self.model_tester	,    '''key_length'''	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   getattr(self.model_tester	,    '''key_length'''	,    __SCREAMING_SNAKE_CASE       )

							def check_decoder_attentions_output(__SCREAMING_SNAKE_CASE   :      List[str]       ):
											a_		:	int									=   len(__SCREAMING_SNAKE_CASE       )
											self.assertEqual(out_len % 2	,    0       )
											a_		:	Dict									=   outputs.decoder_attentions
											self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    self.model_tester.num_hidden_layers       )
											self.assertListEqual(
											    list(decoder_attentions[0].shape[-3:]       )	,    [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]	,    )

							def check_encoder_attentions_output(__SCREAMING_SNAKE_CASE   :      List[str]       ):
											a_		:	Optional[int]									=   [
											    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
											]
											self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    self.model_tester.num_hidden_layers       )
											self.assertListEqual(
											    list(attentions[0].shape[-3:]       )	,    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]	,    )

							for model_class in self.all_model_classes:
											a_		:	Tuple									=   True
											a_		:	Tuple									=   False
											a_		:	Optional[Any]									=   model_class(__SCREAMING_SNAKE_CASE       )
											a_		:	List[Any]									=   model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )
											a_		:	Union[str, Any]									=   len(__SCREAMING_SNAKE_CASE       )
											self.assertEqual(config.output_hidden_states	,    __SCREAMING_SNAKE_CASE       )
											check_encoder_attentions_output(__SCREAMING_SNAKE_CASE       )

											if self.is_encoder_decoder:
															a_		:	Any									=   model_class(__SCREAMING_SNAKE_CASE       )
															a_		:	List[Any]									=   model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )
															self.assertEqual(config.output_hidden_states	,    __SCREAMING_SNAKE_CASE       )
															check_decoder_attentions_output(__SCREAMING_SNAKE_CASE       )

											# Check that output attentions can also be changed via the config
											del inputs_dict["output_attentions"]
											a_		:	Union[str, Any]									=   True
											a_		:	Union[str, Any]									=   model_class(__SCREAMING_SNAKE_CASE       )
											a_		:	int									=   model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )
											self.assertEqual(config.output_hidden_states	,    __SCREAMING_SNAKE_CASE       )
											check_encoder_attentions_output(__SCREAMING_SNAKE_CASE       )

											# Check attention is always last and order is fine
											a_		:	Dict									=   True
											a_		:	str									=   True
											a_		:	int									=   model_class(__SCREAMING_SNAKE_CASE       )
											a_		:	List[Any]									=   model(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )

											self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)	,    len(__SCREAMING_SNAKE_CASE       )       )
											self.assertEqual(model.config.output_hidden_states	,    __SCREAMING_SNAKE_CASE       )
											check_encoder_attentions_output(__SCREAMING_SNAKE_CASE       )





@require_tf
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Any:
							a_		:	int									=   TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''       )
							a_		:	List[Any]									=   tf.constant([[0, 1, 2, 3, 4, 5]]       )
							a_		:	Any									=   model(__SCREAMING_SNAKE_CASE       )[0]

							a_		:	str									=   [1, 6, 768]
							self.assertEqual(output.shape	,    __SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   tf.constant(
							    [
							        [
							            [-0.0347_5493, -0.468_6034, -0.3063_8832],
							            [0.2263_7248, -0.2698_8646, -0.742_3424],
							            [0.1032_4868, -0.4501_3508, -0.5828_0784],
							        ]
							    ]       )
							tf.debugging.assert_near(output[:, :3, :3]	,    __SCREAMING_SNAKE_CASE	,    atol=1e-4       )




