



"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFResNetForImageClassification, TFResNetModel
       from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor






class      UpperCAmelCase_  :



      def __init__(					self					:     List[Any]					,     A					:     Optional[int]					,     A					:     str=3					,     A					:     Any=3_2					,     A					:     Any=3					,     A					:     Dict=1_0					,     A					:     List[str]=[1_0, 2_0, 3_0, 4_0]					,     A					:     Any=[1, 1, 2, 1]					,     A					:     Optional[Any]=True					,     A					:     Optional[Any]=True					,     A					:     Dict="relu"					,     A					:     Optional[Any]=3					,     A					:     Optional[Any]=None					,     ):
           _UpperCAmelCase      :     Dict        =   parent
           _UpperCAmelCase      :     Optional[int]        =   batch_size
           _UpperCAmelCase      :     Optional[Any]        =   image_size
           _UpperCAmelCase      :     Optional[Any]        =   num_channels
           _UpperCAmelCase      :     Dict        =   embeddings_size
           _UpperCAmelCase      :     int        =   hidden_sizes
           _UpperCAmelCase      :     Tuple        =   depths
           _UpperCAmelCase      :     Optional[Any]        =   is_training
           _UpperCAmelCase      :     Dict        =   use_labels
           _UpperCAmelCase      :     int        =   hidden_act
           _UpperCAmelCase      :     Optional[Any]        =   num_labels
           _UpperCAmelCase      :     Optional[int]        =   scope
           _UpperCAmelCase      :     List[Any]        =   len(A						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Tuple        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

           _UpperCAmelCase      :     Tuple        =   self.get_config()

           return config, pixel_values, labels



      def 						snake_case_ (					self					:     str						):
           return ResNetConfig(
               num_channels=self.num_channels					,     embeddings_size=self.embeddings_size					,     hidden_sizes=self.hidden_sizes					,     depths=self.depths					,     hidden_act=self.hidden_act					,     num_labels=self.num_labels					,     image_size=self.image_size					,     )



      def 						snake_case_ (					self					:     int					,     A					:     List[Any]					,     A					:     Optional[int]					,     A					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   TFResNetModel(config=A						)
           _UpperCAmelCase      :     Any        =   model(A						)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape					,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,     )



      def 						snake_case_ (					self					:     str					,     A					:     int					,     A					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     Optional[int]        =   self.num_labels
           _UpperCAmelCase      :     List[str]        =   TFResNetForImageClassification(A						)
           _UpperCAmelCase      :     Optional[Any]        =   model(A					,     labels=A						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     int        =   self.prepare_config_and_inputs()
           _UpperCAmelCase      :     int        =   config_and_inputs
           _UpperCAmelCase      :     Any        =   {"pixel_values": pixel_values}
           return config, inputs_dict







@require_tf
class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Any								=			(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			(
          {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			False
      __SCREAMING_SNAKE_CASE						:       Any								=			False
      __SCREAMING_SNAKE_CASE						:       List[str]								=			False
      __SCREAMING_SNAKE_CASE						:       List[str]								=			False
      __SCREAMING_SNAKE_CASE						:       str								=			False



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Union[str, Any]        =   TFResNetModelTester(self						)
           _UpperCAmelCase      :     int        =   ConfigTester(self					,     config_class=A					,     has_text_modality=A						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           self.create_and_test_config_common_properties()
           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						):
           return



      @unittest.skip(reason="ResNet does not use inputs_embeds"						)
      def 						snake_case_ (					self					:     int						):
           pass



      @unittest.skip(reason="ResNet does not support input and output embeddings"						)
      def 						snake_case_ (					self					:     Tuple						):
           pass



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Any        =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     str        =   model_class(A						)
                _UpperCAmelCase      :     int        =   inspect.signature(model.call						)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase      :     Union[str, Any]        =   [*signature.parameters.keys()]

