


"""simple docstring"""


import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

	from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image



class 			lowerCamelCase__						:







						def __init__(							self   :    List[str]			,			_lowercase   :    Dict			,			_lowercase   :    Optional[Any]=3			,			_lowercase   :    str=32			,			_lowercase   :    Dict=3			,			_lowercase   :    Optional[int]=10			,			_lowercase   :    Any=[8, 16, 32, 64]			,			_lowercase   :    str=[1, 1, 2, 1]			,			_lowercase   :    Union[str, Any]=True			,			_lowercase   :    Optional[int]=True			,			_lowercase   :    str="relu"			,			_lowercase   :    Any=3			,			_lowercase   :    List[str]=None			,			_lowercase   :    List[Any]=["stage2", "stage3", "stage4"]			,			_lowercase   :    int=[2, 3, 4]			,			_lowercase   :    int=1			,			):
								A					       = parent
								A					       = batch_size
								A					       = image_size
								A					       = num_channels
								A					       = embeddings_size
								A					       = hidden_sizes
								A					       = depths
								A					       = is_training
								A					       = use_labels
								A					       = hidden_act
								A					       = num_labels
								A					       = scope
								A					       = len(_lowercase		)
								A					       = out_features
								A					       = out_indices
								A					       = num_groups







						def __a						(							self   :    Tuple		):
								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.num_labels		)

								A					       = self.get_config()

								return config, pixel_values, labels







						def __a						(							self   :    Optional[Any]		):
								return BitConfig(
								    num_channels=self.num_channels			,			embeddings_size=self.embeddings_size			,			hidden_sizes=self.hidden_sizes			,			depths=self.depths			,			hidden_act=self.hidden_act			,			num_labels=self.num_labels			,			out_features=self.out_features			,			out_indices=self.out_indices			,			num_groups=self.num_groups			,			)







						def __a						(							self   :    str			,			_lowercase   :    Any			,			_lowercase   :    Optional[int]			,			_lowercase   :    int		):
								A					       = BitModel(config=_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase		)
								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   :    Any			,			_lowercase   :    str			,			_lowercase   :    Tuple			,			_lowercase   :    Optional[Any]		):
								A					       = self.num_labels
								A					       = BitForImageClassification(_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase			,			labels=_lowercase		)
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)		)







						def __a						(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Dict			,			_lowercase   :    Any		):
								A					       = BitBackbone(config=_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase		)

								# verify feature maps
								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
								A					       = None
								A					       = BitBackbone(config=_lowercase		)
								model.to(_lowercase		)
								model.eval()
								A					       = model(_lowercase		)

								# 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   :    Union[str, 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 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						lowerCAmelCase       =     (
						    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase       =     False
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False







						def __a						(							self   :    Dict		):
								A					       = BitModelTester(self		)
								A					       = ConfigTester(self			,			config_class=_lowercase			,			has_text_modality=_lowercase		)







						def __a						(							self   :    List[str]		):
								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   :    Tuple		):
								return







						@unittest.skip(reason='Bit does not output attentions'		)
						def __a						(							self   :    Tuple		):
								pass







						@unittest.skip(reason='Bit does not use inputs_embeds'		)
						def __a						(							self   :    int		):
								pass







						@unittest.skip(reason='Bit does not support input and output embeddings'		)
						def __a						(							self   :    Dict		):
								pass







						def __a						(							self   :    str		):
								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										A					       = model_class(_lowercase		)
										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]			,			_lowercase		)







						def __a						(							self   :    Tuple		):
								A					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase		)







						def __a						(							self   :    List[Any]		):
								A					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*_lowercase		)







						def __a						(							self   :    Dict		):
								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										A					       = model_class(config=_lowercase		)
										for name, module in model.named_modules():
												if isinstance(_lowercase			,			(nn.BatchNormad, nn.GroupNorm)		):
														self.assertTrue(
														    torch.all(module.weight == 1		)			,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'			,			)
														self.assertTrue(
														    torch.all(module.bias == 0		)			,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'			,			)







						def __a						(							self   :    int		):
								def check_hidden_states_output(_lowercase   :    Union[str, Any]			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[str]		):
										A					       = model_class(_lowercase		)
										model.to(_lowercase		)
										model.eval()

										with torch.no_grad():
												A					       = model(**self._prepare_for_class(_lowercase			,			_lowercase		)		)

