
"""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      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=99  ,							snake_case_=13  ,							snake_case_=16  ,							snake_case_=7  ,							snake_case_=True  ,							snake_case_=True  ,							snake_case_=True  ,							snake_case_=False  ,							snake_case_=True  ,							snake_case_=2  ,							snake_case_=32  ,							snake_case_=4  ,							snake_case_=4  ,							snake_case_=30  ,							snake_case_=0  ,							snake_case_=1  ,							snake_case_=2  ,							snake_case_=None  ,							)   ->       Tuple:
													__lowerCAmelCase        =       parent
													__lowerCAmelCase        =       batch_size
													__lowerCAmelCase        =       decoder_seq_length
													# For common tests
													__lowerCAmelCase        =       self.decoder_seq_length
													__lowerCAmelCase        =       is_training
													__lowerCAmelCase        =       use_attention_mask
													__lowerCAmelCase        =       use_labels

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

													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       decoder_seq_length
													__lowerCAmelCase        =       2
													__lowerCAmelCase        =       1




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       ids_tensor([self.batch_size, self.decoder_seq_length]  ,							self.vocab_size							)

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

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

													__lowerCAmelCase        =       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  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							)   ->       List[Any]:
													__lowerCAmelCase        =       True
													__lowerCAmelCase        =       SpeechaTextaDecoder(config=_UpperCAmelCase							).to(_UpperCAmelCase							).eval()
													__lowerCAmelCase        =       input_ids[:2]

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

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

													__lowerCAmelCase        =       outputs["past_key_values"]

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

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

													print(_UpperCAmelCase							)

													__lowerCAmelCase        =       model(_UpperCAmelCase							)["last_hidden_state"]
													__lowerCAmelCase        =       model(_UpperCAmelCase  ,							past_key_values=_UpperCAmelCase							)["last_hidden_state"]

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

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




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       self.prepare_config_and_inputs()
													(
													    __lowerCAmelCase 
													)        =       config_and_inputs

													__lowerCAmelCase        =       {
													    "input_ids": input_ids,
													    "attention_mask": attention_mask,
													}
													return config, inputs_dict
@require_torch
class      lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase				):



							'''simple docstring'''





							_snake_case		     =						(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
							_snake_case		     =						(SpeechaTextaForCausalLM,) if is_torch_available() else ()
							_snake_case		     =						{'''text-generation''': SpeechaTextaForCausalLM} if is_torch_available() else {}
							_snake_case		     =						True
							_snake_case		     =						False




							def  A__		(				self  ,							)   ->       Dict:
													__lowerCAmelCase        =       SpeechaTextaStandaloneDecoderModelTester(self  ,							is_training=_UpperCAmelCase							)
													__lowerCAmelCase        =       ConfigTester(self  ,							config_class=_UpperCAmelCase							)




							def  A__		(				self							)   ->       Tuple:
													pass




							def  A__		(				self							)   ->       Dict:
													pass




							def  A__		(				self							)   ->       Optional[Any]:
													pass




							def  A__		(				self							)   ->       str:
													self.config_tester.run_common_tests()




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past(*_UpperCAmelCase							)




							def  A__		(				self							)   ->       Optional[Any]:
													return


"""simple docstring"""
def 				lowercase      (_lowerCAmelCase		):
						# must be
						__lowerCAmelCase        =       [list(_lowerCAmelCase		) for row in matrix]
						if matrix and isinstance(_lowerCAmelCase				,  _lowerCAmelCase		):
												if isinstance(matrix[0]				,  _lowerCAmelCase		):
																		__lowerCAmelCase        =       0
																		for row in matrix:
																								if prev_len == 0:
																														__lowerCAmelCase        =       len(_lowerCAmelCase		)
																														__lowerCAmelCase        =       True
																								else:
																														__lowerCAmelCase        =       prev_len == len(_lowerCAmelCase		)
												else:
																		__lowerCAmelCase        =       True
						else:
												__lowerCAmelCase        =       False

						return result

def 				lowercase      (_lowerCAmelCase		):
						if check_matrix(_lowerCAmelCase		) and len(_lowerCAmelCase		) > 0:
												__lowerCAmelCase        =       [list(_lowerCAmelCase		) for row in a]
												__lowerCAmelCase        =       len(_lowerCAmelCase		)
												if isinstance(a[0]				,  _lowerCAmelCase		):
																		__lowerCAmelCase        =       len(a[0]		)
												else:
																		for dat in a:
																								print(_lowerCAmelCase		)
																		return

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

def 				lowercase      (_lowerCAmelCase		):
						if matrix:
												return list(matrix.pop(0		)		) + spiral_traversal(list(zip(*_lowerCAmelCase		)		)[::-1]		)
						else:
												return []


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

			doctest.testmod()

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