



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


def A						(       __UpperCamelCase=None ,	__UpperCamelCase=None					)						->      str:
			return field(default_factory=lambda: default ,	metadata=__UpperCamelCase					)






@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    list_field(
					    default=[]							,     metadata={
					        "help": (
					            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
					            " of all available models"
					        )
					    }							,     )

					A__				:		str			       =    list_field(
					    default=[8]							,     metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}      )

					A__				:		List[str]			       =    list_field(
					    default=[8, 32, 1_28, 5_12]							,     metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}							,     )

					A__				:		List[Any]			       =    field(
					    default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,     )
					A__				:		List[str]			       =    field(
					    default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,     )
					A__				:		Any			       =    field(
					    default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}      )
					A__				:		int			       =    field(default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Use FP16 to accelerate inference."}      )
					A__				:		Union[str, Any]			       =    field(default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Benchmark training of model"}      )
					A__				:		List[str]			       =    field(default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Verbose memory tracing"}      )
					A__				:		Tuple			       =    field(
					    default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,     )
					A__				:		Tuple			       =    field(
					    default=SCREAMING_SNAKE_CASE__							,     metadata={
					        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
					    }							,     )
					A__				:		Optional[Any]			       =    field(default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Trace memory line by line"}      )
					A__				:		Optional[Any]			       =    field(default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Save result to a CSV file"}      )
					A__				:		int			       =    field(default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Save all print statements in a log file"}      )
					A__				:		List[str]			       =    field(default=SCREAMING_SNAKE_CASE__							,     metadata={"help": "Whether to print environment information"}      )
					A__				:		str			       =    field(
					    default=SCREAMING_SNAKE_CASE__							,     metadata={
					        "help": (
					            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
					            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
					            " for debugging / testing and on TPU."
					        )
					    }							,     )
					A__				:		Tuple			       =    field(
					    default=f"""inference_time_{round(time()      )}.csv"""							,     metadata={"help": "CSV filename used if saving time results to csv."}							,     )
					A__				:		List[str]			       =    field(
					    default=f"""inference_memory_{round(time()      )}.csv"""							,     metadata={"help": "CSV filename used if saving memory results to csv."}							,     )
					A__				:		List[str]			       =    field(
					    default=f"""train_time_{round(time()      )}.csv"""							,     metadata={"help": "CSV filename used if saving time results to csv for training."}							,     )
					A__				:		List[str]			       =    field(
					    default=f"""train_memory_{round(time()      )}.csv"""							,     metadata={"help": "CSV filename used if saving memory results to csv for training."}							,     )
					A__				:		int			       =    field(
					    default=f"""env_info_{round(time()      )}.csv"""							,     metadata={"help": "CSV filename used if saving environment information."}							,     )
					A__				:		List[Any]			       =    field(
					    default=f"""log_{round(time()      )}.csv"""							,     metadata={"help": "Log filename used if print statements are saved in log."}							,     )
					A__				:		str			       =    field(default=3							,     metadata={"help": "Times an experiment will be run."}      )
					A__				:		Tuple			       =    field(
					    default=SCREAMING_SNAKE_CASE__							,     metadata={
					        "help": (
					            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
					            " model weights."
					        )
					    }							,     )



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								warnings.warn(
								    F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
								    ' are deprecated in general and it is advised to use external Benchmarking libraries '
								    ' to benchmark Transformer models.'				,					_lowercase				,					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								return json.dumps(dataclasses.asdict(self					)				,					indent=2					)



					@property
					def     _a							(     self					:       str					):






								"""simple docstring"""


								if len(self.models					) <= 0:
											raise ValueError(
											    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
											    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'					)
								return self.models



					@property
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								if not self.multi_process:
											return False
								elif self.is_tpu:
											logger.info('Multiprocessing is currently not possible on TPU.'					)
											return False
								else:
											return True









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     :
					"""simple docstring"""






					def __init__(     self					:       List[Any]				,					_snake_case					:       Any				,					_snake_case					:       Optional[int]=13				,					_snake_case					:       Optional[Any]=64				,					_snake_case					:       List[str]=2				,					_snake_case					:       Any=3				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       Dict=True				,					_snake_case					:       int=32				,					_snake_case					:       int=5				,					_snake_case					:       Union[str, Any]=4				,					_snake_case					:       int=37				,					_snake_case					:       Tuple="gelu"				,					_snake_case					:       Optional[int]=0.1				,					_snake_case					:       Dict=0.1				,					_snake_case					:       List[str]=10				,					_snake_case					:       Union[str, Any]=0.02				,					_snake_case					:       Dict=[1, 16, 4, 4]				,					_snake_case					:       Dict=None				,					):






								"""simple docstring"""


								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     _a							(     self					:       Any					):






								"""simple docstring"""


								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     _a							(     self					:       Tuple					):






								"""simple docstring"""


								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=_snake_case				,					initializer_range=self.initializer_range				,					backbone_featmap_shape=self.backbone_featmap_shape				,					backbone_config=_snake_case				,					)



					def     _a							(     self					:       int				,					_snake_case					:       Optional[int]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[str]				,					_snake_case					:       str				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Any					):






								"""simple docstring"""


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



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


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







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Union[str, 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__				:		Any			       =    False
					A__				:		Union[str, Any]			       =    False



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    ViTHybridModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					@unittest.skip(reason='ViT does not use inputs_embeds'					)
					def     _a							(     self					:       int					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

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



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    model_class(_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]				,					_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								A__       =    _config_zero_init(_snake_case					)
								for model_class in self.all_model_classes:
											A__       =    model_class(config=_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     _a							(     self					:       int					):






								"""simple docstring"""


								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ViTHybridModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Union[str, Any]:
			A__       =    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					:       Tuple					):






								"""simple docstring"""


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



					@slow
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
								    _snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    image_processor(images=_snake_case				,					return_tensors='pt'					).to(_snake_case					)

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

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

								A__       =    torch.tensor([-1.9090, -0.4993, -0.2389]					).to(_snake_case					)

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



					@slow
					@require_accelerate
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								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=_snake_case				,					return_tensors='pt'					)
								A__       =    model(**_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'					)




