
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowercase        =  logging.get_logger(__name__)



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[tf.Tensor, np.ndarray]       )  ->    Dict:

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

							lowerCamelCase_							  =					tf.shape(snake_case__       )

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

							lowerCamelCase_							  =					tensor.shape.as_list()

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      tf.Tensor			,				UpperCAmelCase__				:      Optional[int] = None			,				UpperCAmelCase__				:      Optional[str] = None       )  ->    Optional[int]:
							return tf.nn.softmax(logits=logits + 1e-9			,				axis=snake_case__			,				name=snake_case__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Dict=1e-5			,				UpperCAmelCase__				:      int=-1       )  ->    Dict:

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

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

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Any=0			,				UpperCAmelCase__				:      Tuple=-1       )  ->    Union[str, Any]:

							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

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      tf.Tensor       )  ->    str:

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

							return encoder_extended_attention_mask



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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Optional[int]       )  ->    Any:
							lowerCamelCase_							  =					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.
							lowerCamelCase_							  =					[x for x in data if len(snake_case__       ) > 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}'''       )

							lowerCamelCase_							  =					np.asarray(snake_case__       )

							lowerCamelCase_							  =					1
							lowerCamelCase_							  =					np.array_split(snake_case__			,				snake_case__       )

							# 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
														lowerCamelCase_							  =					np.array_split(snake_case__			,				snake_case__       )

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      List[str]       )  ->    List[str]:

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str       )  ->    int:

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

							return tf.nest.map_structure(_expand_single_ad_tensor			,				snake_case__       )






import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def      __lowerCAmelCase						(							UpperCAmelCase__				:      dict       )  ->    tuple:
							return (data["data"], data["target"])



def      __lowerCAmelCase						(							UpperCAmelCase__				:      np.ndarray			,				UpperCAmelCase__				:      np.ndarray       )  ->    XGBClassifier:
							lowerCamelCase_							  =					XGBClassifier()
							classifier.fit(UpperCAmelCase__			,				UpperCAmelCase__       )
							return classifier



def      __lowerCAmelCase						(							)  ->    None:
							lowerCamelCase_							  =					load_iris()
							lowerCamelCase_  ,   lowerCamelCase_							  =					data_handling(UpperCAmelCase__       )
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					train_test_split(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				test_size=0.2_5       )

							lowerCamelCase_							  =					iris["""target_names"""]

							# Create an XGBoost Classifier from the training data
							lowerCamelCase_							  =					xgboost(UpperCAmelCase__			,				UpperCAmelCase__       )

							# Display the confusion matrix of the classifier with both training and test sets
							ConfusionMatrixDisplay.from_estimator(
							    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				display_labels=UpperCAmelCase__			,				cmap="""Blues"""			,				normalize="""true"""			,				)
							plt.title("""Normalized Confusion Matrix - IRIS Dataset"""       )
							plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()





