






"""simple docstring"""





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class       __A		(   SCREAMING_SNAKE_CASE_			):
					# to overwrite at feature extractactor specific tests
					UpperCAmelCase__								=				None
					UpperCAmelCase__								=				None

					@property
					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											return self.feat_extract_tester.prepare_feat_extract_dict()

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Any:
											__magic_name__:	Dict							=       self.feature_extraction_class(**self.feat_extract_dict					)
											self.assertTrue(hasattr(__snake_case     ,		"""feature_size"""					)					)
											self.assertTrue(hasattr(__snake_case     ,		"""sampling_rate"""					)					)
											self.assertTrue(hasattr(__snake_case     ,		"""padding_value"""					)					)

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											__magic_name__:	List[Any]							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	List[str]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	str							=       feat_extract.model_input_names[0]

											__magic_name__:	Any							=       BatchFeature({input_name: speech_inputs}					)

											self.assertTrue(all(len(__snake_case					) == len(__snake_case					) for x, y in zip(__snake_case     ,		processed_features[input_name]					)					)					)

											__magic_name__:	List[Any]							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case					)
											__magic_name__:	str							=       BatchFeature({input_name: speech_inputs}     ,		tensor_type="""np"""					)

											__magic_name__:	List[str]							=       processed_features[input_name]

											if len(batch_features_input.shape					) < 3:
																	__magic_name__:	Optional[int]							=       batch_features_input[:, :, None]

											self.assertTrue(
											    batch_features_input.shape
											    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.feature_size)					)

					@require_torch
					def   lowerCamelCase__   (			self  :      Tuple					)			->							List[Any]:
											__magic_name__:	Tuple							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case					)
											__magic_name__:	Optional[int]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Dict							=       feat_extract.model_input_names[0]

											__magic_name__:	Optional[int]							=       BatchFeature({input_name: speech_inputs}     ,		tensor_type="""pt"""					)

											__magic_name__:	Union[str, Any]							=       processed_features[input_name]

											if len(batch_features_input.shape					) < 3:
																	__magic_name__:	int							=       batch_features_input[:, :, None]

											self.assertTrue(
											    batch_features_input.shape
											    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.feature_size)					)

					@require_tf
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							Optional[int]:
											__magic_name__:	Dict							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=__snake_case					)
											__magic_name__:	Optional[int]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Optional[Any]							=       feat_extract.model_input_names[0]

											__magic_name__:	Tuple							=       BatchFeature({input_name: speech_inputs}     ,		tensor_type="""tf"""					)

											__magic_name__:	Optional[int]							=       processed_features[input_name]

											if len(batch_features_input.shape					) < 3:
																	__magic_name__:	Any							=       batch_features_input[:, :, None]

											self.assertTrue(
											    batch_features_input.shape
											    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.feature_size)					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Optional[Any]=False					)			->							Any:
											def _inputs_have_equal_length(__snake_case  :      int					):
																	__magic_name__:	Union[str, Any]							=       len(input[0]					)
																	for input_slice in input[1:]:
																							if len(__snake_case					) != length:
																													return False
																	return True

											def _inputs_are_equal(__snake_case  :      Tuple     ,		__snake_case  :      List[str]					):
																	if len(__snake_case					) != len(__snake_case					):
																							return False

																	for input_slice_a, input_slice_a in zip(__snake_case     ,		__snake_case					):
																							if not np.allclose(np.asarray(__snake_case					)     ,		np.asarray(__snake_case					)     ,		atol=1E-3					):
																													return False
																	return True

											__magic_name__:	str							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Any							=       self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case					)
											__magic_name__:	Tuple							=       feat_extract.model_input_names[0]

											__magic_name__:	Tuple							=       BatchFeature({input_name: speech_inputs}					)

											__magic_name__:	List[str]							=       self.feat_extract_tester.seq_length_diff
											__magic_name__:	Optional[Any]							=       self.feat_extract_tester.max_seq_length + pad_diff
											__magic_name__:	Tuple							=       self.feat_extract_tester.min_seq_length
											__magic_name__:	List[str]							=       self.feat_extract_tester.batch_size
											__magic_name__:	int							=       self.feat_extract_tester.feature_size

