




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 			_lowerCamelCase    :

      """simple docstring"""

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







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

          __UpperCamelCase			=    self.get_config()

          return config, pixel_values, labels







      def    snake_case						(  self       :			Tuple					):
          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    snake_case						(  self       :			List[str]			,							snake_case       :			Optional[int]			,							snake_case       :			Tuple			,							snake_case       :			int					):
          __UpperCamelCase			=    ResNetModel(config=snake_case					)
          model.to(snake_case					)
          model.eval()
          __UpperCamelCase			=    model(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    snake_case						(  self       :			Tuple			,							snake_case       :			Union[str, Any]			,							snake_case       :			List[str]			,							snake_case       :			List[str]					):
          __UpperCamelCase			=    self.num_labels
          __UpperCamelCase			=    ResNetForImageClassification(snake_case					)
          model.to(snake_case					)
          model.eval()
          __UpperCamelCase			=    model(snake_case			,							labels=snake_case					)
          self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)					)







      def    snake_case						(  self       :			int			,							snake_case       :			Dict			,							snake_case       :			List[str]			,							snake_case       :			Optional[int]					):
          __UpperCamelCase			=    ResNetBackbone(config=snake_case					)
          model.to(snake_case					)
          model.eval()
          __UpperCamelCase			=    model(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
          __UpperCamelCase			=    None
          __UpperCamelCase			=    ResNetBackbone(config=snake_case					)
          model.to(snake_case					)
          model.eval()
          __UpperCamelCase			=    model(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    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.prepare_config_and_inputs()
          __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    config_and_inputs
          __UpperCamelCase			=    {'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			List[str]     =       (
          (
              ResNetModel,
              ResNetForImageClassification,
              ResNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__      :			Optional[int]     =       (
          {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__      :			Optional[Any]     =       True
      lowerCAmelCase__      :			Union[str, Any]     =       False
      lowerCAmelCase__      :			Optional[int]     =       False
      lowerCAmelCase__      :			Tuple     =       False
      lowerCAmelCase__      :			Any     =       False







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    ResNetModelTester(self					)
          __UpperCamelCase			=    ConfigTester(self			,							config_class=snake_case			,							has_text_modality=snake_case					)







      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       :			int					):
          return







      @unittest.skip(reason='''ResNet does not use inputs_embeds'''					)
      def    snake_case						(  self       :			Union[str, Any]					):
          pass







      @unittest.skip(reason='''ResNet does not support input and output embeddings'''					)
      def    snake_case						(  self       :			Optional[int]					):
          pass







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase			=    ['''pixel_values''']
              self.assertListEqual(arg_names[:1]			,							snake_case					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case					)







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase			=    model_class(config=snake_case					)
              for name, module in model.named_modules():
                  if isinstance(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    snake_case						(  self       :			Optional[int]					):
          def check_hidden_states_output(snake_case       :			str			,							snake_case       :			Union[str, Any]			,							snake_case       :			str					):
              __UpperCamelCase			=    model_class(snake_case					)
              model.to(snake_case					)
              model.eval()

              with torch.no_grad():
                  __UpperCamelCase			=    model(**self._prepare_for_class(snake_case			,							snake_case					)					)

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

              __UpperCamelCase			=    self.model_tester.num_stages
              self.assertEqual(len(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]			,							)

          __UpperCamelCase		,   __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs_for_common()
          __UpperCamelCase			=    ['''basic''', '''bottleneck''']
          for model_class in self.all_model_classes:
              for layer_type in layers_type:
                  __UpperCamelCase			=    layer_type
                  __UpperCamelCase			=    True
                  check_hidden_states_output(snake_case			,							snake_case			,							snake_case					)

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

                  check_hidden_states_output(snake_case			,							snake_case			,							snake_case					)







      @unittest.skip(reason='''ResNet does not use feedforward chunking'''					)
      def    snake_case						(  self       :			Any					):
          pass







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case					)







      @slow
      def    snake_case						(  self       :			Union[str, Any]					):
          for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase			=    ResNetModel.from_pretrained(snake_case					)
              self.assertIsNotNone(snake_case					)







def        __SCREAMING_SNAKE_CASE (	)							->						int:

    """simple docstring"""


    __UpperCamelCase			=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
    return image

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

      """simple docstring"""

      @cached_property
      def    snake_case						(  self       :			str					):
          return (
              AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
              if is_vision_available()
              else None
          )







      @slow
      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(snake_case					)

          __UpperCamelCase			=    self.default_image_processor
          __UpperCamelCase			=    prepare_img()
          __UpperCamelCase			=    image_processor(images=snake_case			,							return_tensors='''pt'''					).to(snake_case					)

          # forward pass
          with torch.no_grad():
              __UpperCamelCase			=    model(**snake_case					)

          # verify the logits
          __UpperCamelCase			=    torch.Size((1, 1000)					)
          self.assertEqual(outputs.logits.shape			,							snake_case					)

          __UpperCamelCase			=    torch.tensor([-11.10_69, -9.78_77, -8.37_77]					).to(snake_case					)

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







@require_torch
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       (ResNetBackbone,) if is_torch_available() else ()
      lowerCAmelCase__      :			List[str]     =       False
      lowerCAmelCase__      :			int     =       ResNetConfig







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    ResNetModelTester(self					)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A										=      {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      ['DetrFeatureExtractor']
				A										=      ['DetrImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      [
				    'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetrForObjectDetection',
				    'DetrForSegmentation',
				    'DetrModel',
				    'DetrPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				A										=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)