

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  :      List[str]			 =logging.get_logger(__name__)

@add_end_docstrings(_a		)
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      *lowerCamelCase__				,      **lowerCamelCase__						):
						super().__init__(*__lowerCAmelCase				,      **__lowerCAmelCase						)
						requires_backends(self				,      "vision"						)
						self.check_model_type(__lowerCAmelCase						)





					def __call__(				self				,      lowerCamelCase__				,      **lowerCamelCase__						):
						return super().__call__(__lowerCAmelCase				,      **__lowerCAmelCase						)





					def    _UpperCAmelCase    (				self				,      **lowerCamelCase__						):
						return {}, {}, {}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= load_image(__lowerCAmelCase						)
						UpperCAmelCase__:	Dict									= image.size
						UpperCAmelCase__:	Union[str, Any]									= self.image_processor(images=__lowerCAmelCase				,      return_tensors=self.framework						)
						return model_inputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[Any]									= self.model(**__lowerCAmelCase						)
						return model_outputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[Any]									= model_outputs.predicted_depth
						UpperCAmelCase__:	Optional[int]									= torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1						)				,      size=self.image_size[::-1]				,      mode="bicubic"				,      align_corners=__lowerCAmelCase						)
						UpperCAmelCase__:	Dict									= prediction.squeeze().cpu().numpy()
						UpperCAmelCase__:	Optional[int]									= (output * 2_5_5 / np.max(__lowerCAmelCase						)).astype("uint8"						)
						UpperCAmelCase__:	Optional[int]									= Image.fromarray(__lowerCAmelCase						)
						UpperCAmelCase__:	Any									= {}
						UpperCAmelCase__:	Optional[int]									= predicted_depth
						UpperCAmelCase__:	Tuple									= depth
						return output_dict






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A       :   Optional[int]					= {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Optional[int]					= ['''Pix2StructImageProcessor''']


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= [
					    '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Pix2StructPreTrainedModel''',
					    '''Pix2StructForConditionalGeneration''',
					    '''Pix2StructVisionModel''',
					    '''Pix2StructTextModel''',
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

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



