





"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       int	 =		"""glpn"""




				def __init__(     self  :str						,    __lowercase  :Union[str, Any]=3						,    __lowercase  :Tuple=4						,    __lowercase  :Tuple=[2, 2, 2, 2]						,    __lowercase  :str=[8, 4, 2, 1]						,    __lowercase  :Any=[32, 64, 160, 256]						,    __lowercase  :Dict=[7, 3, 3, 3]						,    __lowercase  :Union[str, Any]=[4, 2, 2, 2]						,    __lowercase  :Optional[Any]=[1, 2, 5, 8]						,    __lowercase  :Tuple=[4, 4, 4, 4]						,    __lowercase  :Dict="gelu"						,    __lowercase  :Tuple=0.0						,    __lowercase  :Optional[Any]=0.0						,    __lowercase  :Union[str, Any]=0.02						,    __lowercase  :Optional[Any]=0.1						,    __lowercase  :Any=1e-6						,    __lowercase  :Dict=64						,    __lowercase  :Optional[Any]=10						,    __lowercase  :Tuple=-1						,    **__lowercase  :Optional[Any]						,    ):
											super().__init__(**__lowercase			)

											__lowerCamelCase      :      List[Any]         =num_channels
											__lowerCamelCase      :      Dict         =num_encoder_blocks
											__lowerCamelCase      :      Optional[int]         =depths
											__lowerCamelCase      :      Optional[int]         =sr_ratios
											__lowerCamelCase      :      List[str]         =hidden_sizes
											__lowerCamelCase      :      Dict         =patch_sizes
											__lowerCamelCase      :      Dict         =strides
											__lowerCamelCase      :      Any         =mlp_ratios
											__lowerCamelCase      :      Optional[int]         =num_attention_heads
											__lowerCamelCase      :      Dict         =hidden_act
											__lowerCamelCase      :      int         =hidden_dropout_prob
											__lowerCamelCase      :      str         =attention_probs_dropout_prob
											__lowerCamelCase      :      int         =initializer_range
											__lowerCamelCase      :      List[Any]         =drop_path_rate
											__lowerCamelCase      :      Optional[int]         =layer_norm_eps
											__lowerCamelCase      :      Dict         =decoder_hidden_size
											__lowerCamelCase      :      Tuple         =max_depth
											__lowerCamelCase      :      List[Any]         =head_in_index











"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[tf.Tensor, np.ndarray]   ):


							'''simple docstring'''







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

							__lowerCamelCase      :      Tuple         =tf.shape(SCREAMING_SNAKE_CASE   )

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

							__lowerCamelCase      :      Union[str, Any]         =tensor.shape.as_list()

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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor    ,						SCREAMING_SNAKE_CASE       :							Optional[int] = None    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Tuple=1E-5    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]=-1   ):


							'''simple docstring'''







							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_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    ,						__lowerCamelCase      :      Dict         =tf.nn.moments(SCREAMING_SNAKE_CASE    ,						axes=[axis]    ,						keepdims=SCREAMING_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      :      Dict         =[1] * inputs.shape.rank
														__lowerCamelCase      :      Dict         =shape_list(SCREAMING_SNAKE_CASE   )[axis]
														__lowerCamelCase      :      List[str]         =tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      List[str]         =tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# Compute layer normalization using the batch_normalization
							# function.
							__lowerCamelCase      :      int         =tf.nn.batch_normalization(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						offset=SCREAMING_SNAKE_CASE    ,						scale=SCREAMING_SNAKE_CASE    ,						variance_epsilon=SCREAMING_SNAKE_CASE    ,						)
							return outputs



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Dict=0    ,						SCREAMING_SNAKE_CASE       :							int=-1   ):


							'''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

							__lowerCamelCase      :      List[str]         =tf.shape(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]   )
							__lowerCamelCase      :      int         =tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]    ,						axis=0   )
							return tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor   ):


							'''simple docstring'''







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

							return encoder_extended_attention_mask



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							str = "input_ids"   ):


							'''simple docstring'''







							tf.debugging.assert_less(
							    SCREAMING_SNAKE_CASE    ,						tf.cast(SCREAMING_SNAKE_CASE    ,						dtype=tensor.dtype   )    ,						message=(
							        F'The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_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_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =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.
							__lowerCamelCase      :      Any         =[x for x in data if len(SCREAMING_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      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Any         =1
							__lowerCamelCase      :      Optional[Any]         =np.array_split(SCREAMING_SNAKE_CASE    ,						SCREAMING_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      :      Dict         =np.array_split(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =chunk_data
							else:
														__lowerCamelCase      :      Optional[int]         =data



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







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

							return tf.nest.map_structure(_expand_single_ad_tensor    ,						SCREAMING_SNAKE_CASE   )