											# test padding for List[int] + numpy
											__magic_name__:	Union[str, Any]							=       feat_extract.pad(__snake_case     ,		padding=__snake_case					)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

											__magic_name__:	str							=       feat_extract.pad(__snake_case     ,		padding="""longest"""					)
											__magic_name__:	str							=       input_a[input_name]

											__magic_name__:	Optional[int]							=       feat_extract.pad(__snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[-1]					)					)
											__magic_name__:	Any							=       input_a[input_name]

											__magic_name__:	str							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""np"""					)
											__magic_name__:	Any							=       input_a[input_name]

											# max_length parameter has to be provided when setting `padding="max_length"`
											with self.assertRaises(__snake_case					):
																	feat_extract.pad(__snake_case     ,		padding="""max_length"""					)[input_name]

											__magic_name__:	Union[str, Any]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=__snake_case     ,		return_tensors="""np"""					)
											__magic_name__:	Tuple							=       input_a[input_name]

											self.assertFalse(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_are_equal(__snake_case     ,		__snake_case					)					)
											self.assertTrue(len(input_a[0]					) == pad_min_length					)
											self.assertTrue(len(input_a[1]					) == pad_min_length + pad_diff					)
											self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]					))					)
											self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)					)

											if feature_size > 1:
																	self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size					)

											# test padding for `pad_to_multiple_of` for List[int] + numpy
											__magic_name__:	Optional[int]							=       feat_extract.pad(__snake_case     ,		pad_to_multiple_of=1_0					)
											__magic_name__:	int							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		pad_to_multiple_of=1_0					)
											__magic_name__:	List[Any]							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		pad_to_multiple_of=1_0     ,		max_length=__snake_case					)
											__magic_name__:	int							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		pad_to_multiple_of=1_0     ,		max_length=__snake_case     ,		return_tensors="""np"""     ,		)
											__magic_name__:	List[Any]							=       input_a[input_name]

											self.assertTrue(all(len(__snake_case					) % 1_0 == 0 for x in input_a					)					)
											self.assertTrue(_inputs_are_equal(__snake_case     ,		__snake_case					)					)

											__magic_name__:	Tuple							=       pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
											self.assertTrue(all(len(__snake_case					) == expected_mult_pad_length for x in input_a					)					)
											self.assertEqual(input_a.shape[:2]     ,		(batch_size, expected_mult_pad_length)					)

											if feature_size > 1:
																	self.assertTrue(input_a.shape[2] == feature_size					)

											# Check padding value is correct
											__magic_name__:	Optional[int]							=       (np.ones(self.feat_extract_tester.feature_size					) * feat_extract.padding_value).sum()
											self.assertTrue(
											    abs(np.asarray(input_a[0]					)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)					)
											    < 1E-3					)
											self.assertTrue(
											    abs(
											        np.asarray(input_a[1]					)[pad_min_length + pad_diff :].sum()
											        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)					)
											    < 1E-3					)
											self.assertTrue(
											    abs(
											        np.asarray(input_a[2]					)[pad_min_length + 2 * pad_diff :].sum()
											        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)					)
											    < 1E-3					)
											self.assertTrue(
											    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)					) < 1E-3					)
											self.assertTrue(
											    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)					)
											    < 1E-3					)

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      Optional[int]=False					)			->							Optional[Any]:
											def _inputs_have_equal_length(__snake_case  :      Union[str, Any]					):
																	__magic_name__:	Tuple							=       len(input[0]					)
																	for input_slice in input[1:]:
																							if len(__snake_case					) != length:
																													return False
																	return True

											def _inputs_are_equal(__snake_case  :      List[str]     ,		__snake_case  :      Optional[int]					):
																	if len(__snake_case					) != len(__snake_case					):
																							return False

																	for input_slice_a, input_slice_a in zip(__snake_case     ,		__snake_case					):
																							if not np.allclose(np.asarray(__snake_case					)     ,		np.asarray(__snake_case					)     ,		atol=1E-3					):
																													return False
																	return True

