'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import tensorflow as tf

class   _lowercase(     TensorFormatter[Mapping, '''tf.Tensor''', Mapping]				):



   """simple docstring"""





   def __init__(							self:    int			,a:    List[str]=None			,**a:    List[Any]	):
    super().__init__(features=a	)
    __UpperCAmelCase         =					tf_tensor_kwargs
    import tensorflow as tf  # noqa: import tf at initialization





   def 			snake_case	(							self:    Optional[Any]			,a:    str	):
    import tensorflow as tf

    if isinstance(a			,a	) and column:
     if all(
         isinstance(a			,tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
      return tf.stack(a	)
     elif all(
         isinstance(a			,(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
         for x in column	):
      # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
      return tf.ragged.stack(a	)

    return column





   def 			snake_case	(							self:    Dict			,a:    Dict	):
    import tensorflow as tf

    if value is None:
     return value

    __UpperCAmelCase         =					{}

    if isinstance(a			,(np.number, np.ndarray)	) and np.issubdtype(value.dtype			,np.integer	):
     __UpperCAmelCase         =					{'dtype': tf.intaa}
    elif isinstance(a			,(np.number, np.ndarray)	) and np.issubdtype(value.dtype			,np.floating	):
     __UpperCAmelCase         =					{'dtype': tf.floataa}
    elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(a			,PIL.Image.Image	):
      __UpperCAmelCase         =					np.asarray(a	)

    return tf.convert_to_tensor(a			,**{**default_dtype, **self.tf_tensor_kwargs}	)





   def 			snake_case	(							self:    List[Any]			,a:    int	):
    import tensorflow as tf

    # support for torch, tf, jax etc.
    if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(a			,torch.Tensor	):
      return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
    if hasattr(a			,'__array__'	) and not isinstance(a			,tf.Tensor	):
     __UpperCAmelCase         =					data_struct.__array__()
    # support for nested types like struct of list of struct
    if isinstance(a			,np.ndarray	):
     if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
      return self._consolidate([self.recursive_tensorize(a	) for substruct in data_struct]	)
    elif isinstance(a			,(list, tuple)	):
     return self._consolidate([self.recursive_tensorize(a	) for substruct in data_struct]	)
    return self._tensorize(a	)





   def 			snake_case	(							self:    Any			,a:    dict	):
    return map_nested(self._recursive_tensorize			,a			,map_list=a	)





   def 			snake_case	(							self:    List[str]			,a:    pa.Table	):
    __UpperCAmelCase         =					self.numpy_arrow_extractor().extract_row(a	)
    __UpperCAmelCase         =					self.python_features_decoder.decode_row(a	)
    return self.recursive_tensorize(a	)





   def 			snake_case	(							self:    Dict			,a:    pa.Table	):
    __UpperCAmelCase         =					self.numpy_arrow_extractor().extract_column(a	)
    __UpperCAmelCase         =					self.python_features_decoder.decode_column(a			,pa_table.column_names[0]	)
    __UpperCAmelCase         =					self.recursive_tensorize(a	)
    __UpperCAmelCase         =					self._consolidate(a	)
    return column





   def 			snake_case	(							self:    Dict			,a:    pa.Table	):
    __UpperCAmelCase         =					self.numpy_arrow_extractor().extract_batch(a	)
    __UpperCAmelCase         =					self.python_features_decoder.decode_batch(a	)
    __UpperCAmelCase         =					self.recursive_tensorize(a	)
    for column_name in batch:
     __UpperCAmelCase         =					self._consolidate(batch[column_name]	)
    return batch




'''simple docstring'''




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,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



