'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_A								=   logging.get_logger(__name__)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if isinstance(SCREAMING_SNAKE_CASE_  ,       np.ndarray							):
          return list(tensor.shape							)

     lowercase_					:       Optional[Any]     				=					tf.shape(SCREAMING_SNAKE_CASE_							)

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

     lowercase_					:       Optional[int]     				=					tensor.shape.as_list()

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = None  ,       SCREAMING_SNAKE_CASE_ = None							):
     return tf.nn.softmax(logits=logits + 1e-9  ,       axis=SCREAMING_SNAKE_CASE_  ,       name=SCREAMING_SNAKE_CASE_							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=1e-5  ,       SCREAMING_SNAKE_CASE_=-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(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
     lowercase_				,lowercase_					:       int     				=					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
          lowercase_					:       Optional[int]     				=					[1] * inputs.shape.rank
          lowercase_					:       Optional[int]     				=					shape_list(SCREAMING_SNAKE_CASE_							)[axis]
          lowercase_					:       Optional[Any]     				=					tf.reshape(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
          lowercase_					:       str     				=					tf.reshape(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     # Compute layer normalization using the batch_normalization
     # function.
     lowercase_					:       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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=0  ,       SCREAMING_SNAKE_CASE_=-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_					:       Optional[int]     				=					tf.shape(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]							)
     lowercase_					:       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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     if not isinstance(SCREAMING_SNAKE_CASE_  ,       tf.Tensor							):
          lowercase_					:       Any     				=					tf.convert_to_tensor(SCREAMING_SNAKE_CASE_							)  # Catches stray NumPy inputs
     if encoder_attention_mask.shape.rank == 3:
          lowercase_					:       Union[str, Any]     				=					encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.shape.rank == 2:
          lowercase_					:       Union[str, Any]     				=					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_					:       Union[str, Any]     				=					(
         tf.cast(1  ,       encoder_attention_mask.dtype							) - encoder_extended_attention_mask
     ) * encoder_extended_attention_mask.dtype.min

     return encoder_extended_attention_mask

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = "input_ids"							):
     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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Dict     				=					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_					:       str     				=					[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}'''							)

     lowercase_					:       Optional[Any]     				=					np.asarray(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					1
     lowercase_					:       List[str]     				=					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
          lowercase_					:       Tuple     				=					np.array_split(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE_							):
               lowercase_					:       List[str]     				=					chunk_data
     else:
          lowercase_					:       Any     				=					data

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if name in group.attrs:
          lowercase_					:       Tuple     				=					[n.decode('utf8'							) if hasattr(SCREAMING_SNAKE_CASE_  ,       'decode'							) else n for n in group.attrs[name]]
     else:
          lowercase_					:       Tuple     				=					[]
          lowercase_					:       Optional[Any]     				=					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 _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE_							):
          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_							)





'''simple docstring'''




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


_A								=   logging.get_logger(__name__)

_A								=   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Any     =    '''mra'''
   def __init__(self       ,				_a=50_265       ,				_a=768       ,				_a=12       ,				_a=12       ,				_a=3_072       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=1       ,				_a=0.02       ,				_a=1e-5       ,				_a="absolute"       ,				_a=4       ,				_a="full"       ,				_a=0       ,				_a=0       ,				_a=1       ,				_a=0       ,				_a=2       ,				**_a       ,				)    ->     int:
        super().__init__(pad_token_id=_a       ,				bos_token_id=_a       ,				eos_token_id=_a       ,				**_a				)

        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       List[str]     				=					max_position_embeddings
        lowercase_					:       Optional[Any]     				=					hidden_size
        lowercase_					:       Optional[int]     				=					num_hidden_layers
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       Optional[int]     				=					intermediate_size
        lowercase_					:       Dict     				=					hidden_act
        lowercase_					:       str     				=					hidden_dropout_prob
        lowercase_					:       int     				=					attention_probs_dropout_prob
        lowercase_					:       str     				=					initializer_range
        lowercase_					:       Union[str, Any]     				=					type_vocab_size
        lowercase_					:       Any     				=					layer_norm_eps
        lowercase_					:       Union[str, Any]     				=					position_embedding_type
        lowercase_					:       Any     				=					block_per_row
        lowercase_					:       Optional[int]     				=					approx_mode
        lowercase_					:       int     				=					initial_prior_first_n_blocks
        lowercase_					:       str     				=					initial_prior_diagonal_n_blocks




