





'''simple docstring'''







from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''				,		__a				,		)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					RobertaConfig
     _UpperCAmelCase										=					'''roberta'''







     def __init__( self    ,   A__    ) ->	Any:
      super().__init__(A__    )

      snake_case				=			RobertaEmbeddings(A__    )
      self.init_weights()




@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''				,		__a				,		)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					RobertaConfig
     _UpperCAmelCase										=					'''roberta'''







     def __init__( self    ,   A__    ) ->	str:
      super().__init__(A__    )
      snake_case				=			config.num_labels
      snake_case				=			config.num_hidden_layers

      snake_case				=			DeeRobertaModel(A__    )
      snake_case				=			nn.Dropout(config.hidden_dropout_prob    )
      snake_case				=			nn.Linear(config.hidden_size    ,   self.config.num_labels    )







     @add_start_docstrings_to_model_forward(A__    )
     def     UpperCamelCase			( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__=-1    ,   A__=False    ,   ) ->	Union[str, Any]:
      snake_case				=			self.num_layers
      try:
       snake_case				=			self.roberta(
           A__    ,   attention_mask=A__    ,   token_type_ids=A__    ,   position_ids=A__    ,   head_mask=A__    ,   inputs_embeds=A__    ,   )

       snake_case				=			outputs[1]

       snake_case				=			self.dropout(A__    )
       snake_case				=			self.classifier(A__    )
       snake_case				=			(logits,) + outputs[2:]  # add hidden states and attention if they are here
      except HighwayException as e:
       snake_case				=			e.message
       snake_case				=			e.exit_layer
       snake_case				=			outputs[0]

      if not self.training:
       snake_case				=			entropy(A__    )
       snake_case				=			[]
       snake_case				=			[]
      if labels is not None:
       if self.num_labels == 1:
        #  We are doing regression
        snake_case				=			MSELoss()
        snake_case				=			loss_fct(logits.view(-1    )    ,   labels.view(-1    )    )
       else:
        snake_case				=			CrossEntropyLoss()
        snake_case				=			loss_fct(logits.view(-1    ,   self.num_labels    )    ,   labels.view(-1    )    )

       # work with highway exits
       snake_case				=			[]
       for highway_exit in outputs[-1]:
        snake_case				=			highway_exit[0]
        if not self.training:
         highway_logits_all.append(A__    )
         highway_entropy.append(highway_exit[2]    )
        if self.num_labels == 1:
         #  We are doing regression
         snake_case				=			MSELoss()
         snake_case				=			loss_fct(highway_logits.view(-1    )    ,   labels.view(-1    )    )
        else:
         snake_case				=			CrossEntropyLoss()
         snake_case				=			loss_fct(highway_logits.view(-1    ,   self.num_labels    )    ,   labels.view(-1    )    )
        highway_losses.append(A__    )

       if train_highway:
        snake_case				=			(sum(highway_losses[:-1]    ),) + outputs
        # exclude the final highway, of course
       else:
        snake_case				=			(loss,) + outputs
      if not self.training:
       snake_case				=			outputs + ((original_entropy, highway_entropy), exit_layer)
       if output_layer >= 0:
        snake_case				=			(
            (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
        )  # use the highway of the last layer

      return outputs  # (loss), logits, (hidden_states), (attentions), entropy








'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

