
'''simple docstring'''


import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
					    SpeechaTextaDecoder,
					    SpeechaTextaForCausalLM,
					)

@require_torch
class 		__lowercase			:

			def __init__(self     ,				A     ,				A=9_9     ,				A=1_3     ,				A=1_6     ,				A=7     ,				A=True     ,				A=True     ,				A=True     ,				A=False     ,				A=True     ,				A=2     ,				A=3_2     ,				A=4     ,				A=4     ,				A=3_0     ,				A=0     ,				A=1     ,				A=2     ,				A=None     ,				):
								lowerCamelCase_   : Union[str, Any]       =						parent
								lowerCamelCase_   : Tuple       =						batch_size
								lowerCamelCase_   : Union[str, Any]       =						decoder_seq_length
								# For common tests
								lowerCamelCase_   : List[str]       =						self.decoder_seq_length
								lowerCamelCase_   : Optional[Any]       =						is_training
								lowerCamelCase_   : Optional[int]       =						use_attention_mask
								lowerCamelCase_   : Any       =						use_labels

								lowerCamelCase_   : Optional[Any]       =						vocab_size
								lowerCamelCase_   : Union[str, Any]       =						d_model
								lowerCamelCase_   : List[Any]       =						d_model
								lowerCamelCase_   : List[Any]       =						decoder_layers
								lowerCamelCase_   : str       =						decoder_layers
								lowerCamelCase_   : Any       =						decoder_ffn_dim
								lowerCamelCase_   : int       =						decoder_attention_heads
								lowerCamelCase_   : Dict       =						decoder_attention_heads
								lowerCamelCase_   : Optional[int]       =						eos_token_id
								lowerCamelCase_   : Any       =						bos_token_id
								lowerCamelCase_   : Optional[Any]       =						pad_token_id
								lowerCamelCase_   : Optional[Any]       =						decoder_start_token_id
								lowerCamelCase_   : List[str]       =						use_cache
								lowerCamelCase_   : Dict       =						max_position_embeddings

								lowerCamelCase_   : Optional[Any]       =						None
								lowerCamelCase_   : Any       =						decoder_seq_length
								lowerCamelCase_   : Any       =						2
								lowerCamelCase_   : Tuple       =						1

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						ids_tensor([self.batch_size, self.decoder_seq_length]     ,				self.vocab_size )

								lowerCamelCase_   : Dict       =						None
								if self.use_attention_mask:
													lowerCamelCase_   : Tuple       =						ids_tensor([self.batch_size, self.decoder_seq_length]     ,				vocab_size=2 )

								lowerCamelCase_   : List[Any]       =						None
								if self.use_labels:
													lowerCamelCase_   : List[Any]       =						ids_tensor([self.batch_size, self.decoder_seq_length]     ,				self.vocab_size )

								lowerCamelCase_   : str       =						SpeechaTextaConfig(
								    vocab_size=self.vocab_size     ,				d_model=self.d_model     ,				decoder_layers=self.decoder_layers     ,				decoder_ffn_dim=self.decoder_ffn_dim     ,				decoder_attention_heads=self.decoder_attention_heads     ,				eos_token_id=self.eos_token_id     ,				bos_token_id=self.bos_token_id     ,				use_cache=self.use_cache     ,				pad_token_id=self.pad_token_id     ,				decoder_start_token_id=self.decoder_start_token_id     ,				max_position_embeddings=self.max_position_embeddings     ,				)

								return (
								    config,
								    input_ids,
								    attention_mask,
								    lm_labels,
								)

			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				):
								lowerCamelCase_   : Tuple       =						True
								lowerCamelCase_   : Optional[Any]       =						SpeechaTextaDecoder(config=A ).to(A ).eval()
								lowerCamelCase_   : Optional[Any]       =						input_ids[:2]

								input_ids[input_ids == 0] += 1
								# first forward pass
								lowerCamelCase_   : Optional[Any]       =						model(A     ,				use_cache=A )
								lowerCamelCase_   : List[str]       =						model(A )
								lowerCamelCase_   : str       =						model(A     ,				use_cache=A )

								self.parent.assertTrue(len(A ) == len(A ) )
								self.parent.assertTrue(len(A ) == len(A ) + 1 )

								lowerCamelCase_   : Dict       =						outputs['''past_key_values''']

								# create hypothetical next token and extent to next_input_ids
								lowerCamelCase_   : Tuple       =						ids_tensor((2, 1)     ,				config.vocab_size - 1 ) + 1

								# append to next input_ids and
								lowerCamelCase_   : Tuple       =						torch.cat([input_ids, next_tokens]     ,				dim=-1 )

								print(A )

								lowerCamelCase_   : Optional[Any]       =						model(A )['''last_hidden_state''']
								lowerCamelCase_   : Union[str, Any]       =						model(A     ,				past_key_values=A )['''last_hidden_state''']

								# select random slice
								lowerCamelCase_   : Optional[Any]       =						ids_tensor((1,)     ,				output_from_past.shape[-1] ).item()
								lowerCamelCase_   : Union[str, Any]       =						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
								lowerCamelCase_   : Optional[Any]       =						output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								assert torch.allclose(A     ,				A     ,				atol=1E-3 )






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						self.prepare_config_and_inputs()
								(
								    (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), (
								    lowerCamelCase_
								), 
								)   : Optional[int]       =						config_and_inputs

								lowerCamelCase_   : int       =						{
								    '''input_ids''': input_ids,
								    '''attention_mask''': attention_mask,
								}
								return config, inputs_dict

@require_torch
class 		__lowercase			(					_lowercase ,       _lowercase ,       _lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						int		 =     (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
			lowerCamelCase     :						List[str]		 =     (SpeechaTextaForCausalLM,) if is_torch_available() else ()
			lowerCamelCase     :						Optional[Any]		 =     {"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
			lowerCamelCase     :						int		 =     True
			lowerCamelCase     :						Optional[int]		 =     False

			def     UpperCAmelCase__	(self     ,				):
								lowerCamelCase_   : str       =						SpeechaTextaStandaloneDecoderModelTester(self     ,				is_training=A )
								lowerCamelCase_   : Tuple       =						ConfigTester(self     ,				config_class=A )

			def     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								pass

			def     UpperCAmelCase__	(self ):
								pass

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

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past(*A )






			def     UpperCAmelCase__	(self ):
								return




'''simple docstring'''


def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					if not isinstance(_lowercase     ,    _lowercase			):
										raise TypeError('''recursive_lucas_number accepts only integer arguments.'''			)
					if n_th_number == 0:
										return 2
					if n_th_number == 1:
										return 1

					return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
					    n_th_number - 2			)




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					if not isinstance(_lowercase     ,    _lowercase			):
										raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''			)
					lowerCamelCase_, lowerCamelCase_   : List[Any]       =						2, 1
					for _ in range(_lowercase			):
										lowerCamelCase_, lowerCamelCase_   : Dict       =						b, a + b
					return a


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					__lowercase							:				Any					=       int(input('''Enter the number of terms in lucas series:\n''').strip())
					print('''Using recursive function to calculate lucas series:''')
					print(''' '''.join(str(recursive_lucas_number(i)) for i in range(n)))
					print('''\nUsing dynamic function to calculate lucas series:''')
					print(''' '''.join(str(dynamic_lucas_number(i)) for i in range(n)))


