
"""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 		a__						:
   def __init__(			self	,    _a	,    _a=None	,    _a=None	,    _a=None	,    _a="resnet50"	,    _a=3	,    _a=32	,    _a=3	,    _a=True	,    _a=True	,    ):
          lowercase    :				List[Any]       		=							parent
          lowercase    :				str       		=							out_indices if out_indices is not None else [4]
          lowercase    :				Dict       		=							stage_names
          lowercase    :				str       		=							out_features
          lowercase    :				Tuple       		=							backbone
          lowercase    :				Optional[int]       		=							batch_size
          lowercase    :				Optional[Any]       		=							image_size
          lowercase    :				Optional[Any]       		=							num_channels
          lowercase    :				Dict       		=							use_pretrained_backbone
          lowercase    :				List[str]       		=							is_training
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
          lowercase    :				List[str]       		=							self.get_config()

          return config, pixel_values
   def 			__magic_name__			(			self				):
          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 			__magic_name__			(			self	,    _a	,    _a				):
          lowercase    :				Optional[int]       		=							TimmBackbone(config=_a				)
          model.to(_a				)
          model.eval()
          with torch.no_grad():
                 lowercase    :				List[Any]       		=							model(_a				)
          self.parent.assertEqual(
              result.feature_map[-1].shape	,    (self.batch_size, model.channels[-1], 14, 14)	,    )

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


@require_torch
@require_timm
class 		a__						(      a_,			a_,			a_,			unittest.TestCase						):
   __lowerCAmelCase            =				(TimmBackbone,) if is_torch_available() else ()
   __lowerCAmelCase            =				{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							TimmBackboneModelTester(self				)
          lowercase    :				str       		=							ConfigTester(self	,    config_class=_a	,    has_text_modality=_a				)
   def 			__magic_name__			(			self				):
          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 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							"resnet18"
          lowercase    :				List[Any]       		=							"microsoft/resnet-18"

          lowercase    :				Optional[int]       		=							AutoBackbone.from_pretrained(_a	,    use_timm_backbone=_a				)
          lowercase    :				List[Any]       		=							AutoBackbone.from_pretrained(_a				)

          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]				)

          lowercase    :				Union[str, Any]       		=							AutoBackbone.from_pretrained(_a	,    use_timm_backbone=_a	,    out_indices=[1, 2, 3]				)
          lowercase    :				List[str]       		=							AutoBackbone.from_pretrained(_a	,    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 			__magic_name__			(			self				):
          pass
   @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("TimmBackbone initialization is managed on the timm side"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("TimmBackbone models doesn't have inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("TimmBackbone models doesn't have inputs_embeds"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("model weights aren't tied in TimmBackbone."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("model weights aren't tied in TimmBackbone."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("TimmBackbone doesn't support output_attentions."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("Safetensors is not supported by timm."				)
   def 			__magic_name__			(			self				):
          pass
   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				List[Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowercase    :				List[str]       		=							["pixel_values"]
                 self.assertListEqual(arg_names[:1]	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				Union[str, Any]       		=							self.model_tester.prepare_config_and_inputs_for_common()
          lowercase    :				Any       		=							True
          lowercase    :				List[str]       		=							self.has_attentions

          # no need to test all models as different heads yield the same functionality
          lowercase    :				Optional[int]       		=							self.all_model_classes[0]
          lowercase    :				Union[str, Any]       		=							model_class(_a				)
          model.to(_a				)

          lowercase    :				List[str]       		=							self._prepare_for_class(_a	,    _a				)
          lowercase    :				int       		=							model(**_a				)
          lowercase    :				int       		=							outputs[0][-1]

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

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

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

          self.assertIsNotNone(hidden_states.grad				)

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

   def 			__magic_name__			(			self				):
          lowercase		,    lowercase    :				List[str]       		=							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 lowercase    :				Optional[Any]       		=							model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 lowercase    :				Union[str, Any]       		=							model(**_a				)

                 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
                 lowercase    :				str       		=							copy.deepcopy(_a				)
                 lowercase    :				str       		=							None
                 lowercase    :				List[Any]       		=							model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 lowercase    :				Optional[int]       		=							model(**_a				)

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

                 # Check backbone can be initialized with fresh weights
                 lowercase    :				int       		=							copy.deepcopy(_a				)
                 lowercase    :				Dict       		=							False
                 lowercase    :				Dict       		=							model_class(_a				)
                 model.to(_a				)
                 model.eval()
                 lowercase    :				List[str]       		=							model(**_a				)




"""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


