


from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






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

     snake_case					=    tf.shape(UpperCamelCase_    )

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

     snake_case					=    tensor.shape.as_list()

     return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase_    )]
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_ = None							,UpperCamelCase_ = None    ):



     """simple docstring"""






     return tf.nn.softmax(logits=logits + 1e-9							,axis=UpperCamelCase_							,name=UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=1e-5							,UpperCamelCase_=-1    ):



     """simple docstring"""






     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
     snake_case			,  snake_case					=    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
          snake_case					=    [1] * inputs.shape.rank
          snake_case					=    shape_list(UpperCamelCase_    )[axis]
          snake_case					=    tf.reshape(UpperCamelCase_							,UpperCamelCase_    )
          snake_case					=    tf.reshape(UpperCamelCase_							,UpperCamelCase_    )

     # Compute layer normalization using the batch_normalization
     # function.
     snake_case					=    tf.nn.batch_normalization(
         UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,offset=UpperCamelCase_							,scale=UpperCamelCase_							,variance_epsilon=UpperCamelCase_							,)
     return outputs
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=0							,UpperCamelCase_=-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

     snake_case					=    tf.shape(UpperCamelCase_    )
     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(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     if not isinstance(UpperCamelCase_							,tf.Tensor    ):
          snake_case					=    tf.convert_to_tensor(UpperCamelCase_    )  # 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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = "input_ids"    ):



     """simple docstring"""






     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_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    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.
     snake_case					=    [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}'''    )

     snake_case					=    np.asarray(UpperCamelCase_    )

     snake_case					=    1
     snake_case					=    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
          snake_case					=    np.array_split(UpperCamelCase_							,UpperCamelCase_    )

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(UpperCamelCase_    ):
               snake_case					=    chunk_data
     else:
          snake_case					=    data
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






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

def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     def _expand_single_ad_tensor(UpperCamelCase_    ):
          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


_SCREAMING_SNAKE_CASE      :			int      				=   {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			Dict      				=   [
     "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TimesformerModel",
     "TimesformerForVideoClassification",
     "TimesformerPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_timesformer import (
      TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TimesformerForVideoClassification,
      TimesformerModel,
      TimesformerPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE      :			int      				=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




