from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase					      =							logging.get_logger(__name__)






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			Union[tf.Tensor, np.ndarray]  ):






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

      snake_case_  :      List[Any]  	=							tf.shape(snake_case__  )

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

      snake_case_  :      str  	=							tensor.shape.as_list()

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






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






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			List[Any]			,						lowerCAmelCase_:			int			,						lowerCAmelCase_:			Dict			,						lowerCAmelCase_:			List[Any]=1e-5			,						lowerCAmelCase_:			Tuple=-1  ):






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

      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_  :      int  	=							[1] * inputs.shape.rank
            snake_case_  :      str  	=							shape_list(snake_case__  )[axis]
            snake_case_  :      int  	=							tf.reshape(snake_case__			,						snake_case__  )
            snake_case_  :      Optional[int]  	=							tf.reshape(snake_case__			,						snake_case__  )

      # Compute layer normalization using the batch_normalization
      # function.
      snake_case_  :      Optional[int]  	=							tf.nn.batch_normalization(
          snake_case__			,						snake_case__			,						snake_case__			,						offset=snake_case__			,						scale=snake_case__			,						variance_epsilon=snake_case__			,						)
      return outputs






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			Optional[int]			,						lowerCAmelCase_:			Tuple=0			,						lowerCAmelCase_:			List[str]=-1  ):






      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_  :      int  	=							tf.shape(snake_case__  )
      snake_case_  :      Any  	=							tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]  )
      snake_case_  :      Dict  	=							tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,						axis=0  )
      return tf.reshape(snake_case__			,						snake_case__  )






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			tf.Tensor  ):






      if not isinstance(snake_case__			,						tf.Tensor  ):
            snake_case_  :      Union[str, Any]  	=							tf.convert_to_tensor(snake_case__  )  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            snake_case_  :      Tuple  	=							encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            snake_case_  :      List[Any]  	=							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_  :      Any  	=							(
          tf.cast(1			,						encoder_attention_mask.dtype  ) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			tf.Tensor			,						lowerCAmelCase_:			int			,						lowerCAmelCase_:			str = "input_ids"  ):
      tf.debugging.assert_less(
          snake_case__			,						tf.cast(snake_case__			,						dtype=tensor.dtype  )			,						message=(
              f"The maximum value of {tensor_name} ({tf.math.reduce_max(snake_case__  )}) must be smaller than the embedding "
              f"layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
          )			,						)






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			List[Any]			,						lowerCAmelCase_:			List[str]			,						lowerCAmelCase_:			Tuple  ):
      snake_case_  :      Optional[Any]  	=							6_4_5_1_2
      # 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_  :      str  	=							[x for x in data if len(snake_case__  ) > 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_  :      List[str]  	=							np.asarray(snake_case__  )

      snake_case_  :      Optional[Any]  	=							1
      snake_case_  :      Any  	=							np.array_split(snake_case__			,						snake_case__  )

      # 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_  :      Union[str, Any]  	=							np.array_split(snake_case__			,						snake_case__  )

      if num_chunks > 1:
            for chunk_id, chunk_data in enumerate(snake_case__  ):
                  snake_case_  :      List[str]  	=							chunk_data
      else:
            snake_case_  :      List[Any]  	=							data






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			Dict			,						lowerCAmelCase_:			Dict  ):






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






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			int  ):






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

      return tf.nest.map_structure(_expand_single_ad_tensor			,						snake_case__  )











from ...configuration_utils import PretrainedConfig


lowerCamelCase__       		=  {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Tuple								=			"tapas"



       def __init__(				self      ,    a__=3_05_22      ,    a__=7_68      ,    a__=12      ,    a__=12      ,    a__=30_72      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=10_24      ,    a__=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]      ,    a__=0.02      ,    a__=1e-12      ,    a__=0      ,    a__=10.0      ,    a__=0      ,    a__=1.0      ,    a__=None      ,    a__=1.0      ,    a__=False      ,    a__=None      ,    a__=1.0      ,    a__=1.0      ,    a__=False      ,    a__=False      ,    a__="ratio"      ,    a__=None      ,    a__=None      ,    a__=64      ,    a__=32      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__      ,    **a__     )

            # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
            __snake_case      :Optional[Any] 							=    vocab_size
            __snake_case      :Tuple 							=    hidden_size
            __snake_case      :Tuple 							=    num_hidden_layers
            __snake_case      :Any 							=    num_attention_heads
            __snake_case      :List[str] 							=    hidden_act
            __snake_case      :List[str] 							=    intermediate_size
            __snake_case      :int 							=    hidden_dropout_prob
            __snake_case      :List[Any] 							=    attention_probs_dropout_prob
            __snake_case      :Any 							=    max_position_embeddings
            __snake_case      :Optional[int] 							=    type_vocab_sizes
            __snake_case      :Tuple 							=    initializer_range
            __snake_case      :Any 							=    layer_norm_eps

            # Fine-tuning task hyperparameters
            __snake_case      :Optional[int] 							=    positive_label_weight
            __snake_case      :Optional[Any] 							=    num_aggregation_labels
            __snake_case      :Dict 							=    aggregation_loss_weight
            __snake_case      :str 							=    use_answer_as_supervision
            __snake_case      :int 							=    answer_loss_importance
            __snake_case      :List[Any] 							=    use_normalized_answer_loss
            __snake_case      :List[str] 							=    huber_loss_delta
            __snake_case      :Any 							=    temperature
            __snake_case      :Optional[int] 							=    aggregation_temperature
            __snake_case      :List[str] 							=    use_gumbel_for_cells
            __snake_case      :Optional[int] 							=    use_gumbel_for_aggregation
            __snake_case      :Optional[Any] 							=    average_approximation_function
            __snake_case      :Union[str, Any] 							=    cell_selection_preference
            __snake_case      :Tuple 							=    answer_loss_cutoff
            __snake_case      :Union[str, Any] 							=    max_num_rows
            __snake_case      :Optional[int] 							=    max_num_columns
            __snake_case      :str 							=    average_logits_per_cell
            __snake_case      :Union[str, Any] 							=    select_one_column
            __snake_case      :Optional[int] 							=    allow_empty_column_selection
            __snake_case      :Optional[Any] 							=    init_cell_selection_weights_to_zero
            __snake_case      :Tuple 							=    reset_position_index_per_cell
            __snake_case      :Any 							=    disable_per_token_loss

            # Aggregation hyperparameters
            __snake_case      :List[str] 							=    aggregation_labels
            __snake_case      :int 							=    no_aggregation_label_index

            if isinstance(self.aggregation_labels      ,    a__     ):
                 __snake_case      :Any 							=    {int(a__     ): v for k, v in aggregation_labels.items()}