										A					       = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										A					       = self.model_tester.num_stages
										self.assertEqual(len(_lowercase		)			,			expected_num_stages + 1		)

										# Bit'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]			,			)

								A       , A					       = self.model_tester.prepare_config_and_inputs_for_common()
								A					       = ['preactivation', 'bottleneck']
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												A					       = layer_type
												A					       = True
												check_hidden_states_output(_lowercase			,			_lowercase			,			_lowercase		)

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

												check_hidden_states_output(_lowercase			,			_lowercase			,			_lowercase		)







						@unittest.skip(reason='Bit does not use feedforward chunking'		)
						def __a						(							self   :    List[str]		):
								pass







						def __a						(							self   :    Dict		):
								A					       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase		)







						@slow
						def __a						(							self   :    List[str]		):
								for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										A					       = BitModel.from_pretrained(_lowercase		)
										self.assertIsNotNone(_lowercase		)







def  __snake_case       (   )      ->		Optional[int]:







		"""simple docstring"""





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



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







						@cached_property
						def __a						(							self   :    Dict		):
								return (
								    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		) if is_vision_available() else None
								)







						@slow
						def __a						(							self   :    Union[str, Any]		):
								A					       = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_lowercase		)

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

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

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

								A					       = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]		).to(_lowercase		)

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



@require_torch
class 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     (BitBackbone,) if is_torch_available() else ()
						lowerCAmelCase       =     BitConfig

						lowerCAmelCase       =     False







						def __a						(							self   :    List[str]		):
								A					       = BitModelTester(self		)









"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


UpperCamelCase						:				Optional[Any]             =      logging.get_logger(__name__)



@dataclass
class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]







						def __init__(							self   :    str			,			**_lowercase   :    Union[str, Any]		):
								for deprecated_arg in self.deprecated_args:
										if deprecated_arg in kwargs:
												A					       = deprecated_arg[3:]
												A					       = not kwargs.pop(_lowercase		)
												logger.warning(
												    f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
												    f' {positive_arg}={kwargs[positive_arg]}'		)
								A					       = kwargs.pop('tpu_name'			,			self.tpu_name		)
								A					       = kwargs.pop('device_idx'			,			self.device_idx		)
								A					       = kwargs.pop('eager_mode'			,			self.eager_mode		)
								A					       = kwargs.pop('use_xla'			,			self.use_xla		)
								super().__init__(**_lowercase		)

						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Name of TPU"""}							,    )
						lowerCAmelCase       =     field(
						    default=0							,    metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}							,    )
						lowerCAmelCase       =     field(default=UpperCAmelCase_							,    metadata={"""help""": """Benchmark models in eager model."""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={
						        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
						    }							,    )







						@cached_property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								A					       = None
								if self.tpu:
										try:
												if self.tpu_name:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
												else:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver()
										except ValueError:
												A					       = None
								return tpu







						@cached_property
						def __a						(							self   :    Dict		):
								requires_backends(self			,			['tf']		)
								if self.is_tpu:
										tf.config.experimental_connect_to_cluster(self._setup_tpu		)
										tf.tpu.experimental.initialize_tpu_system(self._setup_tpu		)

										A					       = tf.distribute.TPUStrategy(self._setup_tpu		)
								else:
										# currently no multi gpu is allowed
										if self.is_gpu:
												# TODO: Currently only single GPU is supported
												tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,			'GPU'		)
												A					       = tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'		)
										else:
												tf.config.set_visible_devices([]			,			'GPU'		)  # disable GPU
												A					       = tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'		)

								return strategy







						@property
						def __a						(							self   :    List[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_tpu is not None







						@property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_strategy







						@property
						def __a						(							self   :    str		):
								requires_backends(self			,			['tf']		)
								return tf.config.list_physical_devices('GPU'		)







						@property
						def __a						(							self   :    Any		):
								requires_backends(self			,			['tf']		)
								if self.cuda:
										return len(self.gpu_list		)
								return 0







						@property
						def __a						(							self   :    Dict		):
								return self.n_gpu > 0