											__magic_name__:	List[Any]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	List[str]							=       self.feat_extract_tester.prepare_inputs_for_common(numpify=__snake_case					)
											__magic_name__:	str							=       feat_extract.model_input_names[0]

											__magic_name__:	Optional[int]							=       BatchFeature({input_name: speech_inputs}					)

											# truncate to smallest
											__magic_name__:	Optional[int]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		truncation=__snake_case					)
											__magic_name__:	int							=       input_a[input_name]

											__magic_name__:	Optional[int]							=       feat_extract.pad(__snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)					)
											__magic_name__:	Dict							=       input_a[input_name]

											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertFalse(_inputs_have_equal_length(__snake_case					)					)

											# truncate to smallest with np
											__magic_name__:	Union[str, Any]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		return_tensors="""np"""     ,		truncation=__snake_case     ,		)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

											__magic_name__:	Dict							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		return_tensors="""np"""					)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(input_a.shape[1] == len(speech_inputs[0]					)					)

											# since truncation forces padding to be smaller than longest input
											# function can't return `np.ndarray`, but has to return list
											self.assertFalse(_inputs_have_equal_length(__snake_case					)					)

											# truncate to middle
											__magic_name__:	List[str]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[1]					)     ,		truncation=__snake_case     ,		return_tensors="""np"""     ,		)
											__magic_name__:	Tuple							=       input_a[input_name]

											__magic_name__:	Optional[int]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[1]					)     ,		truncation=__snake_case					)
											__magic_name__:	Any							=       input_a[input_name]

											__magic_name__:	Tuple							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[1]					)     ,		return_tensors="""np"""					)
											__magic_name__:	Tuple							=       input_a[input_name]

											self.assertTrue(input_a.shape[1] == len(speech_inputs[1]					)					)
											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(_inputs_are_equal(__snake_case     ,		__snake_case					)					)

											# since truncation forces padding to be smaller than longest input
											# function can't return `np.ndarray`, but has to return list
											self.assertFalse(_inputs_have_equal_length(__snake_case					)					)
											self.assertTrue(len(input_a[-1]					) == len(speech_inputs[-1]					)					)

											# padding has to be max_length when setting `truncation=True`
											with self.assertRaises(__snake_case					):
																	feat_extract.pad(__snake_case     ,		truncation=__snake_case					)[input_name]

											# padding has to be max_length when setting `truncation=True`
											with self.assertRaises(__snake_case					):
																	feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		truncation=__snake_case					)[input_name]

											# padding has to be max_length when setting `truncation=True`
											with self.assertRaises(__snake_case					):
																	feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		truncation=__snake_case					)[input_name]

											# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
											with self.assertRaises(__snake_case					):
																	feat_extract.pad(__snake_case     ,		padding="""max_length"""     ,		truncation=__snake_case					)[input_name]

											# test truncation for `pad_to_multiple_of` for List[int] + numpy
											__magic_name__:	Tuple							=       1_2
											__magic_name__:	Optional[Any]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		pad_to_multiple_of=__snake_case     ,		truncation=__snake_case     ,		)
											__magic_name__:	Tuple							=       input_a[input_name]

											__magic_name__:	List[str]							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=len(speech_inputs[0]					)     ,		pad_to_multiple_of=__snake_case     ,		)
											__magic_name__:	Optional[Any]							=       input_a[input_name]

											# retrieve expected_length as multiple of pad_to_multiple_of
											__magic_name__:	List[Any]							=       len(speech_inputs[0]					)
											if expected_length % pad_to_multiple_of != 0:
																	__magic_name__:	List[Any]							=       ((len(speech_inputs[0]					) // pad_to_multiple_of) + 1) * pad_to_multiple_of

											self.assertTrue(len(input_a[0]					) == expected_length					)
											self.assertTrue(_inputs_have_equal_length(__snake_case					)					)
											self.assertFalse(_inputs_have_equal_length(__snake_case					)					)

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Optional[int]:
											self._check_padding(numpify=__snake_case					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Dict:
											self._check_padding(numpify=__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							int:
											self._check_truncation(numpify=__snake_case					)

					def   lowerCamelCase__   (			self  :      int					)			->							int:
											self._check_truncation(numpify=__snake_case					)

