


'''simple docstring'''






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 		_A (lowerCAmelCase__     :Union[tf.Tensor, np.ndarray]						)      ->     Dict:




     '''simple docstring'''



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

     _a						=							tf.shape(__lowerCamelCase						)

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

     _a						=							tensor.shape.as_list()

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






def 		_A (lowerCAmelCase__     :tf.Tensor ,  lowerCAmelCase__     :Optional[int] = None ,  lowerCAmelCase__     :Optional[str] = None						)      ->     Tuple:




     '''simple docstring'''



     return tf.nn.softmax(logits=logits + 1E-9 ,  axis=__lowerCamelCase ,  name=__lowerCamelCase						)






def 		_A (lowerCAmelCase__     :Optional[int] ,  lowerCAmelCase__     :str ,  lowerCAmelCase__     :Any ,  lowerCAmelCase__     :List[Any]=1E-5 ,  lowerCAmelCase__     :Dict=-1						)      ->     str:




     '''simple docstring'''



     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase ,  __lowerCamelCase						):
          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						=							tf.nn.moments(__lowerCamelCase ,  axes=[axis] ,  keepdims=__lowerCamelCase						)

     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(__lowerCamelCase						)[axis]
          _a						=							tf.reshape(__lowerCamelCase ,  __lowerCamelCase						)
          _a						=							tf.reshape(__lowerCamelCase ,  __lowerCamelCase						)

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






def 		_A (lowerCAmelCase__     :List[Any] ,  lowerCAmelCase__     :Any=0 ,  lowerCAmelCase__     :Tuple=-1						)      ->     List[str]:




     '''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

     _a						=							tf.shape(__lowerCamelCase						)
     _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(__lowerCamelCase ,  __lowerCamelCase						)






def 		_A (lowerCAmelCase__     :tf.Tensor						)      ->     List[Any]:




     '''simple docstring'''



     if not isinstance(__lowerCamelCase ,  tf.Tensor						):
          _a						=							tf.convert_to_tensor(__lowerCamelCase						)  # 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 		_A (lowerCAmelCase__     :tf.Tensor ,  lowerCAmelCase__     :int ,  lowerCAmelCase__     :str = "input_ids"						)      ->     List[str]:




     '''simple docstring'''



     tf.debugging.assert_less(
         __lowerCamelCase ,  tf.cast(__lowerCamelCase ,  dtype=tensor.dtype						) ,  message=(
             f'The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase						)}) must be smaller than the embedding '
             f'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
         ) ,  )






def 		_A (lowerCAmelCase__     :Dict ,  lowerCAmelCase__     :List[str] ,  lowerCAmelCase__     :Optional[int]						)      ->     int:




     '''simple docstring'''



     _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(__lowerCamelCase						) > 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(__lowerCamelCase						)

     _a						=							1
     _a						=							np.array_split(__lowerCamelCase ,  __lowerCamelCase						)

     # 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(__lowerCamelCase ,  __lowerCamelCase						)

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






def 		_A (lowerCAmelCase__     :Optional[Any] ,  lowerCAmelCase__     :Union[str, Any]						)      ->     Dict:




     '''simple docstring'''



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






def 		_A (lowerCAmelCase__     :Union[str, Any]						)      ->     Optional[Any]:




     '''simple docstring'''



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

     return tf.nest.map_structure(_expand_single_ad_tensor ,  __lowerCamelCase						)








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     Optional[Any]												={
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XCLIPModel''',
     '''XCLIPPreTrainedModel''',
     '''XCLIPTextModel''',
     '''XCLIPVisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_x_clip import (
     XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XCLIPConfig,
     XCLIPTextConfig,
     XCLIPVisionConfig,
 )
 from .processing_x_clip import XCLIPProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_x_clip import (
      XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      XCLIPModel,
      XCLIPPreTrainedModel,
      XCLIPTextModel,
      XCLIPVisionModel,
  )

else:
 import sys

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


