



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

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



class 		_lowercase		(							TensorFormatter[Mapping, np.ndarray, Mapping]						):

   def __init__(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[str]=None				, **__lowerCAmelCase      :							List[str]				)							->					Tuple:


    """simple docstring"""






    super().__init__(features=__lowerCAmelCase				)
    a							=		np_array_kwargs

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							int				)							->					Optional[Any]:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, __lowerCAmelCase				):
     if column and all(
         isinstance(__lowerCAmelCase				, np.ndarray				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
      return np.stack(__lowerCAmelCase				)
     else:
      # don't use np.array(column, dtype=object)
      # since it fails in certain cases
      # see https://stackoverflow.com/q/51005699
      a							=		np.empty(len(__lowerCAmelCase				)				, dtype=__lowerCAmelCase				)
      a							=		column
      return out
    return column

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Dict				)							->					List[Any]:


    """simple docstring"""






    if isinstance(__lowerCAmelCase				, (str, bytes, type(__lowerCAmelCase				))				):
     return value
    elif isinstance(__lowerCAmelCase				, (np.character, np.ndarray)				) and np.issubdtype(value.dtype				, np.character				):
     return value
    elif isinstance(__lowerCAmelCase				, np.number				):
     return value

    a							=		{}

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

     if isinstance(__lowerCAmelCase				, PIL.Image.Image				):
      return np.asarray(__lowerCAmelCase				, **self.np_array_kwargs				)

    return np.asarray(__lowerCAmelCase				, **{**default_dtype, **self.np_array_kwargs}				)

   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Union[str, Any]				)							->					Any:


    """simple docstring"""






    if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(__lowerCAmelCase				, torch.Tensor				):
      return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
    if hasattr(__lowerCAmelCase				, "__array__"				) and not isinstance(__lowerCAmelCase				, (np.ndarray, np.character, np.number)				):
     a							=		data_struct.__array__()
    # support for nested types like struct of list of struct
    if isinstance(__lowerCAmelCase				, np.ndarray				):
     if data_struct.dtype == object:
      return self._consolidate([self.recursive_tensorize(__lowerCAmelCase				) for substruct in data_struct]				)
    if isinstance(__lowerCAmelCase				, (list, tuple)				):
     return self._consolidate([self.recursive_tensorize(__lowerCAmelCase				) for substruct in data_struct]				)
    return self._tensorize(__lowerCAmelCase				)

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							dict				)							->					Optional[int]:


    """simple docstring"""






    return map_nested(self._recursive_tensorize				, __lowerCAmelCase				, map_list=__lowerCAmelCase				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							pa.Table				)							->					Mapping:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_row(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_row(__lowerCAmelCase				)
    return self.recursive_tensorize(__lowerCAmelCase				)

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							pa.Table				)							->					np.ndarray:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_column(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_column(__lowerCAmelCase				, pa_table.column_names[0]				)
    a							=		self.recursive_tensorize(__lowerCAmelCase				)
    a							=		self._consolidate(__lowerCAmelCase				)
    return column




   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							pa.Table				)							->					Mapping:


    """simple docstring"""






    a							=		self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase				)
    a							=		self.python_features_decoder.decode_batch(__lowerCAmelCase				)
    a							=		self.recursive_tensorize(__lowerCAmelCase				)
    for column_name in batch:
     a							=		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,
)


lowercase__  :	Optional[Any]            =      {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	List[Any]            =      ['MarianTokenizer']

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

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

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :	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

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




