




'''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([-11.1_069, -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 unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )

class      _a       (		unittest.TestCase							):






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=13      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=24      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=16      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=4      ,)							->    List[str]:
          _snake_case							=   parent
          _snake_case							=   batch_size
          _snake_case							=   seq_length
          _snake_case							=   is_training
          _snake_case							=   use_attention_mask
          _snake_case							=   use_token_type_ids
          _snake_case							=   use_labels
          _snake_case							=   vocab_size
          _snake_case							=   hidden_size
          _snake_case							=   embedding_size
          _snake_case							=   num_hidden_layers
          _snake_case							=   num_attention_heads
          _snake_case							=   intermediate_size
          _snake_case							=   hidden_act
          _snake_case							=   hidden_dropout_prob
          _snake_case							=   attention_probs_dropout_prob
          _snake_case							=   max_position_embeddings
          _snake_case							=   type_vocab_size
          _snake_case							=   type_sequence_label_size
          _snake_case							=   initializer_range
          _snake_case							=   num_choices






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)

          _snake_case							=   None
          if self.use_attention_mask:
               _snake_case							=   random_attention_mask([self.batch_size, self.seq_length]					)

          _snake_case							=   None
          if self.use_token_type_ids:
               _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size					)

          _snake_case							=   ElectraConfig(
              vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,embedding_size=self.embedding_size      ,num_hidden_layers=self.num_hidden_layers      ,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      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,initializer_range=self.initializer_range      ,)

          return config, input_ids, token_type_ids, attention_mask






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
          _snake_case							=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict







@require_flax
class      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		True

     SCREAMING_SNAKE_CASE_							:					Any					       =		(
         (
             FlaxElectraModel,
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,
             FlaxElectraForPreTraining,
             FlaxElectraForTokenClassification,
             FlaxElectraForQuestionAnswering,
             FlaxElectraForMultipleChoice,
             FlaxElectraForSequenceClassification,
         )
         if is_flax_available()
         else ()
     )






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   FlaxElectraModelTester(self					)






     @slow
     def        _lowercase			(				self					)							->    List[Any]:
          for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-generator"					)
               else:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-discriminator"					)
               _snake_case							=   model(np.ones((1, 1)					)					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)


