





"""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 					__UpperCAmelCase							(						_UpperCamelCase  ):
				# to overwrite at feature extractactor specific tests
				__lowerCamelCase				:	int				     =				None
				__lowerCamelCase				:	Tuple				     =				None
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							return self.feat_extract_tester.prepare_feat_extract_dict()
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							self.assertTrue(hasattr(a_	, "feature_size"    )    )
							self.assertTrue(hasattr(a_	, "sampling_rate"    )    )
							self.assertTrue(hasattr(a_	, "padding_value"    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Any		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Union[str, Any]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

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

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

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

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

							if len(batch_features_input.shape    ) < 3:
										a__  :	List[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)    )
				@require_torch
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	List[Any]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Optional[Any]		 =			feat_extract.model_input_names[0]

							a__  :	Any		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="pt"    )

							a__  :	Optional[int]		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	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)    )
				@require_tf
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[int]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	Optional[int]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	str		 =			feat_extract.model_input_names[0]

							a__  :	List[str]		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="tf"    )

							a__  :	Tuple		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	List[str]		 =			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       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[Any]=False    )	->	List[Any]:



							'''simple docstring'''



							def _inputs_have_equal_length(a_    :			int    ):
										a__  :	Any		 =			len(input[0]    )
										for input_slice in input[1:]:
													if len(a_    ) != length:
																return False
										return True

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

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

							a__  :	int		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	int		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	Tuple		 =			feat_extract.model_input_names[0]

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

							a__  :	Optional[int]		 =			self.feat_extract_tester.seq_length_diff
							a__  :	Optional[Any]		 =			self.feat_extract_tester.max_seq_length + pad_diff
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.min_seq_length
							a__  :	Tuple		 =			self.feat_extract_tester.batch_size
							a__  :	str		 =			self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							a__  :	Any		 =			feat_extract.pad(a_	, padding=a_    )
							a__  :	List[str]		 =			input_a[input_name]

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

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

							a__  :	Tuple		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

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

							a__  :	Tuple		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							self.assertFalse(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_are_equal(a_	, a_    )    )
							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
							a__  :	Optional[int]		 =			feat_extract.pad(a_	, pad_to_multiple_of=10    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	List[str]		 =			feat_extract.pad(a_	, padding="longest"	, pad_to_multiple_of=10    )
							a__  :	Dict		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_    )
							a__  :	Tuple		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_	, return_tensors="np"	, )
							a__  :	Optional[int]		 =			input_a[input_name]

							self.assertTrue(all(len(a_    ) % 10 == 0 for x in input_a    )    )
							self.assertTrue(_inputs_are_equal(a_	, a_    )    )

							a__  :	List[str]		 =			pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							self.assertTrue(all(len(a_    ) == 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
							a__  :	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       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[int]=False    )	->	List[str]:



							'''simple docstring'''



							def _inputs_have_equal_length(a_    :			List[Any]    ):
										a__  :	List[str]		 =			len(input[0]    )
										for input_slice in input[1:]:
													if len(a_    ) != length:
																return False
										return True

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

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

							a__  :	Dict		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

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

							a__  :	int		 =			feat_extract.pad(a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )    )
							a__  :	List[Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )

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

							a__  :	str		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, return_tensors="np"    )
							a__  :	Union[str, Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							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(a_    )    )

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

							a__  :	List[str]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, truncation=a_    )
							a__  :	str		 =			input_a[input_name]

							a__  :	Any		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, return_tensors="np"    )
							a__  :	Optional[int]		 =			input_a[input_name]

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

							# 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(a_    )    )
							self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

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

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

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

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

							# test truncation for `pad_to_multiple_of` for List[int] + numpy
							a__  :	Tuple		 =			12
							a__  :	Dict		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, pad_to_multiple_of=a_	, truncation=a_	, )
							a__  :	Optional[int]		 =			input_a[input_name]

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

							# retrieve expected_length as multiple of pad_to_multiple_of
							a__  :	Dict		 =			len(speech_inputs[0]    )
							if expected_length % pad_to_multiple_of != 0:
										a__  :	Tuple		 =			((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(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	int:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				@require_torch
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Tuple		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Tuple		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	int		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	str		 =			feat_extract.pad(a_	, 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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							a__  :	int		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Optional[int]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Optional[Any]		 =			feat_extract.model_input_names[0]

							a__  :	Dict		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	List[Any]		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	List[str]		 =			feat_extract.pad(a_	, 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       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			self.feat_extract_dict
							a__  :	Dict		 =			True
							a__  :	Dict		 =			self.feature_extraction_class(**a_    )
							a__  :	List[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	List[Any]		 =			[len(a_    ) for x in speech_inputs]
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	int		 =			BatchFeature({input_name: speech_inputs}    )

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






				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			self.feat_extract_dict
							a__  :	Any		 =			True
							a__  :	Optional[Any]		 =			self.feature_extraction_class(**a_    )
							a__  :	List[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Optional[int]		 =			[len(a_    ) for x in speech_inputs]
							a__  :	Tuple		 =			feat_extract.model_input_names[0]

							a__  :	int		 =			BatchFeature({input_name: speech_inputs}    )
							a__  :	Tuple		 =			min(a_    )

							a__  :	int		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, truncation=a_	, return_tensors="np"    )
							self.assertIn("attention_mask"	, a_    )
							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]    )






