
import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=32		,	lowerCamelCase=3		,	lowerCamelCase=4		,	lowerCamelCase=[10, 20, 30, 40]		,	lowerCamelCase=[2, 2, 3, 2]		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=37		,	lowerCamelCase="gelu"		,	lowerCamelCase=10		,	lowerCamelCase=0.0_2		,	lowerCamelCase=["stage2", "stage3", "stage4"]		,	lowerCamelCase=[2, 3, 4]		,	lowerCamelCase=None		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				image_size
          snake_case__						=				num_channels
          snake_case__						=				num_stages
          snake_case__						=				hidden_sizes
          snake_case__						=				depths
          snake_case__						=				is_training
          snake_case__						=				use_labels
          snake_case__						=				intermediate_size
          snake_case__						=				hidden_act
          snake_case__						=				num_labels
          snake_case__						=				initializer_range
          snake_case__						=				out_features
          snake_case__						=				out_indices
          snake_case__						=				scope
      def A_			(			self ):
          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 A_			(			self ):
          return ConvNextVaConfig(
              num_channels=self.num_channels		,	hidden_sizes=self.hidden_sizes		,	depths=self.depths		,	num_stages=self.num_stages		,	hidden_act=self.hidden_act		,	is_decoder=lowerCamelCase		,	initializer_range=self.initializer_range		,	out_features=self.out_features		,	out_indices=self.out_indices		,	num_labels=self.num_labels		,	)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				ConvNextVaModel(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase )
          # 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		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				ConvNextVaForImageClassification(lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase		,	labels=lowerCamelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels) )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				ConvNextVaBackbone(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase )

          # verify hidden states
          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__						=				ConvNextVaBackbone(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase )

          # 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 ):
          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



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						,					snake_case__						,					snake_case__						=				config_and_inputs
          snake_case__						=				{"pixel_values": pixel_values, "labels": labels}
          return config, inputs_dict





@require_torch
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		List[str]    	=     (
          (
              ConvNextVaModel,
              ConvNextVaForImageClassification,
              ConvNextVaBackbone,
          )
          if is_torch_available()
          else ()
      )
      _A						:		List[str]    	=     (
          {'feature-extraction': ConvNextVaModel, 'image-classification': ConvNextVaForImageClassification}
          if is_torch_available()
          else {}
      )

      _A						:		int    	=     False
      _A						:		List[Any]    	=     False
      _A						:		Union[str, Any]    	=     False
      _A						:		Union[str, Any]    	=     False
      _A						:		List[str]    	=     False
      def A_			(			self ):
          snake_case__						=				ConvNextVaModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase		,	has_text_modality=lowerCamelCase		,	hidden_size=37 )
      def A_			(			self ):
          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 ):
          return
      @unittest.skip(reason="ConvNextV2 does not use inputs_embeds" )
      def A_			(			self ):
          pass
      @unittest.skip(reason="ConvNextV2 does not support input and output embeddings" )
      def A_			(			self ):
          pass
      @unittest.skip(reason="ConvNextV2 does not use feedforward chunking" )
      def A_			(			self ):
          pass
      def A_			(			self ):
          if not self.model_tester.is_training:
              return

          for model_class in self.all_model_classes:
              snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_with_labels()
              snake_case__						=				True

              if model_class.__name__ in [
                  *get_values(lowerCamelCase ),
                  *get_values(lowerCamelCase ),
              ]:
                  continue

              snake_case__						=				model_class(lowerCamelCase )
              model.to(lowerCamelCase )
              model.train()
              snake_case__						=				self._prepare_for_class(lowerCamelCase		,	lowerCamelCase		,	return_labels=lowerCamelCase )
              snake_case__						=				model(**lowerCamelCase ).loss
              loss.backward()
      def A_			(			self ):
          if not self.model_tester.is_training:
              return

          for model_class in self.all_model_classes:
              snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_with_labels()
              snake_case__						=				False
              snake_case__						=				True

