






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      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase=99  ,    _UpperCamelCase=13  ,    _UpperCamelCase=16  ,    _UpperCamelCase=7  ,    _UpperCamelCase=True  ,    _UpperCamelCase=True  ,    _UpperCamelCase=True  ,    _UpperCamelCase=False  ,    _UpperCamelCase=True  ,    _UpperCamelCase=2  ,    _UpperCamelCase=32  ,    _UpperCamelCase=4  ,    _UpperCamelCase=4  ,    _UpperCamelCase=30  ,    _UpperCamelCase=0  ,    _UpperCamelCase=1  ,    _UpperCamelCase=2  ,    _UpperCamelCase=None  ,    ):






												"""simple docstring"""
												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    UpperCamelCase__   (						self ):






												"""simple docstring"""
												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    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    ):






												"""simple docstring"""
												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    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.prepare_config_and_inputs()
												(
												    (
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							(
												    lowerCAmelCase__
												)    ,							
												)         =    config_and_inputs

												lowerCAmelCase__         =    {
												    'input_ids': input_ids,
												    'attention_mask': attention_mask,
												}
												return config, inputs_dict




@require_torch
class      __SCREAMING_SNAKE_CASE       ( __lowercase		,       __lowercase		,       __lowercase		,       unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE						:							Union[str, Any]				=       (SpeechaTextaForCausalLM,) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE						:							int				=       {'''text-generation''': SpeechaTextaForCausalLM} if is_torch_available() else {}
						_SCREAMING_SNAKE_CASE						:							Any				=       True
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       False






						def    UpperCamelCase__   (						self  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    SpeechaTextaStandaloneDecoderModelTester(self  ,    is_training=_UpperCamelCase )
												lowerCAmelCase__         =    ConfigTester(self  ,    config_class=_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												pass






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												pass






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												pass






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past(*_UpperCamelCase )







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return













import numpy as np


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase=5  ,    _UpperCamelCase=5 ):






												"""simple docstring"""
												lowerCAmelCase__         =    depth
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    None
												lowerCAmelCase__         =    min_leaf_size
												lowerCAmelCase__         =    None






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if labels.ndim != 1:
																		print('Error: Input labels must be one dimensional' )

												return np.mean((labels - prediction) ** 2 )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if x.ndim != 1:
																		print('Error: Input data set must be one dimensional' )
																		return
												if len(_UpperCamelCase ) != len(_UpperCamelCase ):
																		print('Error: X and y have different lengths' )
																		return
												if y.ndim != 1:
																		print('Error: Data set labels must be one dimensional' )
																		return

												if len(_UpperCamelCase ) < 2 * self.min_leaf_size:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												if self.depth == 1:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )
																		return

												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    self.mean_squared_error(_UpperCamelCase  ,    np.mean(_UpperCamelCase ) ) * 2
												for i in range(len(_UpperCamelCase ) ):
																		if len(x[:i] ) < self.min_leaf_size:
																								continue
																		elif len(x[i:] ) < self.min_leaf_size:
																								continue
																		else:
																								lowerCAmelCase__         =    self.mean_squared_error(x[:i]  ,    np.mean(y[:i] ) )
																								lowerCAmelCase__         =    self.mean_squared_error(x[i:]  ,    np.mean(y[i:] ) )
																								lowerCAmelCase__         =    error_left + error_right
																								if error < min_error:
																														lowerCAmelCase__         =    i
																														lowerCAmelCase__         =    error

												if best_split != 0:
																		lowerCAmelCase__         =    x[:best_split]
																		lowerCAmelCase__         =    y[:best_split]
																		lowerCAmelCase__         =    x[best_split:]
																		lowerCAmelCase__         =    y[best_split:]

																		lowerCAmelCase__         =    x[best_split]
																		lowerCAmelCase__         =    DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size )
																		lowerCAmelCase__         =    DecisionTree(
																		    depth=self.depth - 1  ,    min_leaf_size=self.min_leaf_size )
																		self.left.train(_UpperCamelCase  ,    _UpperCamelCase )
																		self.right.train(_UpperCamelCase  ,    _UpperCamelCase )
												else:
																		lowerCAmelCase__         =    np.mean(_UpperCamelCase )

												return







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												if self.prediction is not None:
																		return self.prediction
												elif self.left or self.right is not None:
																		if x >= self.decision_boundary:
																								return self.right.predict(_UpperCamelCase )
																		else:
																								return self.left.predict(_UpperCamelCase )
												else:
																		print('Error: Decision tree not yet trained' )
																		return None




class      __SCREAMING_SNAKE_CASE       :






						@staticmethod
						def    UpperCamelCase__   (						_UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    float(0 )
												for label in labels:
																		squared_error_sum += (label - prediction) ** 2

												return float(squared_error_sum / labels.size )







def 						_UpperCamelCase (     )	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    np.arange(-1.0     ,							1.0     ,							0.005		)
						lowerCAmelCase__         =    np.sin(UpperCamelCase_		)

						lowerCAmelCase__         =    DecisionTree(depth=10     ,							min_leaf_size=10		)
						tree.train(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    (np.random.rand(10		) * 2) - 1
						lowerCAmelCase__         =    np.array([tree.predict(UpperCamelCase_		) for x in test_cases]		)
						lowerCAmelCase__         =    np.mean((predictions - test_cases) ** 2		)

						print('Test values: ' + str(UpperCamelCase_		)		)
						print('Predictions: ' + str(UpperCamelCase_		)		)
						print('Average error: ' + str(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					import doctest

					doctest.testmod(name="""mean_squarred_error""", verbose=True)





