

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				:						Tuple    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[int]=1_3    ,				__UpperCAmelCase				:						int=6_4    ,				__UpperCAmelCase				:						Optional[int]=2    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						Union[str, Any]=3_2    ,				__UpperCAmelCase				:						Union[str, Any]=5    ,				__UpperCAmelCase				:						List[Any]=4    ,				__UpperCAmelCase				:						Optional[Any]=3_7    ,				__UpperCAmelCase				:						Any="gelu"    ,				__UpperCAmelCase				:						Tuple=0.1    ,				__UpperCAmelCase				:						str=0.1    ,				__UpperCAmelCase				:						Optional[int]=1_0    ,				__UpperCAmelCase				:						List[Any]=0.02    ,				__UpperCAmelCase				:						List[Any]=[1, 1_6, 4, 4]    ,				__UpperCAmelCase				:						Tuple=None    ,				)   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      scope
						SCREAMING_SNAKE_CASE__								=      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
						SCREAMING_SNAKE_CASE__								=      (self.image_size // 3_2) ** 2
						SCREAMING_SNAKE_CASE__								=      num_patches + 1

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      {
						    """global_padding""": """same""",
						    """layer_type""": """bottleneck""",
						    """depths""": [3, 4, 9],
						    """out_features""": ["""stage1""", """stage2""", """stage3"""],
						    """embedding_dynamic_padding""": True,
						    """hidden_sizes""": [4, 8, 1_6, 3_2],
						    """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=A__    ,				initializer_range=self.initializer_range    ,				backbone_featmap_shape=self.backbone_featmap_shape    ,				backbone_config=A__    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[int]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      ViTHybridModel(config=A__       )
						model.to(A__       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(A__       )
						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      ViTHybridForImageClassification(A__       )
						model.to(A__       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(A__    ,				labels=A__       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		str							=							(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			lowerCamelCase__   :		Tuple							=							(
			    {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		List[Any]							=							False
			lowerCamelCase__   :		Tuple							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      ViTHybridModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=A__    ,				has_text_modality=A__    ,				hidden_size=3_7       )

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

def 	A       (     ):




			'''simple docstring'''


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





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

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

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

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

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

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				A__       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([-1.9_090, -0.4_993, -0.2_389]       ).to(A__       )

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





			@slow
			@require_accelerate
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""       )
						SCREAMING_SNAKE_CASE__								=      ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""    ,				device_map="""auto"""       )

						SCREAMING_SNAKE_CASE__								=      prepare_img()

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

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





"""simple docstring"""




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

A_							:			Tuple											= "2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
						raise ImportWarning(
						    "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
						    "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

A_							:			Tuple											= concatenate_datasets
A_							:			List[str]											= DownloadConfig
A_							:			int											= DownloadManager
A_							:			Optional[Any]											= DownloadMode
A_							:			Optional[int]											= DownloadConfig
A_							:			List[Any]											= DownloadMode
A_							:			Any											= DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


