




'''simple docstring'''





import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
   from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class      _a       :






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=3      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=3      ,_SCREAMING_SNAKE_CASE=10      ,_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]      ,_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE="relu"      ,_SCREAMING_SNAKE_CASE=3      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=["stage2", "stage3", "stage4"]      ,_SCREAMING_SNAKE_CASE=[2, 3, 4]      ,)							->    List[Any]:
          _snake_case							=   parent
          _snake_case							=   batch_size
          _snake_case							=   image_size
          _snake_case							=   num_channels
          _snake_case							=   embeddings_size
          _snake_case							=   hidden_sizes
          _snake_case							=   depths
          _snake_case							=   is_training
          _snake_case							=   use_labels
          _snake_case							=   hidden_act
          _snake_case							=   num_labels
          _snake_case							=   scope
          _snake_case							=   len(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   out_features
          _snake_case							=   out_indices






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

          _snake_case							=   None
          if self.use_labels:
               _snake_case							=   ids_tensor([self.batch_size]      ,self.num_labels					)

          _snake_case							=   self.get_config()

          return config, pixel_values, labels






     def        _lowercase			(				self					)							->    Optional[Any]:
          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      ,out_features=self.out_features      ,out_indices=self.out_indices      ,)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Tuple:
          _snake_case							=   ResNetModel(config=_SCREAMING_SNAKE_CASE					)
          model.to(_SCREAMING_SNAKE_CASE					)
          model.eval()
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          # 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 // 32, self.image_size // 32)      ,)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Union[str, Any]:
          _snake_case							=   self.num_labels
          _snake_case							=   ResNetForImageClassification(_SCREAMING_SNAKE_CASE					)
          model.to(_SCREAMING_SNAKE_CASE					)
          model.eval()
          _snake_case							=   model(_SCREAMING_SNAKE_CASE      ,labels=_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							=   ResNetBackbone(config=_SCREAMING_SNAKE_CASE					)
          model.to(_SCREAMING_SNAKE_CASE					)
          model.eval()
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps					)      ,len(config.out_features					)					)
          self.parent.assertListEqual(list(result.feature_maps[0].shape					)      ,[self.batch_size, self.hidden_sizes[1], 4, 4]					)

          # verify channels
          self.parent.assertEqual(len(model.channels					)      ,len(config.out_features					)					)
          self.parent.assertListEqual(model.channels      ,config.hidden_sizes[1:]					)

          # verify backbone works with out_features=None
          _snake_case							=   None
          _snake_case							=   ResNetBackbone(config=_SCREAMING_SNAKE_CASE					)
          model.to(_SCREAMING_SNAKE_CASE					)
          model.eval()
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps					)      ,1					)
          self.parent.assertListEqual(list(result.feature_maps[0].shape					)      ,[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

          # verify channels
          self.parent.assertEqual(len(model.channels					)      ,1					)
          self.parent.assertListEqual(model.channels      ,[config.hidden_sizes[-1]]					)






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
          _snake_case							=   {"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class      _a       (		__lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		(
         (
             ResNetModel,
             ResNetForImageClassification,
             ResNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_							:					Any					       =		(
         {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_							:					Tuple					       =		True
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		False
     SCREAMING_SNAKE_CASE_							:					str					       =		False
     SCREAMING_SNAKE_CASE_							:					Any					       =		False
     SCREAMING_SNAKE_CASE_							:					Optional[Any]					       =		False






     def        _lowercase			(				self					)							->    Dict:
          _snake_case							=   ResNetModelTester(self					)
          _snake_case							=   ConfigTester(self      ,config_class=_SCREAMING_SNAKE_CASE      ,has_text_modality=_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    List[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        _lowercase			(				self					)							->    Union[str, Any]:
          return






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






     @unittest.skip(reason="ResNet does not support input and output embeddings"					)
     def        _lowercase			(				self					)							->    Union[str, Any]:
          pass






