
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Union[tf.Tensor, np.ndarray]						):



     '''simple docstring'''




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

     UpperCamelCase__				=    tf.shape(__a						)

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

     UpperCamelCase__				=    tensor.shape.as_list()

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






def 						__magic_name__			(   __a      :	tf.Tensor	,		__a      :	Optional[int] = None	,		__a      :	Optional[str] = None						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	Union[str, Any]	,		__a      :	List[str]=1E-5	,		__a      :	Optional[Any]=-1						):



     '''simple docstring'''




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

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

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






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	List[Any]=0	,		__a      :	List[str]=-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

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






def 						__magic_name__			(   __a      :	tf.Tensor						):



     '''simple docstring'''




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

     return encoder_extended_attention_mask






def 						__magic_name__			(   __a      :	tf.Tensor	,		__a      :	int	,		__a      :	str = "input_ids"						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	Optional[Any]	,		__a      :	str						):



     '''simple docstring'''




     UpperCamelCase__				=    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.
     UpperCamelCase__				=    [x for x in data if len(__a						) > 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}"						)

     UpperCamelCase__				=    np.asarray(__a						)

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

     # 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
          UpperCamelCase__				=    np.array_split(__a	,		__a						)

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






def 						__magic_name__			(   __a      :	Tuple	,		__a      :	List[Any]						):



     '''simple docstring'''




     if name in group.attrs:
          UpperCamelCase__				=    [n.decode("""utf8"""						) if hasattr(__a	,		"""decode"""						) else n for n in group.attrs[name]]
     else:
          UpperCamelCase__				=    []
          UpperCamelCase__				=    0
          while "%s%d" % (name, chunk_id) in group.attrs:
               data.extend(
                   [n.decode("""utf8"""						) if hasattr(__a	,		"""decode"""						) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]						)
               chunk_id += 1
     return data






def 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




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

     return tf.nest.map_structure(_expand_single_ad_tensor	,		__a						)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''GitForCausalLM''',
        '''GitModel''',
        '''GitPreTrainedModel''',
        '''GitVisionModel''',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





