






"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a			:    int				 =		logging.get_logger(__name__)





def 						lowercase__(A						)    ->List[int]:




						"""simple docstring"""



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

						lowercase__    :				Optional[Any]=	tf.shape(A						)

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

						lowercase__    :				Any=	tensor.shape.as_list()

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





def 						lowercase__(A ,		A = None ,		A = None						)    ->tf.Tensor:




						"""simple docstring"""



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





def 						lowercase__(A ,		A ,		A ,		A=1e-5 ,		A=-1						)    ->Any:




						"""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
						lowercase__,     lowercase__    :				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
												lowercase__    :				int=	[1] * inputs.shape.rank
												lowercase__    :				Tuple=	shape_list(A						)[axis]
												lowercase__    :				Any=	tf.reshape(A ,		A						)
												lowercase__    :				Optional[int]=	tf.reshape(A ,		A						)

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





def 						lowercase__(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

						lowercase__    :				Union[str, Any]=	tf.shape(A						)
						lowercase__    :				Union[str, Any]=	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]						)
						lowercase__    :				Tuple=	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] ,		axis=0						)
						return tf.reshape(A ,		A						)





def 						lowercase__(A						)    ->tf.Tensor:




						"""simple docstring"""



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

						return encoder_extended_attention_mask





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




						"""simple docstring"""



						lowercase__    :				Any=	64_512
						# 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.
						lowercase__    :				str=	[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}'''						)

						lowercase__    :				int=	np.asarray(A						)

						lowercase__    :				List[Any]=	1
						lowercase__    :				Union[str, Any]=	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
												lowercase__    :				Any=	np.array_split(A ,		A						)

						if num_chunks > 1:
												for chunk_id, chunk_data in enumerate(A						):
																		lowercase__    :				Optional[int]=	chunk_data
						else:
												lowercase__    :				str=	data





def 						lowercase__(A ,		A						)    ->Tuple:




						"""simple docstring"""



						if name in group.attrs:
												lowercase__    :				Optional[int]=	[n.decode("utf8"						) if hasattr(A ,		"decode"						) else n for n in group.attrs[name]]
						else:
												lowercase__    :				Optional[int]=	[]
												lowercase__    :				Optional[Any]=	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 						lowercase__(A						)    ->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 OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    Union[str, Any]				 =		{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    int				 =		[
			    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PegasusXForConditionalGeneration""",
			    """PegasusXModel""",
			    """PegasusXPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus_x import (
						    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusXForConditionalGeneration,
						    PegasusXModel,
						    PegasusXPreTrainedModel,
						)


else:
			import sys

			a			:    Optional[Any]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






