





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


snake_case_           =      logging.get_logger(__name__)






def lowerCamelCase__   (							snake_case_     :  Union[tf.Tensor, np.ndarray]	)				->    List[int]:
   if isinstance(snake_case_	,				np.ndarray	):
      return list(tensor.shape	)

   __snake_case								=       tf.shape(snake_case_	)

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

   __snake_case								=       tensor.shape.as_list()

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






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






def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  List[Any]	,				snake_case_     :  int	,				snake_case_     :  Optional[int]=1e-5	,				snake_case_     :  List[str]=-1	)				->    Tuple:
   # 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(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
   __snake_case	,     __snake_case								=       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
      __snake_case								=       [1] * inputs.shape.rank
      __snake_case								=       shape_list(snake_case_	)[axis]
      __snake_case								=       tf.reshape(snake_case_	,				snake_case_	)
      __snake_case								=       tf.reshape(snake_case_	,				snake_case_	)

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






def lowerCamelCase__   (							snake_case_     :  List[str]	,				snake_case_     :  Optional[int]=0	,				snake_case_     :  str=-1	)				->    Dict:
   # 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

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






def lowerCamelCase__   (							snake_case_     :  tf.Tensor	)				->    tf.Tensor:
   if not isinstance(snake_case_	,				tf.Tensor	):
      __snake_case								=       tf.convert_to_tensor(snake_case_	)  # Catches stray NumPy inputs
   if encoder_attention_mask.shape.rank == 3:
      __snake_case								=       encoder_attention_mask[:, None, :, :]
   if encoder_attention_mask.shape.rank == 2:
      __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))
   __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 lowerCamelCase__   (							snake_case_     :  tf.Tensor	,				snake_case_     :  int	,				snake_case_     :  str = "input_ids"	)				->    None:
   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__   (							snake_case_     :  Dict	,				snake_case_     :  Any	,				snake_case_     :  Any	)				->    List[Any]:
   __snake_case								=       6_4512
   # 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.
   __snake_case								=       [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}"""	)

   __snake_case								=       np.asarray(snake_case_	)

   __snake_case								=       1
   __snake_case								=       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
      __snake_case								=       np.array_split(snake_case_	,				snake_case_	)

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






def lowerCamelCase__   (							snake_case_     :  Optional[int]	,				snake_case_     :  Optional[Any]	)				->    List[str]:
   if name in group.attrs:
      __snake_case								=       [n.decode('''utf8'''	) if hasattr(snake_case_	,				'''decode'''	) else n for n in group.attrs[name]]
   else:
      __snake_case								=       []
      __snake_case								=       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__   (							snake_case_     :  Optional[Any]	)				->    Union[str, Any]:

   def _expand_single_ad_tensor(snake_case_     :  List[str]	):
      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 math






def lowerCamelCase__   (							snake_case_     :  int	)				->    list[int]:
   __snake_case								=       []
   __snake_case								=       2
   __snake_case								=       int(math.sqrt(snake_case_	)	)  # Size of every segment
   __snake_case								=       [True] * (end + 1)
   __snake_case								=       []

   while start <= end:
      if temp[start] is True:
         in_prime.append(snake_case_	)
         for i in range(start * start	,				end + 1	,				snake_case_	):
            __snake_case								=       False
      start += 1
   prime += in_prime

   __snake_case								=       end + 1
   __snake_case								=       min(2 * end	,				snake_case_	)

   while low <= n:
      __snake_case								=       [True] * (high - low + 1)
      for each in in_prime:
         __snake_case								=       math.floor(low / each	) * each
         if t < low:
            t += each

         for j in range(snake_case_	,				high + 1	,				snake_case_	):
            __snake_case								=       False

      for j in range(len(snake_case_	)	):
         if temp[j] is True:
            prime.append(j + low	)

      __snake_case								=       high + 1
      __snake_case								=       min(high + end	,				snake_case_	)

   return prime







print(sieve(10**6))

