




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					,			__lowercase=None					,			**__lowercase   ):







						'''simple docstring'''




						super().__init__(features=__lowercase   )
						lowerCamelCase_				:						Dict				  =				np_array_kwargs

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




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

						lowerCamelCase_				:						int				  =				{}

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

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

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

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




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

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

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




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

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				self.numpy_arrow_extractor().extract_row(__lowercase   )
						lowerCamelCase_				:						int				  =				self.python_features_decoder.decode_row(__lowercase   )
						return self.recursive_tensorize(__lowercase   )

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				self.numpy_arrow_extractor().extract_column(__lowercase   )
						lowerCamelCase_				:						int				  =				self.python_features_decoder.decode_column(__lowercase					,			pa_table.column_names[0]   )
						lowerCamelCase_				:						int				  =				self.recursive_tensorize(__lowercase   )
						lowerCamelCase_				:						List[Any]				  =				self._consolidate(__lowercase   )
						return column





					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				self.numpy_arrow_extractor().extract_batch(__lowercase   )
						lowerCamelCase_				:						int				  =				self.python_features_decoder.decode_batch(__lowercase   )
						lowerCamelCase_				:						List[str]				  =				self.recursive_tensorize(__lowercase   )
						for column_name in batch:
							lowerCamelCase_				:						int				  =				self._consolidate(batch[column_name]   )
						return batch






from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['LayoutLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'LayoutLMForMaskedLM',
							    'LayoutLMForSequenceClassification',
							    'LayoutLMForTokenClassification',
							    'LayoutLMForQuestionAnswering',
							    'LayoutLMModel',
							    'LayoutLMPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFLayoutLMForMaskedLM',
							    'TFLayoutLMForSequenceClassification',
							    'TFLayoutLMForTokenClassification',
							    'TFLayoutLMForQuestionAnswering',
							    'TFLayoutLMMainLayer',
							    'TFLayoutLMModel',
							    'TFLayoutLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
