




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


snake_case_ : List[Any]    		=       logging.get_logger(__name__)


def    A						(__A   :    Union[tf.Tensor, np.ndarray]       )   ->      List[int]:







     """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    A						(__A   :    tf.Tensor   ,				__A   :    Optional[int] = None   ,				__A   :    Optional[str] = None       )   ->      tf.Tensor:







     """simple docstring"""


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


def    A						(__A   :    Union[str, Any]   ,				__A   :    Optional[int]   ,				__A   :    str   ,				__A   :    Optional[int]=1E-5   ,				__A   :    int=-1       )   ->      Any:







     """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    A						(__A   :    Any   ,				__A   :    List[Any]=0   ,				__A   :    Tuple=-1       )   ->      Optional[Any]:







     """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    A						(__A   :    tf.Tensor       )   ->      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    A						(__A   :    tf.Tensor   ,				__A   :    int   ,				__A   :    str = "input_ids"       )   ->      None:







     """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    A						(__A   :    Tuple   ,				__A   :    Dict   ,				__A   :    List[Any]       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							64512
     # 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    A						(__A   :    List[Any]   ,				__A   :    Any       )   ->      int:







     """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    A						(__A   :    List[Any]       )   ->      Any:







     """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       )













'''simple docstring'''


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"	,set() )
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   class       a_   :





        def __init__( self		:					int	,      lowercase		:					int						):





           """simple docstring"""




           lowercase_			:Optional[Any]    			= metric_id







   class       a_   :
        __A      =			[MetricMock(_lowerCAmelCase							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]





        def 							lowercase__      ( self		:					Union[str, Any]						):





           """simple docstring"""




           return self._metrics







   monkeypatch.setattr("datasets.inspect.huggingface_hub"	,HfhMock() )


@pytest.mark.parametrize(
    "func, args"	,[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple ):
   if "tmp_path" in args:
      lowercase_			:Union[str, Any]    			= tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
   with pytest.warns(__lowerCamelCase	,match="https://huggingface.co/docs/evaluate" ):
      func(*__lowerCamelCase )


