





'''simple docstring'''


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
       import torch

       from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class    lowerCAmelCase_		:




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase="resnet50"			,     _lowerCAmelCase=3			,     _lowerCAmelCase=32			,     _lowerCAmelCase=3			,     _lowerCAmelCase=True			,     _lowerCAmelCase=True			,     )    ->							str:
     _lowerCAmelCase				=      parent
     _lowerCAmelCase				=      out_indices if out_indices is not None else [4]
     _lowerCAmelCase				=      stage_names
     _lowerCAmelCase				=      out_features
     _lowerCAmelCase				=      backbone
     _lowerCAmelCase				=      batch_size
     _lowerCAmelCase				=      image_size
     _lowerCAmelCase				=      num_channels
     _lowerCAmelCase				=      use_pretrained_backbone
     _lowerCAmelCase				=      is_training




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
     _lowerCAmelCase				=      self.get_config()

     return config, pixel_values




 def 							_snake_case   (	self		)    ->							Optional[int]:
     return TimmBackboneConfig(
         image_size=self.image_size			,     num_channels=self.num_channels			,     out_features=self.out_features			,     out_indices=self.out_indices			,     stage_names=self.stage_names			,     use_pretrained_backbone=self.use_pretrained_backbone			,     backbone=self.backbone			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      TimmBackbone(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     with torch.no_grad():
         _lowerCAmelCase				=      model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.feature_map[-1].shape			,     (self.batch_size, model.channels[-1], 14, 14)			,     )





 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs
     _lowerCAmelCase				=      {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
@require_timm
class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							str         =					(TimmBackbone,) if is_torch_available() else ()
 __lowerCamelCase						:							Optional[Any]         =					{"feature-extraction": TimmBackbone} if is_torch_available() else {}
 __lowerCamelCase						:							List[str]         =					False
 __lowerCamelCase						:							Optional[Any]         =					False
 __lowerCamelCase						:							Union[str, Any]         =					False
 __lowerCamelCase						:							int         =					False




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      TimmBackboneModelTester(self		)
     _lowerCAmelCase				=      ConfigTester(self			,     config_class=_lowerCAmelCase			,     has_text_modality=_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							int:
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      "resnet18"
     _lowerCAmelCase				=      "microsoft/resnet-18"

     _lowerCAmelCase				=      AutoBackbone.from_pretrained(_lowerCAmelCase			,     use_timm_backbone=_lowerCAmelCase		)
     _lowerCAmelCase				=      AutoBackbone.from_pretrained(_lowerCAmelCase		)

     self.assertEqual(len(timm_model.out_features		)			,     len(transformers_model.out_features		)		)
     self.assertEqual(len(timm_model.stage_names		)			,     len(transformers_model.stage_names		)		)
     self.assertEqual(timm_model.channels			,     transformers_model.channels		)
     # Out indices are set to the last layer by default. For timm models, we don't know
     # the number of layers in advance, so we set it to (-1,), whereas for transformers
     # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
     self.assertEqual(timm_model.out_indices			,     (-1,)		)
     self.assertEqual(transformers_model.out_indices			,     [len(timm_model.stage_names		) - 1]		)

     _lowerCAmelCase				=      AutoBackbone.from_pretrained(_lowerCAmelCase			,     use_timm_backbone=_lowerCAmelCase			,     out_indices=[1, 2, 3]		)
     _lowerCAmelCase				=      AutoBackbone.from_pretrained(_lowerCAmelCase			,     out_indices=[1, 2, 3]		)

     self.assertEqual(timm_model.out_indices			,     transformers_model.out_indices		)
     self.assertEqual(len(timm_model.out_features		)			,     len(transformers_model.out_features		)		)
     self.assertEqual(timm_model.channels			,     transformers_model.channels		)




 @unittest.skip("TimmBackbone doesn't support feed forward chunking"		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass




 @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"		)
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     pass




 @unittest.skip("TimmBackbone initialization is managed on the timm side"		)
 def 							_snake_case   (	self		)    ->							Optional[Any]:
     pass




 @unittest.skip("TimmBackbone models doesn't have inputs_embeds"		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass




 @unittest.skip("TimmBackbone models doesn't have inputs_embeds"		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass




 @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"		)
 def 							_snake_case   (	self		)    ->							str:
     pass




 @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"		)
 def 							_snake_case   (	self		)    ->							List[str]:
     pass




 @unittest.skip("model weights aren't tied in TimmBackbone."		)
 def 							_snake_case   (	self		)    ->							Optional[int]:
     pass




 @unittest.skip("model weights aren't tied in TimmBackbone."		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass




 @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"		)
 def 							_snake_case   (	self		)    ->							Any:
     pass




 @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"		)
 def 							_snake_case   (	self		)    ->							Optional[Any]:
     pass




 @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."		)
 def 							_snake_case   (	self		)    ->							List[str]:
     pass




 @unittest.skip("TimmBackbone doesn't support output_attentions."		)
 def 							_snake_case   (	self		)    ->							List[str]:
     pass




 @unittest.skip("Safetensors is not supported by timm."		)
 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     pass




 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
 def 							_snake_case   (	self		)    ->							Optional[int]:
     pass




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         _lowerCAmelCase				=      inspect.signature(model.forward		)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase				=      [*signature.parameters.keys()]

         _lowerCAmelCase				=      ["pixel_values"]
         self.assertListEqual(arg_names[:1]			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase				=      True
     _lowerCAmelCase				=      self.has_attentions

     # no need to test all models as different heads yield the same functionality
     _lowerCAmelCase				=      self.all_model_classes[0]
     _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)

     _lowerCAmelCase				=      self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase		)
     _lowerCAmelCase				=      model(**_lowerCAmelCase		)
     _lowerCAmelCase				=      outputs[0][-1]

     # Encoder-/Decoder-only models
     _lowerCAmelCase				=      outputs.hidden_states[0]
     hidden_states.retain_grad()

     if self.has_attentions:
         _lowerCAmelCase				=      outputs.attentions[0]
         attentions.retain_grad()

     output.flatten()[0].backward(retain_graph=_lowerCAmelCase		)

     self.assertIsNotNone(hidden_states.grad		)

     if self.has_attentions:
         self.assertIsNotNone(attentions.grad		)





 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         _lowerCAmelCase				=      model(**_lowerCAmelCase		)

         self.assertEqual(len(result.feature_maps		)			,     len(config.out_indices		)		)
         self.assertEqual(len(model.channels		)			,     len(config.out_indices		)		)

         # Check output of last stage is taken if out_features=None, out_indices=None
         _lowerCAmelCase				=      copy.deepcopy(_lowerCAmelCase		)
         _lowerCAmelCase				=      None
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         _lowerCAmelCase				=      model(**_lowerCAmelCase		)

         self.assertEqual(len(result.feature_maps		)			,     1		)
         self.assertEqual(len(model.channels		)			,     1		)

         # Check backbone can be initialized with fresh weights
         _lowerCAmelCase				=      copy.deepcopy(_lowerCAmelCase		)
         _lowerCAmelCase				=      False
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.eval()
         _lowerCAmelCase				=      model(**_lowerCAmelCase		)









'''simple docstring'''


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

_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

@add_end_docstrings(__magic_name__		)
class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[Any]:
     super().__init__(*_lowerCAmelCase			,     **_lowerCAmelCase		)
     requires_backends(self			,     "vision"		)
     self.check_model_type(_lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							str:
     return super().__call__(_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Tuple:
     return {}, {}, {}




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      load_image(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.size
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=self.framework		)
     return model_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.model(**_lowerCAmelCase		)
     return model_outputs





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


