





'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[tf.Tensor, np.ndarray]				):
			if isinstance(lowerCamelCase_		,       np.ndarray				):
						return list(tensor.shape				)

			__lowercase										=							tf.shape(lowerCamelCase_				)

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

			__lowercase										=							tensor.shape.as_list()

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  tf.Tensor		,       lowerCamelCase_			:  Optional[int] = None		,       lowerCamelCase_			:  Optional[str] = None				):
			return tf.nn.softmax(logits=logits + 1E-9		,       axis=lowerCamelCase_		,       name=lowerCamelCase_				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  str=1E-5		,       lowerCamelCase_			:  Optional[int]=-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(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
			__lowercase						,		__lowercase										=							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
						__lowercase										=							[1] * inputs.shape.rank
						__lowercase										=							shape_list(lowerCamelCase_				)[axis]
						__lowercase										=							tf.reshape(lowerCamelCase_		,       lowerCamelCase_				)
						__lowercase										=							tf.reshape(lowerCamelCase_		,       lowerCamelCase_				)

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Dict=0		,       lowerCamelCase_			:  Any=-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

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




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

			return encoder_extended_attention_mask




def    _lowerCAmelCase						(				lowerCamelCase_			:  tf.Tensor		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  str = "input_ids"				):
			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    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  List[Any]				):
			__lowercase										=							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.
			__lowercase										=							[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}"				)

			__lowercase										=							np.asarray(lowerCamelCase_				)

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

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  List[Any]				):
			if name in group.attrs:
						__lowercase										=							[n.decode('''utf8'''				) if hasattr(lowerCamelCase_		,       '''decode'''				) else n for n in group.attrs[name]]
			else:
						__lowercase										=							[]
						__lowercase										=							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    _lowerCAmelCase						(				lowerCamelCase_			:  Any				):

			def _expand_single_ad_tensor(lowerCamelCase_			:  Tuple				):
						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_				)












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							None:







				'''simple docstring'''





				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''     ,_lowerCamelCase     ,)
				super().__init__(*_lowerCamelCase     ,**_lowerCamelCase )