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

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

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






     def        _lowercase			(				self					)							->    Dict:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    str:
          _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _snake_case							=   model_class(config=_SCREAMING_SNAKE_CASE					)
               for name, module in model.named_modules():
                    if isinstance(_SCREAMING_SNAKE_CASE      ,(nn.BatchNormad, nn.GroupNorm)					):
                         self.assertTrue(
                             torch.all(module.weight == 1					)      ,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,)
                         self.assertTrue(
                             torch.all(module.bias == 0					)      ,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""      ,)






     def        _lowercase			(				self					)							->    Optional[int]:
          def check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					):
               _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
               model.to(_SCREAMING_SNAKE_CASE					)
               model.eval()

               with torch.no_grad():
                    _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)

               _snake_case							=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _snake_case							=   self.model_tester.num_stages
               self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,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]      ,)

          _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case							=   ["basic", "bottleneck"]
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    _snake_case							=   layer_type
                    _snake_case							=   True
                    check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    _snake_case							=   True

                    check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)






     @unittest.skip(reason="ResNet does not use feedforward chunking"					)
     def        _lowercase			(				self					)							->    List[Any]:
          pass






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE					)






     @slow
     def        _lowercase			(				self					)							->    str:
          for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case							=   ResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







def 			__a			(     )		->				List[Any]:

     """simple docstring"""




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

@require_torch
@require_vision
class      _a       (		unittest.TestCase							):






     @cached_property
     def        _lowercase			(				self					)							->    Optional[Any]:
          return (
              AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
              if is_vision_available()
              else None
          )






     @slow
     def        _lowercase			(				self					)							->    Dict:
          _snake_case							=   ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   self.default_image_processor
          _snake_case							=   prepare_img()
          _snake_case							=   image_processor(images=_SCREAMING_SNAKE_CASE      ,return_tensors="pt"					).to(_SCREAMING_SNAKE_CASE					)

          # forward pass
          with torch.no_grad():
               _snake_case							=   model(**_SCREAMING_SNAKE_CASE					)

          # verify the logits
          _snake_case							=   torch.Size((1, 1_000)					)
          self.assertEqual(outputs.logits.shape      ,_SCREAMING_SNAKE_CASE					)

          _snake_case							=   torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]					).to(_SCREAMING_SNAKE_CASE					)

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







@require_torch
class      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		(ResNetBackbone,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_							:					str					       =		False
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		ResNetConfig






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   ResNetModelTester(self					)








'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_		:    List[str]									=		500000

UpperCamelCase_			,			UpperCamelCase_		:    Dict									=		os.path.split(__file__)
UpperCamelCase_		:    int									=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.select(range(0      ,		len(_UpperCamelCase					)      ,		2					)					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.sort("numbers"					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				List[Any]:

     """simple docstring"""




     _snake_case							=   dataset.shuffle()





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset					)		->				Any:

     """simple docstring"""




     _snake_case							=   dataset.train_test_split(0.1					)





@get_duration
def 			__a			(     _UpperCamelCase:					datasets.Dataset      ,		_UpperCamelCase:					int=10					)		->				List[str]:

     """simple docstring"""




     for shard_id in range(_UpperCamelCase					):
          _snake_case							=   dataset.shard(_UpperCamelCase      ,		_UpperCamelCase					)





def 			__a			(     )		->				int:

     """simple docstring"""




     _snake_case							=   {"num examples": SPEED_TEST_N_EXAMPLES}
     _snake_case							=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print("generating dataset"					)
          _snake_case							=   datasets.Features({"text": datasets.Value("string"					), "numbers": datasets.Value("float32"					)}					)
          _snake_case							=   generate_example_dataset(
              os.path.join(_UpperCamelCase      ,		"dataset.arrow"					)      ,		_UpperCamelCase      ,		num_examples=_UpperCamelCase					)
          print("Functions"					)
          for func in functions:
               print(func.__name__					)
               _snake_case							=   func(_UpperCamelCase					)

     with open(_UpperCamelCase      ,		"wb"					) as f:
          f.write(json.dumps(_UpperCamelCase					).encode("utf-8"					)					)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()