"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__UpperCAmelCase						=			logging.get_logger(__name__)






def      lowercase__ (					lowerCAmelCase__   :			Union[tf.Tensor, np.ndarray]			)    ->			List[int]:



			'''simple docstring'''
			if isinstance(lowerCAmelCase__							,			np.ndarray			):
						return list(tensor.shape			)

			a__  :	Optional[int]		 =			tf.shape(lowerCAmelCase__			)

			if tensor.shape == tf.TensorShape(lowerCAmelCase__			):
						return dynamic

			a__  :	Union[str, Any]		 =			tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(lowerCAmelCase__			)]






def      lowercase__ (					lowerCAmelCase__   :			tf.Tensor							,			lowerCAmelCase__   :			Optional[int] = None							,			lowerCAmelCase__   :			Optional[str] = None			)    ->			tf.Tensor:



			'''simple docstring'''
			return tf.nn.softmax(logits=logits + 1E-9							,			axis=lowerCAmelCase__							,			name=lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Union[str, Any]=1E-5							,			lowerCAmelCase__   :			str=-1			)    ->			int:



			'''simple docstring'''
			# This is a very simplified functional layernorm, designed to duplicate
			# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
			# models in Transformers.

			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCAmelCase__							,			lowerCAmelCase__			):
						raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."			)

			# Get mean and variance on the axis to be normalized
			a__ ,	a__  :	Dict		 =			tf.nn.moments(lowerCAmelCase__							,			axes=[axis]							,			keepdims=lowerCAmelCase__			)

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						a__  :	Dict		 =			[1] * inputs.shape.rank
						a__  :	Dict		 =			shape_list(lowerCAmelCase__			)[axis]
						a__  :	Any		 =			tf.reshape(lowerCAmelCase__							,			lowerCAmelCase__			)
						a__  :	List[Any]		 =			tf.reshape(lowerCAmelCase__							,			lowerCAmelCase__			)

			# Compute layer normalization using the batch_normalization
			# function.
			a__  :	List[str]		 =			tf.nn.batch_normalization(
			    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			offset=lowerCAmelCase__							,			scale=lowerCAmelCase__							,			variance_epsilon=lowerCAmelCase__							,			)
			return outputs






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Any=0							,			lowerCAmelCase__   :			List[str]=-1			)    ->			List[str]:



			'''simple docstring'''
			# Replicates the behavior of torch.flatten in TF

			# If end_dim or start_dim is negative, count them from the end
			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			a__  :	Optional[int]		 =			tf.shape(lowerCAmelCase__			)
			a__  :	str		 =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
			a__  :	Union[str, Any]		 =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]							,			axis=0			)
			return tf.reshape(lowerCAmelCase__							,			lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			tf.Tensor			)    ->			tf.Tensor:



			'''simple docstring'''
			if not isinstance(lowerCAmelCase__							,			tf.Tensor			):
						a__  :	Optional[Any]		 =			tf.convert_to_tensor(lowerCAmelCase__			)  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						a__  :	Tuple		 =			encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						a__  :	int		 =			encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			a__  :	int		 =			(
			    tf.cast(1							,			encoder_attention_mask.dtype			) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask






def      lowercase__ (					lowerCAmelCase__   :			tf.Tensor							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			str = "input_ids"			)    ->			None:



			'''simple docstring'''
			tf.debugging.assert_less(
			    lowerCAmelCase__							,			tf.cast(lowerCAmelCase__							,			dtype=tensor.dtype			)							,			message=(
			        F"The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCAmelCase__			)}) must be smaller than the embedding "
			        F"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
			    )							,			)






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			Dict			)    ->			Tuple:



			'''simple docstring'''
			a__  :	Optional[Any]		 =			6_4_5_1_2
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			a__  :	Optional[Any]		 =			[x for x in data if len(lowerCAmelCase__			) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    "The following attributes cannot be saved to HDF5 file because "
						    F"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
						    F"bytes: {bad_attributes}"			)

			a__  :	List[str]		 =			np.asarray(lowerCAmelCase__			)

			a__  :	List[str]		 =			1
			a__  :	str		 =			np.array_split(lowerCAmelCase__							,			lowerCAmelCase__			)

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
						num_chunks += 1
						a__  :	str		 =			np.array_split(lowerCAmelCase__							,			lowerCAmelCase__			)

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(lowerCAmelCase__			):
									a__  :	List[Any]		 =			chunk_data
			else:
						a__  :	List[str]		 =			data






def      lowercase__ (					lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Tuple			)    ->			Tuple:



			'''simple docstring'''
			if name in group.attrs:
						a__  :	str		 =			[n.decode("utf8"			) if hasattr(lowerCAmelCase__							,			"decode"			) else n for n in group.attrs[name]]
			else:
						a__  :	str		 =			[]
						a__  :	Any		 =			0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode("utf8"			) if hasattr(lowerCAmelCase__							,			"decode"			) else n for n in group.attrs["%s%d" % (name, chunk_id)]]			)
									chunk_id += 1
			return data






def      lowercase__ (					lowerCAmelCase__   :			Tuple			)    ->			List[Any]:



			'''simple docstring'''

			def _expand_single_ad_tensor(lowerCAmelCase__   :			List[str]			):
						if isinstance(lowerCAmelCase__							,			tf.Tensor			) and t.shape.rank == 1:
									return tf.expand_dims(lowerCAmelCase__							,			axis=-1			)
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor							,			lowerCAmelCase__			)