

# 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 torch


class SCREAMING_SNAKE_CASE  (		TensorFormatter[Mapping, """torch.Tensor""", Mapping]							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

				"""simple docstring"""


				super().__init__(features=__UpperCamelCase							)
				snake_case_      		= torch_tensor_kwargs
				import torch  # noqa import torch at initialization






			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				import torch

				if isinstance(__UpperCamelCase ,				__UpperCamelCase							) and column:
					if all(
					    isinstance(__UpperCamelCase ,				torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
					    for x in column							):
						return torch.stack(__UpperCamelCase							)
				return column






			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				import torch

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

				snake_case_      		= {}

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

					if isinstance(__UpperCamelCase ,				PIL.Image.Image							):
						snake_case_      		= np.asarray(__UpperCamelCase							)
				return torch.tensor(__UpperCamelCase ,				**{**default_dtype, **self.torch_tensor_kwargs}							)






			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				import torch

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






			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


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






			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.numpy_arrow_extractor().extract_row(__UpperCamelCase							)
				snake_case_      		= self.python_features_decoder.decode_row(__UpperCamelCase							)
				return self.recursive_tensorize(__UpperCamelCase							)






			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


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







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.numpy_arrow_extractor().extract_batch(__UpperCamelCase							)
				snake_case_      		= self.python_features_decoder.decode_batch(__UpperCamelCase							)
				snake_case_      		= self.recursive_tensorize(__UpperCamelCase							)
				for column_name in batch:
					snake_case_      		= self._consolidate(batch[column_name]							)
				return batch



from typing import TYPE_CHECKING

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


A											=     {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['PerceiverFeatureExtractor']
		A											=     ['PerceiverImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PerceiverForImageClassificationConvProcessing',
		    'PerceiverForImageClassificationFourier',
		    'PerceiverForImageClassificationLearned',
		    'PerceiverForMaskedLM',
		    'PerceiverForMultimodalAutoencoding',
		    'PerceiverForOpticalFlow',
		    'PerceiverForSequenceClassification',
		    'PerceiverLayer',
		    'PerceiverModel',
		    'PerceiverPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

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