
"""simple docstring"""






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      _SCREAMING_SNAKE_CASE   (     TensorFormatter[Mapping, np.ndarray, Mapping]				):






			def __init__(   self			,  __A=None			,  **__A			)     ->	Union[str, Any]:
						super().__init__(features=__A			)
						lowerCAmelCase_       :str     	=    np_array_kwargs






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






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[int]:
						if isinstance(__A			,  (str, bytes, type(__A			))			):
									return value
						elif isinstance(__A			,  (np.character, np.ndarray)			) and np.issubdtype(value.dtype			,  np.character			):
									return value
						elif isinstance(__A			,  np.number			):
									return value

						lowerCAmelCase_       :List[str]     	=    {}

						if isinstance(__A			,  np.ndarray			) and np.issubdtype(value.dtype			,  np.integer			):
									lowerCAmelCase_       :Optional[Any]     	=    {"""dtype""": np.intaa}
						elif isinstance(__A			,  np.ndarray			) and np.issubdtype(value.dtype			,  np.floating			):
									lowerCAmelCase_       :Optional[Any]     	=    {"""dtype""": np.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

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

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






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[Any]:
						# 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			,  (np.ndarray, np.character, np.number)			):
									lowerCAmelCase_       :Optional[int]     	=    data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__A			,  np.ndarray			):
									if data_struct.dtype == object:
												return self._consolidate([self.recursive_tensorize(__A			) for substruct in data_struct]			)
						if isinstance(__A			,  (list, tuple)			):
									return self._consolidate([self.recursive_tensorize(__A			) for substruct in data_struct]			)
						return self._tensorize(__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[Any]:
						return map_nested(self._recursive_tensorize			,  __A			,  map_list=__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Mapping:
						lowerCAmelCase_       :int     	=    self.numpy_arrow_extractor().extract_row(__A			)
						lowerCAmelCase_       :List[Any]     	=    self.python_features_decoder.decode_row(__A			)
						return self.recursive_tensorize(__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	np.ndarray:
						lowerCAmelCase_       :Optional[Any]     	=    self.numpy_arrow_extractor().extract_column(__A			)
						lowerCAmelCase_       :List[str]     	=    self.python_features_decoder.decode_column(__A			,  pa_table.column_names[0]			)
						lowerCAmelCase_       :Any     	=    self.recursive_tensorize(__A			)
						lowerCAmelCase_       :List[str]     	=    self._consolidate(__A			)
						return column






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Mapping:
						lowerCAmelCase_       :Optional[Any]     	=    self.numpy_arrow_extractor().extract_batch(__A			)
						lowerCAmelCase_       :Any     	=    self.python_features_decoder.decode_batch(__A			)
						lowerCAmelCase_       :int     	=    self.recursive_tensorize(__A			)
						for column_name in batch:
									lowerCAmelCase_       :Tuple     	=    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 					=    {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

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

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

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

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['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 					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