              if (
                  model_class.__name__
                  in [*get_values(lowerCamelCase ), *get_values(lowerCamelCase )]
                  or not model_class.supports_gradient_checkpointing
              ):
                  continue

              snake_case__						=				model_class(lowerCamelCase )
              model.to(lowerCamelCase )
              model.gradient_checkpointing_enable()
              model.train()
              snake_case__						=				self._prepare_for_class(lowerCamelCase		,	lowerCamelCase		,	return_labels=lowerCamelCase )
              snake_case__						=				model(**lowerCamelCase ).loss
              loss.backward()
      def A_			(			self ):
          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(lowerCamelCase )
              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]		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase )
      def A_			(			self ):
          def check_hidden_states_output(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
              snake_case__						=				model_class(lowerCamelCase )
              model.to(lowerCamelCase )
              model.eval()

              with torch.no_grad():
                  snake_case__						=				model(**self._prepare_for_class(lowerCamelCase		,	lowerCamelCase ) )

              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(lowerCamelCase )		,	expected_num_stages + 1 )

              # ConvNextV2'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()

          for model_class in self.all_model_classes:
              snake_case__						=				True
              check_hidden_states_output(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )

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

              check_hidden_states_output(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )



      @slow
      def A_			(			self ):
          for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__						=				ConvNextVaModel.from_pretrained(lowerCamelCase )
              self.assertIsNotNone(lowerCamelCase )





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
    return image





@require_torch
@require_vision
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      @cached_property
      def A_			(			self ):
          return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224" ) if is_vision_available() else None



      @slow
      def A_			(			self ):
          snake_case__						=				ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224" ).to(lowerCamelCase )

          snake_case__						=				self.default_image_processor
          snake_case__						=				prepare_img()
          snake_case__						=				preprocessor(images=lowerCamelCase		,	return_tensors="pt" ).to(lowerCamelCase )

          # forward pass
          with torch.no_grad():
              snake_case__						=				model(**lowerCamelCase )

          # verify the logits
          snake_case__						=				torch.Size((1, 10_00) )
          self.assertEqual(outputs.logits.shape		,	lowerCamelCase )

          snake_case__						=				torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6] ).to(lowerCamelCase )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]		,	lowerCamelCase		,	atol=1e-4 ) )





from typing import TYPE_CHECKING

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


__magic_name__            =  {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPTokenizerFast''']

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPFeatureExtractor''']
     __magic_name__            =  ['''CLIPImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''CLIPModel''',
         '''CLIPPreTrainedModel''',
         '''CLIPTextModel''',
         '''CLIPTextModelWithProjection''',
         '''CLIPVisionModel''',
         '''CLIPVisionModelWithProjection''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFCLIPModel''',
         '''TFCLIPPreTrainedModel''',
         '''TFCLIPTextModel''',
         '''TFCLIPVisionModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''FlaxCLIPModel''',
         '''FlaxCLIPPreTrainedModel''',
         '''FlaxCLIPTextModel''',
         '''FlaxCLIPTextPreTrainedModel''',
         '''FlaxCLIPVisionModel''',
         '''FlaxCLIPVisionPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_clip import (
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPConfig,
         CLIPOnnxConfig,
         CLIPTextConfig,
         CLIPVisionConfig,
     )
     from .processing_clip import CLIPProcessor
     from .tokenization_clip import CLIPTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_clip_fast import CLIPTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clip import CLIPFeatureExtractor
          from .image_processing_clip import CLIPImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clip import (
              CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPModel,
              CLIPPreTrainedModel,
              CLIPTextModel,
              CLIPTextModelWithProjection,
              CLIPVisionModel,
              CLIPVisionModelWithProjection,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_clip import (
              TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCLIPModel,
              TFCLIPPreTrainedModel,
              TFCLIPTextModel,
              TFCLIPVisionModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_clip import (
              FlaxCLIPModel,
              FlaxCLIPPreTrainedModel,
              FlaxCLIPTextModel,
              FlaxCLIPTextPreTrainedModel,
              FlaxCLIPVisionModel,
              FlaxCLIPVisionPreTrainedModel,
          )


else:
     import sys

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



