"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_A   					=    logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       ):
				if isinstance(__UpperCAmelCase  ,					np.ndarray       ):
								return list(tensor.shape       )

				SCREAMING_SNAKE_CASE__		  =				tf.shape(__UpperCAmelCase       )

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

				SCREAMING_SNAKE_CASE__		  =				tensor.shape.as_list()

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None       ):
				return tf.nn.softmax(logits=logits + 1e-9  ,					axis=__UpperCAmelCase  ,					name=__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=1e-5  ,					__UpperCAmelCase=-1       ):
				# 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(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								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
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				tf.nn.moments(__UpperCAmelCase  ,					axes=[axis]  ,					keepdims=__UpperCAmelCase       )

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

				# Compute layer normalization using the batch_normalization
				# function.
				SCREAMING_SNAKE_CASE__		  =				tf.nn.batch_normalization(
				    __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					offset=__UpperCAmelCase  ,					scale=__UpperCAmelCase  ,					variance_epsilon=__UpperCAmelCase  ,					)
				return outputs




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=0  ,					__UpperCAmelCase=-1       ):
				# 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

				SCREAMING_SNAKE_CASE__		  =				tf.shape(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]       )
				SCREAMING_SNAKE_CASE__		  =				tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  ,					axis=0       )
				return tf.reshape(__UpperCAmelCase  ,					__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       ):
				if not isinstance(__UpperCAmelCase  ,					tf.Tensor       ):
								SCREAMING_SNAKE_CASE__		  =				tf.convert_to_tensor(__UpperCAmelCase       )  # Catches stray NumPy inputs
				if encoder_attention_mask.shape.rank == 3:
								SCREAMING_SNAKE_CASE__		  =				encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.shape.rank == 2:
								SCREAMING_SNAKE_CASE__		  =				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))
				SCREAMING_SNAKE_CASE__		  =				(
				    tf.cast(1  ,					encoder_attention_mask.dtype       ) - encoder_extended_attention_mask
				) * encoder_extended_attention_mask.dtype.min

				return encoder_extended_attention_mask




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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       ):
				SCREAMING_SNAKE_CASE__		  =				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.
				SCREAMING_SNAKE_CASE__		  =				[x for x in data if len(__UpperCAmelCase       ) > 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}"""       )

				SCREAMING_SNAKE_CASE__		  =				np.asarray(__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				1
				SCREAMING_SNAKE_CASE__		  =				np.array_split(__UpperCAmelCase  ,					__UpperCAmelCase       )

				# 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
								SCREAMING_SNAKE_CASE__		  =				np.array_split(__UpperCAmelCase  ,					__UpperCAmelCase       )

				if num_chunks > 1:
								for chunk_id, chunk_data in enumerate(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__		  =				chunk_data
				else:
								SCREAMING_SNAKE_CASE__		  =				data




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       ):
				if name in group.attrs:
								SCREAMING_SNAKE_CASE__		  =				[n.decode("utf8"       ) if hasattr(__UpperCAmelCase  ,					"decode"       ) else n for n in group.attrs[name]]
				else:
								SCREAMING_SNAKE_CASE__		  =				[]
								SCREAMING_SNAKE_CASE__		  =				0
								while "%s%d" % (name, chunk_id) in group.attrs:
												data.extend(
												    [n.decode("utf8"       ) if hasattr(__UpperCAmelCase  ,					"decode"       ) else n for n in group.attrs["%s%d" % (name, chunk_id)]]       )
												chunk_id += 1
				return data




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       ):

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

				return tf.nest.map_structure(_expand_single_ad_tensor  ,					__UpperCAmelCase       )





"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A   					=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    [
						    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'VanForImageClassification',
						    'VanModel',
						    'VanPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure)




