


"""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 snake_case   :







		def __init__(       self   :							Union[str, Any]     , a__   :							List[Any]     , a__   :							Union[str, Any]=99     , a__   :							Union[str, Any]=13     , a__   :							str=16     , a__   :							List[str]=7     , a__   :							List[str]=True     , a__   :							Optional[Any]=True     , a__   :							int=True     , a__   :							Union[str, Any]=False     , a__   :							Optional[int]=True     , a__   :							Dict=2     , a__   :							List[str]=32     , a__   :							List[Any]=4     , a__   :							Dict=4     , a__   :							List[Any]=30     , a__   :							Optional[Any]=0     , a__   :							Optional[int]=1     , a__   :							Tuple=2     , a__   :							Optional[int]=None     , )							->    List[str]:





						'''simple docstring'''







						_A       	=      parent
						_A       	=      batch_size
						_A       	=      decoder_seq_length
						# For common tests
						_A       	=      self.decoder_seq_length
						_A       	=      is_training
						_A       	=      use_attention_mask
						_A       	=      use_labels

						_A       	=      vocab_size
						_A       	=      d_model
						_A       	=      d_model
						_A       	=      decoder_layers
						_A       	=      decoder_layers
						_A       	=      decoder_ffn_dim
						_A       	=      decoder_attention_heads
						_A       	=      decoder_attention_heads
						_A       	=      eos_token_id
						_A       	=      bos_token_id
						_A       	=      pad_token_id
						_A       	=      decoder_start_token_id
						_A       	=      use_cache
						_A       	=      max_position_embeddings

						_A       	=      None
						_A       	=      decoder_seq_length
						_A       	=      2
						_A       	=      1







		def 			a_			(       self   :							int		)							->    Optional[Any]:





						'''simple docstring'''







						_A       	=      ids_tensor([self.batch_size, self.decoder_seq_length]     , self.vocab_size		)

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

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

						_A       	=      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 			a_			(       self   :							Optional[Any]     , a__   :							Union[str, Any]     , a__   :							Optional[int]     , a__   :							str     , a__   :							List[Any]     , )							->    List[Any]:





						'''simple docstring'''







						_A       	=      True
						_A       	=      SpeechaTextaDecoder(config=a__		).to(a__		).eval()
						_A       	=      input_ids[:2]

						input_ids[input_ids == 0] += 1
						# first forward pass
						_A       	=      model(a__     , use_cache=a__		)
						_A       	=      model(a__		)
						_A       	=      model(a__     , use_cache=a__		)

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

						_A       	=      outputs["past_key_values"]

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

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

						print(a__		)

						_A       	=      model(a__		)["last_hidden_state"]
						_A       	=      model(a__     , past_key_values=a__		)["last_hidden_state"]

						# select random slice
						_A       	=      ids_tensor((1,)     , output_from_past.shape[-1]		).item()
						_A       	=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
						_A       	=      output_from_past[:, 0, random_slice_idx].detach()

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







		def 			a_			(       self   :							Optional[Any]		)							->    Dict:





						'''simple docstring'''







						_A       	=      self.prepare_config_and_inputs()
						(
						    (
						    _A
						)	,		(
						    _A
						)	,		(
						    _A
						)	,		(
						    _A
						)	,		
						)       	=      config_and_inputs

						_A       	=      {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						}
						return config, inputs_dict





@require_torch
class snake_case   (	_UpperCamelCase  ,					_UpperCamelCase  ,					_UpperCamelCase  ,					unittest.TestCase):
		__UpperCamelCase							     =      (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
		__UpperCamelCase							     =      (SpeechaTextaForCausalLM,) if is_torch_available() else ()
		__UpperCamelCase							     =      {'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
		__UpperCamelCase							     =      True
		__UpperCamelCase							     =      False







		def 			a_			(       self   :							Tuple     , )							->    Any:





						'''simple docstring'''







						_A       	=      SpeechaTextaStandaloneDecoderModelTester(self     , is_training=a__		)
						_A       	=      ConfigTester(self     , config_class=a__		)







		def 			a_			(       self   :							Optional[Any]		)							->    Tuple:





						'''simple docstring'''







						pass







		def 			a_			(       self   :							str		)							->    int:





						'''simple docstring'''







						pass







		def 			a_			(       self   :							Dict		)							->    Optional[Any]:





						'''simple docstring'''







						pass







		def 			a_			(       self   :							Optional[Any]		)							->    Tuple:





						'''simple docstring'''







						self.config_tester.run_common_tests()







		def 			a_			(       self   :							Optional[int]		)							->    Tuple:





						'''simple docstring'''







						_A       	=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past(*a__		)







		def 			a_			(       self   :							Tuple		)							->    Any:





						'''simple docstring'''







						return



"""simple docstring"""


def   a__			(					__lowercase )						->						bool:
				# must be
				_A       	=      [list(__lowercase ) for row in matrix]
				if matrix and isinstance(__lowercase  ,      __lowercase ):
								if isinstance(matrix[0]  ,      __lowercase ):
												_A       	=      0
												for row in matrix:
																if prev_len == 0:
																				_A       	=      len(__lowercase )
																				_A       	=      True
																else:
																				_A       	=      prev_len == len(__lowercase )
								else:
												_A       	=      True
				else:
								_A       	=      False

				return result






def   a__			(					__lowercase )						->						None:
				if check_matrix(__lowercase ) and len(__lowercase ) > 0:
								_A       	=      [list(__lowercase ) for row in a]
								_A       	=      len(__lowercase )
								if isinstance(a[0]  ,      __lowercase ):
												_A       	=      len(a[0] )
								else:
												for dat in a:
																print(__lowercase )
												return

								# horizotal printing increasing
								for i in range(0  ,      __lowercase ):
												print(a[0][i] )
								# vertical printing down
								for i in range(1  ,      __lowercase ):
												print(a[i][mat_col - 1] )
								# horizotal printing decreasing
								if mat_row > 1:
												for i in range(mat_col - 2  ,      -1  ,      -1 ):
																print(a[mat_row - 1][i] )
        # vertical printing up
								for i in range(mat_row - 2  ,      0  ,      -1 ):
												print(a[i][0] )
								_A       	=      [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
								if len(__lowercase ) > 0:
												spiral_print_clockwise(__lowercase )
								else:
												return
				else:
								print("Not a valid matrix" )
								return






def   a__			(					__lowercase )						->						list[int]:
				if matrix:
								return list(matrix.pop(0 ) ) + spiral_traversal(list(zip(*__lowercase ) )[::-1] )
				else:
								return []


# driver code
if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a_											=     [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
		spiral_print_clockwise(a)