                _UpperCAmelCase      :     str        =   ["pixel_values"]
                self.assertListEqual(arg_names[:1]					,     A						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     int        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           def check_hidden_states_output(A					:     Dict					,     A					:     Tuple					,     A					:     Any						):
                _UpperCAmelCase      :     Tuple        =   model_class(A						)
                _UpperCAmelCase      :     str        =   model(**self._prepare_for_class(A					,     A						)						)

                _UpperCAmelCase      :     List[str]        =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                _UpperCAmelCase      :     Tuple        =   self.model_tester.num_stages
                self.assertEqual(len(A						)					,     expected_num_stages + 1						)

                # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]						)					,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,     )

           _UpperCAmelCase      :     str        =   self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase      :     Union[str, Any]        =   ["basic", "bottleneck"]
           for model_class in self.all_model_classes:
                for layer_type in layers_type:
                     _UpperCAmelCase      :     List[str]        =   layer_type
                     _UpperCAmelCase      :     int        =   True
                     check_hidden_states_output(A					,     A					,     A						)

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     _UpperCAmelCase      :     Tuple        =   True

                     check_hidden_states_output(A					,     A					,     A						)



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Optional[Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*A						)



      @slow
      def 						snake_case_ (					self					:     Tuple						):
           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase      :     Union[str, Any]        =   TFResNetModel.from_pretrained(A						)
                self.assertIsNotNone(A						)







def 						__snake_case						(				)  ->       List[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
     return image






@require_tf
@require_vision
class      UpperCAmelCase_  (    unittest.TestCase						):



      @cached_property
      def 						snake_case_ (					self					:     Any						):
           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
               if is_vision_available()
               else None
           )



      @slow
      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Tuple        =   TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

           _UpperCAmelCase      :     str        =   self.default_image_processor
           _UpperCAmelCase      :     Optional[Any]        =   prepare_img()
           _UpperCAmelCase      :     str        =   image_processor(images=A					,     return_tensors="tf"						)

           # forward pass
           _UpperCAmelCase      :     Optional[int]        =   model(**A						)

           # verify the logits
           _UpperCAmelCase      :     Any        =   tf.TensorShape((1, 1_0_0_0)						)
           self.assertEqual(outputs.logits.shape					,     A						)

           _UpperCAmelCase      :     Union[str, Any]        =   tf.constant([-11.1_069, -9.7_877, -8.3_777]						)

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()					,     A					,     atol=1e-4						)						)





"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						List[str]=7    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   None
     if token is not None:
          _UpperCAmelCase      :     str        =   {"Accept": "application/vnd.github+json", "Authorization": f'Bearer {token}'}

     # The id of a workflow (not of a workflow run)
     _UpperCAmelCase      :     Any        =   "636036"

     _UpperCAmelCase      :     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}'

     _UpperCAmelCase      :     Tuple        =   requests.get(SCREAMING_SNAKE_CASE__						,  headers=SCREAMING_SNAKE_CASE__    ).json()

     return result["workflow_runs"]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   get_daily_ci_runs(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   None
     for workflow_run in workflow_runs:
          if workflow_run["status"] == "completed":
               _UpperCAmelCase      :     str        =   workflow_run["id"]
               break

     return workflow_run_id

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   get_last_daily_ci_runs(SCREAMING_SNAKE_CASE__    )
     if workflow_run_id is not None:
          _UpperCAmelCase      :     Any        =   get_artifacts_links(worflow_run_id=SCREAMING_SNAKE_CASE__						,  token=SCREAMING_SNAKE_CASE__    )
          for artifact_name in artifact_names:
               if artifact_name in artifacts_links:
                    _UpperCAmelCase      :     List[str]        =   artifacts_links[artifact_name]
                    download_artifact(
                        artifact_name=SCREAMING_SNAKE_CASE__						,  artifact_url=SCREAMING_SNAKE_CASE__						,  output_dir=SCREAMING_SNAKE_CASE__						,  token=SCREAMING_SNAKE_CASE__    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Union[str, Any]:





     '''simple docstring'''

     get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

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

     return results
