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




      def   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						int							   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

             _UpperCAmelCase  :						List[str]							   =					self.get_config()

             return config, pixel_values, labels




      def   _A			( 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		,      out_features=self.out_features		,      out_indices=self.out_indices		,      )




      def   _A			( self		:       Optional[Any]		,      A		:       Union[str, Any]		,      A		:       Union[str, Any]		,      A		:       Optional[Any] ):
             _UpperCAmelCase  :						Optional[Any]							   =					ResNetModel(config=a_ )
             model.to(a_ )
             model.eval()
             _UpperCAmelCase  :						Dict							   =					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 // 32, self.image_size // 32)		,      )




      def   _A			( self		:       List[Any]		,      A		:       Any		,      A		:       Optional[int]		,      A		:       Tuple ):
             _UpperCAmelCase  :						str							   =					self.num_labels
             _UpperCAmelCase  :						Dict							   =					ResNetForImageClassification(a_ )
             model.to(a_ )
             model.eval()
             _UpperCAmelCase  :						str							   =					model(a_		,      labels=a_ )
             self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels) )




      def   _A			( self		:       Dict		,      A		:       Optional[Any]		,      A		:       Tuple		,      A		:       Any ):
             _UpperCAmelCase  :						Union[str, Any]							   =					ResNetBackbone(config=a_ )
             model.to(a_ )
             model.eval()
             _UpperCAmelCase  :						int							   =					model(a_ )

             # 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  :						Union[str, Any]							   =					None
             _UpperCAmelCase  :						str							   =					ResNetBackbone(config=a_ )
             model.to(a_ )
             model.eval()
             _UpperCAmelCase  :						str							   =					model(a_ )

             # 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   _A			( self		:       List[str] ):
             _UpperCAmelCase  :						List[Any]							   =					self.prepare_config_and_inputs()
             _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						Optional[Any]							   =					config_and_inputs
             _UpperCAmelCase  :						Any							   =					{"pixel_values": pixel_values}
             return config, inputs_dict

@require_torch
class 			lowerCamelCase_			(snake_case__							,				snake_case__							,				unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Any      			=		(
          (
              ResNetModel,
              ResNetForImageClassification,
              ResNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase:						List[str]      			=		(
          {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
          if is_torch_available()
          else {}
      )

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




      def   _A			( self		:       str ):
             _UpperCAmelCase  :						Any							   =					ResNetModelTester(self )
             _UpperCAmelCase  :						Optional[int]							   =					ConfigTester(self		,      config_class=a_		,      has_text_modality=a_ )




      def   _A			( self		:       str ):
             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   _A			( self		:       Optional[int] ):
             return




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




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




      def   _A			( self		:       Dict ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _UpperCAmelCase  :						List[Any]							   =					model_class(a_ )
                    _UpperCAmelCase  :						List[str]							   =					inspect.signature(model.forward )
                    # 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   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Optional[Any]							   =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a_ )




      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Optional[Any]							   =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_backbone(*a_ )




      def   _A			( self		:       Tuple ):
             _UpperCAmelCase   ,						_UpperCAmelCase  :						Union[str, Any]							   =					self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _UpperCAmelCase  :						Optional[int]							   =					model_class(config=a_ )
                    for name, module in model.named_modules():
                           if isinstance(a_		,      (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   _A			( self		:       List[Any] ):
             def check_hidden_states_output(A		:       str		,      A		:       Any		,      A		:       int ):
                    _UpperCAmelCase  :						List[Any]							   =					model_class(a_ )
                    model.to(a_ )
                    model.eval()

                    with torch.no_grad():
                           _UpperCAmelCase  :						Union[str, Any]							   =					model(**self._prepare_for_class(a_		,      a_ ) )

                    _UpperCAmelCase  :						Any							   =					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

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

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

                           check_hidden_states_output(a_		,      a_		,      a_ )




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




      def   _A			( self		:       str ):
             _UpperCAmelCase  :						Any							   =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a_ )





      @slow
      def   _A			( self		:       Any ):
             for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _UpperCAmelCase  :						str							   =					ResNetModel.from_pretrained(a_ )
                    self.assertIsNotNone(a_ )

def 			UpperCamelCase_							(      )					->					Optional[int]:


       """simple docstring"""







       _UpperCAmelCase  :						List[Any]							   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
       return image

@require_torch
@require_vision
class 			lowerCamelCase_			(unittest.TestCase					):
      '''simple docstring'''

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





      @slow
      def   _A			( self		:       Optional[Any] ):
             _UpperCAmelCase  :						str							   =					ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(a_ )

             _UpperCAmelCase  :						Dict							   =					self.default_image_processor
             _UpperCAmelCase  :						Optional[int]							   =					prepare_img()
             _UpperCAmelCase  :						Optional[int]							   =					image_processor(images=a_		,      return_tensors="pt" ).to(a_ )

             # forward pass
             with torch.no_grad():
                    _UpperCAmelCase  :						Optional[Any]							   =					model(**a_ )

             # verify the logits
             _UpperCAmelCase  :						int							   =					torch.Size((1, 1000) )
             self.assertEqual(outputs.logits.shape		,      a_ )

             _UpperCAmelCase  :						int							   =					torch.tensor([-11.1_069, -9.7_877, -8.3_777] ).to(a_ )

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

@require_torch
class 			lowerCamelCase_			(snake_case__							,				unittest.TestCase					):
      '''simple docstring'''

      __UpperCamelCase:						Union[str, Any]      			=		(ResNetBackbone,) if is_torch_available() else ()
      __UpperCamelCase:						int      			=		False
      __UpperCamelCase:						str      			=		ResNetConfig




      def   _A			( self		:       Union[str, Any] ):
             _UpperCAmelCase  :						Union[str, Any]							   =					ResNetModelTester(self )


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						[
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						[
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

