



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a__      :				List[str]   	=			logging.get_logger(__name__)

def   _lowerCAmelCase   (						A__	):
      if isinstance(A__						,  np.ndarray	):
            return list(tensor.shape	)

      lowercase__     =  tf.shape(A__	)

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

      lowercase__     =  tensor.shape.as_list()

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

def   _lowerCAmelCase   (						A__						,  A__ = None						,  A__ = None	):
      return tf.nn.softmax(logits=logits + 1E-9						,  axis=A__						,  name=A__	)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__=1E-5						,  A__=-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(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__     =  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__     =  [1] * inputs.shape.rank
            lowercase__     =  shape_list(A__	)[axis]
            lowercase__     =  tf.reshape(A__						,  A__	)
            lowercase__     =  tf.reshape(A__						,  A__	)

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

def   _lowerCAmelCase   (						A__						,  A__=0						,  A__=-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(A__	)
      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(A__						,  A__	)

def   _lowerCAmelCase   (						A__	):
      if not isinstance(A__						,  tf.Tensor	):
            lowercase__     =  tf.convert_to_tensor(A__	)  # 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   (						A__						,  A__						,  A__ = "input_ids"	):
      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   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  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__     =  [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__     =  np.asarray(A__	)

      lowercase__     =  1
      lowercase__     =  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__     =  np.array_split(A__						,  A__	)

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

def   _lowerCAmelCase   (						A__						,  A__	):
      if name in group.attrs:
            lowercase__     =  [n.decode('utf8'	) if hasattr(A__						,  '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(A__						,  'decode'	) else n for n in group.attrs['%s%d' % (name, chunk_id)]]	)
                  chunk_id += 1
      return data

def   _lowerCAmelCase   (						A__	):

      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__	)





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__      :				List[str]   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}



class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[str]       				=    "focalnet"


   def __init__(						self  :			Dict			,				lowerCAmelCase  :			Union[str, Any]=2_24			,				lowerCAmelCase  :			List[str]=4			,				lowerCAmelCase  :			int=3			,				lowerCAmelCase  :			Union[str, Any]=96			,				lowerCAmelCase  :			List[Any]=False			,				lowerCAmelCase  :			int=[1_92, 3_84, 7_68, 7_68]			,				lowerCAmelCase  :			str=[2, 2, 6, 2]			,				lowerCAmelCase  :			Tuple=[2, 2, 2, 2]			,				lowerCAmelCase  :			Optional[Any]=[3, 3, 3, 3]			,				lowerCAmelCase  :			int="gelu"			,				lowerCAmelCase  :			Any=4.0			,				lowerCAmelCase  :			List[str]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.1			,				lowerCAmelCase  :			Union[str, Any]=False			,				lowerCAmelCase  :			Tuple=1E-4			,				lowerCAmelCase  :			List[Any]=False			,				lowerCAmelCase  :			Optional[int]=False			,				lowerCAmelCase  :			List[str]=False			,				lowerCAmelCase  :			str=0.02			,				lowerCAmelCase  :			Optional[int]=1E-5			,				lowerCAmelCase  :			List[Any]=32			,				lowerCAmelCase  :			List[Any]=None			,				lowerCAmelCase  :			Union[str, Any]=None			,				**lowerCAmelCase  :			str			,				)	->				List[str]:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)

         lowercase__     =  image_size
         lowercase__     =  patch_size
         lowercase__     =  num_channels
         lowercase__     =  embed_dim
         lowercase__     =  use_conv_embed
         lowercase__     =  hidden_sizes
         lowercase__     =  depths
         lowercase__     =  focal_levels
         lowercase__     =  focal_windows
         lowercase__     =  hidden_act
         lowercase__     =  mlp_ratio
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  drop_path_rate
         lowercase__     =  use_layerscale
         lowercase__     =  layerscale_value
         lowercase__     =  use_post_layernorm
         lowercase__     =  use_post_layernorm_in_modulation
         lowercase__     =  normalize_modulator
         lowercase__     =  initializer_range
         lowercase__     =  layer_norm_eps
         lowercase__     =  encoder_stride
         lowercase__     =  ['stem'] + [f'''stage{idx}''' for idx in range(1			,				len(self.depths) + 1)]
         lowercase__,	lowercase__     =  get_aligned_output_features_output_indices(
             out_features=lowerCAmelCase			,				out_indices=lowerCAmelCase			,				stage_names=self.stage_names)
