from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


lowerCAmelCase__     						=			logging.get_logger(__name__)






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

       lowerCAmelCase__					     = tf.shape(lowerCAmelCase__     )

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

       lowerCAmelCase__					     = tensor.shape.as_list()

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = None     ,  lowerCAmelCase__ = None     ):
       return tf.nn.softmax(logits=logits + 1e-9     ,  axis=lowerCAmelCase__     ,  name=lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=1e-5     ,  lowerCAmelCase__=-1     ):
       # This is a very simplified functional layernorm, designed to duplicate
       # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
       # models in Transformers.

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

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=0     ,  lowerCAmelCase__=-1     ):
       # Replicates the behavior of torch.flatten in TF

       # If end_dim or start_dim is negative, count them from the end
       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

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






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

       return encoder_extended_attention_mask






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = "input_ids"     ):
       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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = 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.
       lowerCAmelCase__					     = [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}"""     )

       lowerCAmelCase__					     = np.asarray(lowerCAmelCase__     )

       lowerCAmelCase__					     = 1
       lowerCAmelCase__					     = 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
              lowerCAmelCase__					     = np.array_split(lowerCAmelCase__     ,  lowerCAmelCase__     )

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       if name in group.attrs:
              lowerCAmelCase__					     = [n.decode('utf8'     ) if hasattr(lowerCAmelCase__     ,  'decode'     ) else n for n in group.attrs[name]]
       else:
              lowerCAmelCase__					     = []
              lowerCAmelCase__					     = 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 				__lowerCamelCase		(						lowerCAmelCase__     ):

       def _expand_single_ad_tensor(lowerCAmelCase__     ):
              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__     )

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase__     						=			TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase__     						=			TaTokenizerFast

lowerCAmelCase__     						=			{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'MT5EncoderModel',
        'MT5ForConditionalGeneration',
        'MT5ForQuestionAnswering',
        'MT5Model',
        'MT5PreTrainedModel',
        'MT5Stack',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(
        __name__,
        globals()['__file__'],
        _import_structure,
        extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
        module_spec=__spec__,
    )
