



"""simple docstring"""







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       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=13						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	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__=10						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=[1, 16, 4, 4]						,	SCREAMING_SNAKE_CASE__=None						,	)      ->	Optional[Any]:
				A__											=					parent
				A__											=					batch_size
				A__											=					image_size
				A__											=					patch_size
				A__											=					num_channels
				A__											=					is_training
				A__											=					use_labels
				A__											=					hidden_size
				A__											=					num_hidden_layers
				A__											=					num_attention_heads
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					hidden_dropout_prob
				A__											=					attention_probs_dropout_prob
				A__											=					type_sequence_label_size
				A__											=					initializer_range
				A__											=					scope
				A__											=					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
				A__											=					(self.image_size // 32) ** 2
				A__											=					num_patches + 1


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

				A__											=					self.get_config()

				return config, pixel_values, labels


			def   snake_case__	(      self		)      ->	int:
				A__											=					{
				    "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=SCREAMING_SNAKE_CASE__						,	initializer_range=self.initializer_range						,	backbone_featmap_shape=self.backbone_featmap_shape						,	backbone_config=SCREAMING_SNAKE_CASE__						,	)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				A__											=					ViTHybridModel(config=SCREAMING_SNAKE_CASE__		)
				model.to(SCREAMING_SNAKE_CASE__		)
				model.eval()
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				A__											=					self.type_sequence_label_size
				A__											=					ViTHybridForImageClassification(SCREAMING_SNAKE_CASE__		)
				model.to(SCREAMING_SNAKE_CASE__		)
				model.eval()
				A__											=					model(SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.type_sequence_label_size)		)







			def   snake_case__	(      self		)      ->	Any:
				A__											=					self.prepare_config_and_inputs()
				A__							,							A__							,							A__											=					config_and_inputs
				A__											=					{"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					List[Any]							  =     (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			A__	:					str							  =     (
			    {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			A__	:					Union[str, Any]							  =     False
			A__	:					List[str]							  =     False
			A__	:					Tuple							  =     False


			def   snake_case__	(      self		)      ->	Any:
				A__											=					ViTHybridModelTester(self		)
				A__											=					ConfigTester(self						,	config_class=SCREAMING_SNAKE_CASE__						,	has_text_modality=SCREAMING_SNAKE_CASE__						,	hidden_size=37		)


			def   snake_case__	(      self		)      ->	List[str]:
				self.config_tester.run_common_tests()


			@unittest.skip(reason="ViT does not use inputs_embeds"		)
			def   snake_case__	(      self		)      ->	Union[str, Any]:
				pass


			def   snake_case__	(      self		)      ->	int:
				A__							,							A__											=					self.model_tester.prepare_config_and_inputs_for_common()

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__							,							A__											=					self.model_tester.prepare_config_and_inputs_for_common()

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

					A__											=					["pixel_values"]
					self.assertListEqual(arg_names[:1]						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Any:
				A__							,							A__											=					self.model_tester.prepare_config_and_inputs_for_common()

				A__											=					_config_zero_init(SCREAMING_SNAKE_CASE__		)
				for model_class in self.all_model_classes:
					A__											=					model_class(config=SCREAMING_SNAKE_CASE__		)
					# Skip the check for the backbone
					for name, module in model.named_modules():
						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
							A__											=					[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   snake_case__	(      self		)      ->	Tuple:
				for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					A__											=					ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)



def       _lowerCamelCase				(							)	-> Any:






	"""simple docstring"""





	A__											=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
	return image






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

			"""simple docstring"""


			@cached_property
			def   snake_case__	(      self		)      ->	str:
				return (
				    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
				    if is_vision_available()
				    else None
				)


			@slow
			def   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
				    SCREAMING_SNAKE_CASE__		)

				A__											=					self.default_image_processor
				A__											=					prepare_img()
				A__											=					image_processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		).to(SCREAMING_SNAKE_CASE__		)

				# forward pass
				with torch.no_grad():
					A__											=					model(**SCREAMING_SNAKE_CASE__		)

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

				A__											=					torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9]		).to(SCREAMING_SNAKE_CASE__		)

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







			@slow
			@require_accelerate
			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
				A__											=					ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"						,	device_map="auto"		)

				A__											=					prepare_img()

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

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









from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


a_      :      Any    =     logging.get_logger(__name__)


a_      :      str    =     TypeVar('DatasetType', Dataset, IterableDataset)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = "first_exhausted"							,  ):
 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError('''Unable to interleave an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."       )
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )
 else:
  return _interleave_iterable_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = 0							,  ):

 if not dsets:
  raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )
 else:
  return _concatenate_iterable_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )





