
"""simple docstring"""




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
   from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor

class 		a__						:
   def __init__(			self	,    _a	,    _a=2	,    _a=32	,    _a=16	,    _a=3	,    _a=True	,    _a=True	,    _a=32	,    _a=4	,    _a=[0, 1, 2, 3]	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=0.0_2	,    _a=3	,    _a=[1, 384, 24, 24]	,    _a=True	,    _a=None	,    ):
          lowercase    :				List[Any]       		=							parent
          lowercase    :				int       		=							batch_size
          lowercase    :				int       		=							image_size
          lowercase    :				Optional[Any]       		=							patch_size
          lowercase    :				List[Any]       		=							num_channels
          lowercase    :				Optional[int]       		=							is_training
          lowercase    :				Tuple       		=							use_labels
          lowercase    :				Optional[Any]       		=							hidden_size
          lowercase    :				Any       		=							num_hidden_layers
          lowercase    :				int       		=							backbone_out_indices
          lowercase    :				int       		=							num_attention_heads
          lowercase    :				Any       		=							intermediate_size
          lowercase    :				Optional[int]       		=							hidden_act
          lowercase    :				Union[str, Any]       		=							hidden_dropout_prob
          lowercase    :				List[str]       		=							attention_probs_dropout_prob
          lowercase    :				Tuple       		=							initializer_range
          lowercase    :				Any       		=							num_labels
          lowercase    :				Union[str, Any]       		=							backbone_featmap_shape
          lowercase    :				Optional[int]       		=							scope
          lowercase    :				List[str]       		=							is_hybrid
          # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
          lowercase    :				Tuple       		=							(image_size // patch_size) ** 2
          lowercase    :				int       		=							num_patches + 1
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          lowercase    :				List[Any]       		=							None
          if self.use_labels:
                 lowercase    :				Tuple       		=							ids_tensor([self.batch_size, self.image_size, self.image_size]	,    self.num_labels				)

          lowercase    :				Optional[int]       		=							self.get_config()

          return config, pixel_values, labels
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							{
              "global_padding": "same",
              "layer_type": "bottleneck",
              "depths": [3, 4, 9],
              "out_features": ["stage1", "stage2", "stage3"],
              "embedding_dynamic_padding": True,
              "hidden_sizes": [96, 192, 384, 768],
              "num_groups": 2,
          }

          return DPTConfig(
              image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    backbone_out_indices=self.backbone_out_indices	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    is_decoder=_a	,    initializer_range=self.initializer_range	,    is_hybrid=self.is_hybrid	,    backbone_config=_a	,    backbone_featmap_shape=self.backbone_featmap_shape	,    )
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Optional[Any]       		=							DPTModel(config=_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				Optional[Any]       		=							model(_a				)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				Tuple       		=							self.num_labels
          lowercase    :				Any       		=							DPTForDepthEstimation(_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				Any       		=							model(_a				)
          self.parent.assertEqual(result.predicted_depth.shape	,    (self.batch_size, self.image_size, self.image_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[Any]       		=							self.num_labels
          lowercase    :				int       		=							DPTForSemanticSegmentation(_a				)
          model.to(_a				)
          model.eval()
          lowercase    :				Optional[Any]       		=							model(_a	,    labels=_a				)
          self.parent.assertEqual(
              result.logits.shape	,    (self.batch_size, self.num_labels, self.image_size, self.image_size)				)

   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.prepare_config_and_inputs()
          lowercase		,    lowercase		,    lowercase    :				str       		=							config_and_inputs
          lowercase    :				List[Any]       		=							{"pixel_values": pixel_values}
          return config, inputs_dict


@require_torch
class 		a__						(      a_,			a_,			unittest.TestCase						):
   __lowerCAmelCase            =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   __lowerCAmelCase            =				(
       {
           """depth-estimation""": DPTForDepthEstimation,
           """feature-extraction""": DPTModel,
           """image-segmentation""": DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							DPTModelTester(self				)
          lowercase    :				Optional[Any]       		=							ConfigTester(self	,    config_class=_a	,    has_text_modality=_a	,    hidden_size=37				)
   def 			__magic_name__			(			self				):
          self.config_tester.run_common_tests()
   @unittest.skip(reason="DPT does not use inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Union[str, Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Any       		=							model_class(_a				)
                 self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)				)
                 lowercase    :				Union[str, Any]       		=							model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_a	,    nn.Linear				)				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Union[str, Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Optional[Any]       		=							model_class(_a				)
                 lowercase    :				Tuple       		=							inspect.signature(model.forward				)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowercase    :				Union[str, Any]       		=							[*signature.parameters.keys()]

                 lowercase    :				Union[str, Any]       		=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_a				)
   def 			__magic_name__			(			self				):
          for model_class in self.all_model_classes:
                 if model_class.__name__ == "DPTForDepthEstimation":
                        continue

                 lowercase		,    lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs_for_common()
                 lowercase    :				Dict       		=							True

                 if model_class in get_values(_a				):
                        continue

                 lowercase    :				int       		=							model_class(_a				)
                 model.to(_a				)
                 model.train()
                 lowercase    :				Optional[Any]       		=							self._prepare_for_class(_a	,    _a	,    return_labels=_a				)
                 lowercase    :				int       		=							model(**_a				).loss
                 loss.backward()
   def 			__magic_name__			(			self				):
          for model_class in self.all_model_classes:
                 if model_class.__name__ == "DPTForDepthEstimation":
                        continue

                 lowercase		,    lowercase    :				Union[str, Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()
                 lowercase    :				Optional[int]       		=							False
                 lowercase    :				Optional[Any]       		=							True

                 if model_class in get_values(_a				) or not model_class.supports_gradient_checkpointing:
                        continue
                 lowercase    :				int       		=							model_class(_a				)
                 model.to(_a				)
                 model.gradient_checkpointing_enable()
                 model.train()
                 lowercase    :				int       		=							self._prepare_for_class(_a	,    _a	,    return_labels=_a				)
                 lowercase    :				Tuple       		=							model(**_a				).loss
                 loss.backward()
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Tuple       		=							self.model_tester.prepare_config_and_inputs_for_common()

          lowercase    :				Tuple       		=							_config_zero_init(_a				)
          for model_class in self.all_model_classes:
                 lowercase    :				List[str]       		=							model_class(config=_a				)
                 # Skip the check for the backbone
                 lowercase    :				Union[str, Any]       		=							[]
                 for name, module in model.named_modules():
                        if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                               lowercase    :				int       		=							[f"""{name}.{key}""" for key in module.state_dict().keys()]
                               break

                 for name, param in model.named_parameters():
                        if param.requires_grad:
                               if name in backbone_params:
                                      continue
                               self.assertIn(
                                   ((param.data.mean() * 1E9).round() / 1E9).item()	,    [0.0, 1.0]	,    msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""	,    )
   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
   def 			__magic_name__			(			self				):
          pass
   @slow
   def 			__magic_name__			(			self				):
          for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                 lowercase    :				Dict       		=							DPTModel.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)

   def 			__magic_name__			(			self				):
          # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
          lowercase		,    lowercase    :				Optional[Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				Dict       		=							"add"
          with self.assertRaises(_a				):
                 lowercase    :				Optional[int]       		=							DPTForDepthEstimation(_a				)


def   __magic_name__	(      ) ->				List[Any]:
       lowercase    :				List[Any]       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image

@require_torch
@require_vision
@slow
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"				)
          lowercase    :				List[str]       		=							DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"				).to(_a				)

          lowercase    :				Tuple       		=							prepare_img()
          lowercase    :				Tuple       		=							image_processor(images=_a	,    return_tensors="pt"				).to(_a				)

          # forward pass
          with torch.no_grad():
                 lowercase    :				Any       		=							model(**_a				)
                 lowercase    :				Optional[int]       		=							outputs.predicted_depth

          # verify the predicted depth
          lowercase    :				List[Any]       		=							torch.Size((1, 384, 384)				)
          self.assertEqual(predicted_depth.shape	,    _a				)

          lowercase    :				Union[str, Any]       		=							torch.tensor(
              [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]				).to(_a				)

          self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100	,    _a	,    atol=1E-4				)				)




"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def   __magic_name__	(      __snake_case				:       Union[str, Any]     , __snake_case				:       List[str]=7  ) ->				str:
       lowercase    :				int       		=							None
       if token is not None:
              lowercase    :				Any       		=							{"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

       # The id of a workflow (not of a workflow run)
       lowercase    :				int       		=							"636036"

       lowercase    :				Dict       		=							f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
       # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
       url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

       lowercase    :				int       		=							requests.get(__snake_case     , headers=__snake_case  ).json()

       return result["workflow_runs"]


def   __magic_name__	(      __snake_case				:       Dict  ) ->				Tuple:
       lowercase    :				Tuple       		=							get_daily_ci_runs(__snake_case  )
       lowercase    :				Union[str, Any]       		=							None
       for workflow_run in workflow_runs:
              if workflow_run["status"] == "completed":
                     lowercase    :				List[Any]       		=							workflow_run["id"]
                     break

       return workflow_run_id


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Optional[Any]     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase    :				Dict       		=							get_last_daily_ci_runs(__snake_case  )
       if workflow_run_id is not None:
              lowercase    :				Dict       		=							get_artifacts_links(worflow_run_id=__snake_case     , token=__snake_case  )
              for artifact_name in artifact_names:
                     if artifact_name in artifacts_links:
                            lowercase    :				Optional[int]       		=							artifacts_links[artifact_name]
                            download_artifact(
                                artifact_name=__snake_case     , artifact_url=__snake_case     , output_dir=__snake_case     , token=__snake_case  )


def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       Optional[int]     , __snake_case				:       Tuple  ) ->				Optional[int]:
       get_last_daily_ci_artifacts(__snake_case     , __snake_case     , __snake_case  )

       lowercase    :				str       		=							{}
       for artifact_name in artifact_names:
              lowercase    :				Optional[Any]       		=							os.path.join(__snake_case     , f"""{artifact_name}.zip"""  )
              if os.path.isfile(__snake_case  ):
                     lowercase    :				List[Any]       		=							{}
                     with zipfile.ZipFile(__snake_case  ) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(__snake_case  ):
                                          # read the file
                                          with z.open(__snake_case  ) as f:
                                                 lowercase    :				str       		=							f.read().decode("UTF-8"  )

       return results


