


'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__snake_case											=					logging.get_logger(__name__)


def       a				(      __a       )  ->     List[int]:



					'''simple docstring'''






					if isinstance(__a			, np.ndarray       ):
										return list(tensor.shape       )

					UpperCamelCase__       :int							 =			tf.shape(__a       )

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

					UpperCamelCase__       :int							 =			tensor.shape.as_list()

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


def       a				(      __a			, __a = None			, __a = None       )  ->     tf.Tensor:



					'''simple docstring'''






					return tf.nn.softmax(logits=logits + 1e-9			, axis=__a			, name=__a       )


def       a				(      __a			, __a			, __a			, __a=1e-5			, __a=-1       )  ->     Dict:



					'''simple docstring'''






					if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__a			, __a       ):
										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
					UpperCamelCase__ ,    UpperCamelCase__       :int							 =			tf.nn.moments(__a			, axes=[axis]			, keepdims=__a       )

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

					# Compute layer normalization using the batch_normalization
					# function.
					UpperCamelCase__       :Optional[Any]							 =			tf.nn.batch_normalization(
					    __a			, __a			, __a			, offset=__a			, scale=__a			, variance_epsilon=__a			, )
					return outputs


def       a				(      __a			, __a=0			, __a=-1       )  ->     Union[str, Any]:



					'''simple docstring'''






					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

					UpperCamelCase__       :Optional[Any]							 =			tf.shape(__a       )
					UpperCamelCase__       :Dict							 =			tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]       )
					UpperCamelCase__       :Dict							 =			tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			, axis=0       )
					return tf.reshape(__a			, __a       )


def       a				(      __a       )  ->     tf.Tensor:



					'''simple docstring'''






					if not isinstance(__a			, tf.Tensor       ):
										UpperCamelCase__       :int							 =			tf.convert_to_tensor(__a       )  # Catches stray NumPy inputs
					if encoder_attention_mask.shape.rank == 3:
										UpperCamelCase__       :Any							 =			encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.shape.rank == 2:
										UpperCamelCase__       :str							 =			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))
					UpperCamelCase__       :Optional[Any]							 =			(
					    tf.cast(1			, encoder_attention_mask.dtype       ) - encoder_extended_attention_mask
					) * encoder_extended_attention_mask.dtype.min

					return encoder_extended_attention_mask


def       a				(      __a			, __a			, __a = "input_ids"       )  ->     None:



					'''simple docstring'''






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


def       a				(      __a			, __a			, __a       )  ->     Dict:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			64512
					# 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.
					UpperCamelCase__       :Union[str, Any]							 =			[x for x in data if len(__a       ) > 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}'''       )

					UpperCamelCase__       :int							 =			np.asarray(__a       )

					UpperCamelCase__       :Tuple							 =			1
					UpperCamelCase__       :Dict							 =			np.array_split(__a			, __a       )

					# 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
										UpperCamelCase__       :List[Any]							 =			np.array_split(__a			, __a       )

					if num_chunks > 1:
										for chunk_id, chunk_data in enumerate(__a       ):
															UpperCamelCase__       :Dict							 =			chunk_data
					else:
										UpperCamelCase__       :Optional[Any]							 =			data


def       a				(      __a			, __a       )  ->     int:



					'''simple docstring'''






					if name in group.attrs:
										UpperCamelCase__       :int							 =			[n.decode('''utf8'''       ) if hasattr(__a			, '''decode'''       ) else n for n in group.attrs[name]]
					else:
										UpperCamelCase__       :Union[str, Any]							 =			[]
										UpperCamelCase__       :Optional[int]							 =			0
										while "%s%d" % (name, chunk_id) in group.attrs:
															data.extend(
															    [n.decode('''utf8'''       ) if hasattr(__a			, '''decode'''       ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]       )
															chunk_id += 1
					return data







def       a				(      __a       )  ->     List[str]:



					'''simple docstring'''






					def _expand_single_ad_tensor(__a       ):
										if isinstance(__a			, tf.Tensor       ) and t.shape.rank == 1:
															return tf.expand_dims(__a			, axis=-1       )
										return t

					return tf.nest.map_structure(_expand_single_ad_tensor			, __a       )



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import _LazyModule


__snake_case											=					{'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
		from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)