





from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_lowerCamelCase   				=			logging.get_logger(__name__)




@add_end_docstrings(lowercase__    )
class   UpperCamelCase_       (     lowercase__    ):


							def __init__(							self    :List[str]							,				*__A    :int							,				**__A    :List[Any]		)   ->   Optional[int]:



									"""simple docstring"""



									super().__init__(*__lowercase							,				**__lowercase		)
									requires_backends(self							,				"""vision"""		)
									self.check_model_type(__lowercase		)


							def __call__(							self    :List[str]							,				__A    :Union[str, List[str], "Image.Image", List["Image.Image"]]							,				**__A    :int		)   ->   Tuple:



									"""simple docstring"""



									return super().__call__(__lowercase							,				**__lowercase		)


							def      _snake_case  (							self    :Optional[int]							,				**__A    :int		)   ->   Optional[int]:



									"""simple docstring"""



									return {}, {}, {}


							def      _snake_case  (							self    :List[str]							,				__A    :Union[str, Any]		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      load_image(__lowercase		)
									SCREAMING_SNAKE_CASE__			=      image.size
									SCREAMING_SNAKE_CASE__			=      self.image_processor(images=__lowercase							,				return_tensors=self.framework		)
									return model_inputs


							def      _snake_case  (							self    :int							,				__A    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.model(**__lowercase		)
									return model_outputs


							def      _snake_case  (							self    :List[Any]							,				__A    :Optional[int]		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      model_outputs.predicted_depth
									SCREAMING_SNAKE_CASE__			=      torch.nn.functional.interpolate(
									    predicted_depth.unsqueeze(1		)							,				size=self.image_size[::-1]							,				mode="""bicubic"""							,				align_corners=__lowercase		)
									SCREAMING_SNAKE_CASE__			=      prediction.squeeze().cpu().numpy()
									SCREAMING_SNAKE_CASE__			=      (output * 255 / np.max(__lowercase		)).astype("""uint8"""		)
									SCREAMING_SNAKE_CASE__			=      Image.fromarray(__lowercase		)
									SCREAMING_SNAKE_CASE__			=      {}
									SCREAMING_SNAKE_CASE__			=      predicted_depth
									SCREAMING_SNAKE_CASE__			=      depth
									return output_dict






from typing import TYPE_CHECKING

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


_lowerCamelCase   				=			{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3TokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'LayoutLMv3ForQuestionAnswering',
	    'LayoutLMv3ForSequenceClassification',
	    'LayoutLMv3ForTokenClassification',
	    'LayoutLMv3Model',
	    'LayoutLMv3PreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLayoutLMv3ForQuestionAnswering',
	    'TFLayoutLMv3ForSequenceClassification',
	    'TFLayoutLMv3ForTokenClassification',
	    'TFLayoutLMv3Model',
	    'TFLayoutLMv3PreTrainedModel',
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3FeatureExtractor']
	_lowerCamelCase   				=			['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
	from .configuration_layoutlmva import (
	    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    LayoutLMvaConfig,
	    LayoutLMvaOnnxConfig,
	)
	from .processing_layoutlmva import LayoutLMvaProcessor
	from .tokenization_layoutlmva import LayoutLMvaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		    LayoutLMvaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_layoutlmva import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		    TFLayoutLMvaPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
		from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
	import sys

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