					@require_torch
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											__magic_name__:	List[Any]							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	Optional[Any]							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	Optional[int]							=       feat_extract.model_input_names[0]

											__magic_name__:	Optional[Any]							=       BatchFeature({input_name: speech_inputs}					)

											__magic_name__:	List[Any]							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""np"""					)[input_name]
											__magic_name__:	Any							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""pt"""					)[input_name]

											self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_pt.numpy().astype(np.floataa					).sum()					) < 1E-2					)

					@require_tf
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							int:
											__magic_name__:	Dict							=       self.feature_extraction_class(**self.feat_extract_dict					)
											__magic_name__:	str							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	Any							=       feat_extract.model_input_names[0]

											__magic_name__:	Union[str, Any]							=       BatchFeature({input_name: speech_inputs}					)

											__magic_name__:	int							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""np"""					)[input_name]
											__magic_name__:	Tuple							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""tf"""					)[input_name]

											self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_tf.numpy().astype(np.floataa					).sum()					) < 1E-2					)

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							int:
											__magic_name__:	Optional[Any]							=       self.feat_extract_dict
											__magic_name__:	Optional[Any]							=       True
											__magic_name__:	List[str]							=       self.feature_extraction_class(**__snake_case					)
											__magic_name__:	List[str]							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	Union[str, Any]							=       [len(__snake_case					) for x in speech_inputs]
											__magic_name__:	int							=       feat_extract.model_input_names[0]

											__magic_name__:	List[str]							=       BatchFeature({input_name: speech_inputs}					)

											__magic_name__:	Tuple							=       feat_extract.pad(__snake_case     ,		padding="""longest"""     ,		return_tensors="""np"""					)
											self.assertIn("""attention_mask"""     ,		__snake_case					)
											self.assertListEqual(list(processed.attention_mask.shape					)     ,		list(processed[input_name].shape[:2]					)					)
											self.assertListEqual(processed.attention_mask.sum(-1					).tolist()     ,		__snake_case					)






					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Tuple:
											__magic_name__:	Any							=       self.feat_extract_dict
											__magic_name__:	Optional[Any]							=       True
											__magic_name__:	str							=       self.feature_extraction_class(**__snake_case					)
											__magic_name__:	Optional[Any]							=       self.feat_extract_tester.prepare_inputs_for_common()
											__magic_name__:	str							=       [len(__snake_case					) for x in speech_inputs]
											__magic_name__:	Union[str, Any]							=       feat_extract.model_input_names[0]

											__magic_name__:	Optional[int]							=       BatchFeature({input_name: speech_inputs}					)
											__magic_name__:	Union[str, Any]							=       min(__snake_case					)

											__magic_name__:	Dict							=       feat_extract.pad(
											    __snake_case     ,		padding="""max_length"""     ,		max_length=__snake_case     ,		truncation=__snake_case     ,		return_tensors="""np"""					)
											self.assertIn("""attention_mask"""     ,		__snake_case					)
											self.assertListEqual(
											    list(processed_pad.attention_mask.shape					)     ,		[processed_pad[input_name].shape[0], max_length]					)
											self.assertListEqual(
											    processed_pad.attention_mask[:, :max_length].sum(-1					).tolist()     ,		[max_length for x in speech_inputs]					)










import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

									lowercase_	:   Optional[int]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Any  				=     TaConfig(
									    vocab_size=snake_case__,   d_model=snake_case__,   num_heads=snake_case__,   d_kv=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__,   feed_forward_proj=snake_case__,   is_decoder=snake_case__,   is_encoder_decoder=snake_case__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.dropout_pre(snake_case__  )

									# inverted the attention mask
									lowercase_	:   List[str]  				=     encoder_input_tokens.size()
									lowercase_	:   Union[str, Any]  				=     self.get_extended_attention_mask(snake_case__,   snake_case__  )

									for lyr in self.encoders:
																lowercase_	:   Optional[Any]  				=     lyr(snake_case__,   snake_case__  )[0]
									lowercase_	:   Dict  				=     self.layer_norm(snake_case__  )

									return self.dropout_post(snake_case__  ), encoder_inputs_mask