

import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
  from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase=13				,  lowerCAmelCase=64				,  lowerCAmelCase=2				,  lowerCAmelCase=3				,  lowerCAmelCase=True				,  lowerCAmelCase=True				,  lowerCAmelCase=32				,  lowerCAmelCase=5				,  lowerCAmelCase=4				,  lowerCAmelCase=37				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.1				,  lowerCAmelCase=10				,  lowerCAmelCase=0.02				,  lowerCAmelCase=[1, 16, 4, 4]				,  lowerCAmelCase=None				,  )    ->					int:
        '''simple docstring'''






        _lowercase      			=parent
        _lowercase      			=batch_size
        _lowercase      			=image_size
        _lowercase      			=patch_size
        _lowercase      			=num_channels
        _lowercase      			=is_training
        _lowercase      			=use_labels
        _lowercase      			=hidden_size
        _lowercase      			=num_hidden_layers
        _lowercase      			=num_attention_heads
        _lowercase      			=intermediate_size
        _lowercase      			=hidden_act
        _lowercase      			=hidden_dropout_prob
        _lowercase      			=attention_probs_dropout_prob
        _lowercase      			=type_sequence_label_size
        _lowercase      			=initializer_range
        _lowercase      			=scope
        _lowercase      			=backbone_featmap_shape

        # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        # the number of patches is based on the feature map of the backbone, which by default uses an output stride
        # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
        _lowercase      			=(self.image_size // 32) ** 2
        _lowercase      			=num_patches + 1




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        _lowercase      			=None
        if self.use_labels:
              _lowercase      			=ids_tensor([self.batch_size]				,  self.type_sequence_label_size   )

        _lowercase      			=self.get_config()

        return config, pixel_values, labels




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			={
            'global_padding': 'same',
            'layer_type': 'bottleneck',
            'depths': [3, 4, 9],
            'out_features': ['stage1', 'stage2', 'stage3'],
            'embedding_dynamic_padding': True,
            'hidden_sizes': [4, 8, 16, 32],
            'num_groups': 2,
        }

        return ViTHybridConfig(
            image_size=self.image_size				,  patch_size=self.patch_size				,  num_channels=self.num_channels				,  hidden_size=self.hidden_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				,  is_decoder=lowerCAmelCase				,  initializer_range=self.initializer_range				,  backbone_featmap_shape=self.backbone_featmap_shape				,  backbone_config=lowerCAmelCase				,  )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        _lowercase      			=ViTHybridModel(config=lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        _lowercase      			=model(lowerCAmelCase   )
        self.parent.assertEqual(result.last_hidden_state.shape				,  (self.batch_size, self.seq_length, self.hidden_size)   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.type_sequence_label_size
        _lowercase      			=ViTHybridForImageClassification(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        _lowercase      			=model(lowerCAmelCase				,  labels=lowerCAmelCase   )
        self.parent.assertEqual(result.logits.shape				,  (self.batch_size, self.type_sequence_label_size)   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.prepare_config_and_inputs()
        _lowercase			,			_lowercase			,			_lowercase      			=config_and_inputs
        _lowercase      			={'pixel_values': pixel_values}
        return config, inputs_dict





@require_torch
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     unittest.TestCase				):
  _a		     =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
  _a		     =    (
      {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
      if is_torch_available()
      else {}
  )
  _a		     =    False
  _a		     =    False
  _a		     =    False




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=ViTHybridModelTester(self   )
        _lowercase      			=ConfigTester(self				,  config_class=lowerCAmelCase				,  has_text_modality=lowerCAmelCase				,  hidden_size=37   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  @unittest.skip(reason='ViT does not use inputs_embeds'   )
  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        pass




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )
              self.assertIsInstance(model.get_input_embeddings()				,  (nn.Module)   )
              _lowercase      			=model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCAmelCase				,  nn.Linear   )   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowercase      			=['pixel_values']
              self.assertListEqual(arg_names[:1]				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self.model_tester.prepare_config_and_inputs_for_common()

        _lowercase      			=_config_zero_init(lowerCAmelCase   )
        for model_class in self.all_model_classes:
              _lowercase      			=model_class(config=lowerCAmelCase   )
              # Skip the check for the backbone
              for name, module in model.named_modules():
                    if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                          _lowercase      			=[F'''{name}.{key}''' for key in module.state_dict().keys()]
                          break

              for name, param in model.named_parameters():
                    if param.requires_grad:
                          if name in backbone_params:
                                continue
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()				,  [0.0, 1.0]				,  msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,  )




  @slow
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowercase      			=ViTHybridModel.from_pretrained(lowerCAmelCase   )
              self.assertIsNotNone(lowerCAmelCase   )
def 	a (     )       -> List[Any]:





      """simple docstring"""



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







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




  @cached_property
  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        return (
            ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
            if is_vision_available()
            else None
        )




  @slow
  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
            lowerCAmelCase   )

        _lowercase      			=self.default_image_processor
        _lowercase      			=prepare_img()
        _lowercase      			=image_processor(images=lowerCAmelCase				,  return_tensors='pt'   ).to(lowerCAmelCase   )

        # forward pass
        with torch.no_grad():
              _lowercase      			=model(**lowerCAmelCase   )

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

        _lowercase      			=torch.tensor([-1.9090, -0.4993, -0.2389]   ).to(lowerCAmelCase   )

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




  @slow
  @require_accelerate
  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase      			=ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'   )
        _lowercase      			=ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'				,  device_map='auto'   )

        _lowercase      			=prepare_img()

        _lowercase      			=image_processor(images=lowerCAmelCase				,  return_tensors='pt'   )
        _lowercase      			=model(**lowerCAmelCase   )
        _lowercase      			=outputs.logits
        # model predicts one of the 1000 ImageNet classes
        _lowercase      			=logits.argmax(-1   ).item()

        self.assertTrue(model.config.idalabel[predicted_class_idx]				,  'tabby, tabby cat'   )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_   							=  {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TimesformerModel',
      'TimesformerForVideoClassification',
      'TimesformerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

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


