





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a__: str							   =						logging.get_logger(__name__)


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

       A__		   =		tf.shape(UpperCamelCase__    )

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

       A__		   =		tensor.shape.as_list()

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


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


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			Union[str, Any]					,	UpperCamelCase__     :			Tuple=1e-5					,	UpperCamelCase__     :			Optional[int]=-1    )->int:
       # 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(UpperCamelCase__					,	UpperCamelCase__    ):
              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
       A__   ,      A__		   =		tf.nn.moments(UpperCamelCase__					,	axes=[axis]					,	keepdims=UpperCamelCase__    )

       if axis != -1:
              # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
              # on every dimension except axis
              A__		   =		[1] * inputs.shape.rank
              A__		   =		shape_list(UpperCamelCase__    )[axis]
              A__		   =		tf.reshape(UpperCamelCase__					,	UpperCamelCase__    )
              A__		   =		tf.reshape(UpperCamelCase__					,	UpperCamelCase__    )

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


def       UpperCamelCase__(			UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			List[str]=0					,	UpperCamelCase__     :			Dict=-1    )->Any:
       # 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

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


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

       return encoder_extended_attention_mask


def       UpperCamelCase__(			UpperCamelCase__     :			tf.Tensor					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			str = "input_ids"    )->None:
       tf.debugging.assert_less(
           UpperCamelCase__					,	tf.cast(UpperCamelCase__					,	dtype=tensor.dtype    )					,	message=(
               f"The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase__    )}) must be smaller than the embedding "
               f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
           )					,	)


def       UpperCamelCase__(			UpperCamelCase__     :			Union[str, Any]					,	UpperCamelCase__     :			Union[str, Any]					,	UpperCamelCase__     :			Optional[int]    )->Union[str, Any]:
       A__		   =		6_45_12
       # 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.
       A__		   =		[x for x in data if len(UpperCamelCase__    ) > 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}"    )

       A__		   =		np.asarray(UpperCamelCase__    )

       A__		   =		1
       A__		   =		np.array_split(UpperCamelCase__					,	UpperCamelCase__    )

       # 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
              A__		   =		np.array_split(UpperCamelCase__					,	UpperCamelCase__    )

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


def       UpperCamelCase__(			UpperCamelCase__     :			Union[str, Any]					,	UpperCamelCase__     :			Dict    )->int:
       if name in group.attrs:
              A__		   =		[n.decode('''utf8'''    ) if hasattr(UpperCamelCase__					,	'''decode'''    ) else n for n in group.attrs[name]]
       else:
              A__		   =		[]
              A__		   =		0
              while "%s%d" % (name, chunk_id) in group.attrs:
                     data.extend(
                         [n.decode('''utf8'''    ) if hasattr(UpperCamelCase__					,	'''decode'''    ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]    )
                     chunk_id += 1
       return data


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]    )->Union[str, Any]:

       def _expand_single_ad_tensor(UpperCamelCase__     :			Optional[Any]    ):
              if isinstance(UpperCamelCase__					,	tf.Tensor    ) and t.shape.rank == 1:
                     return tf.expand_dims(UpperCamelCase__					,	axis=-1    )
              return t

       return tf.nest.map_structure(_expand_single_ad_tensor					,	UpperCamelCase__    )












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__: str							   =						{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Union[str, Any]							   =						[
           'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MegaForCausalLM',
           'MegaForMaskedLM',
           'MegaForMultipleChoice',
           'MegaForQuestionAnswering',
           'MegaForSequenceClassification',
           'MegaForTokenClassification',
           'MegaModel',
           'MegaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mega import (
                  MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MegaForCausalLM,
                  MegaForMaskedLM,
                  MegaForMultipleChoice,
                  MegaForQuestionAnswering,
                  MegaForSequenceClassification,
                  MegaForTokenClassification,
                  MegaModel,
                  MegaPreTrainedModel,
              )

else:
       import sys

       a__: Union[str, Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





