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        lowercase_   :



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													_snake_case						:					int    =   None
													if self.use_labels:
																			_snake_case						:					List[str]    =   ids_tensor([self.batch_size]				,    self.num_labels    )

													_snake_case						:					Optional[int]    =   self.get_config()

													return config, pixel_values, labels



							def 	UpperCamelCase	(						self    ):
													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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					List[str]    =   BitModel(config=lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					int    =   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 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Any    =   self.num_labels
													_snake_case						:					str    =   BitForImageClassification(lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					List[str]    =   model(lowercase_				,    labels=lowercase_    )
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[Any]    =   BitBackbone(config=lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					Dict    =   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
													_snake_case						:					List[Any]    =   None
													_snake_case						:					List[Any]    =   BitBackbone(config=lowercase_    )
													model.to(lowercase_    )
													model.eval()
													_snake_case						:					Optional[int]    =   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 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.prepare_config_and_inputs()
													_snake_case    ,_snake_case    ,_snake_case						:					Tuple    =   config_and_inputs
													_snake_case						:					List[str]    =   {"pixel_values": pixel_values}
													return config, inputs_dict




@require_torch
class        lowercase_   (					__snake_case						,			__snake_case						,			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 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   BitModelTester(self    )
													_snake_case						:					int    =   ConfigTester(self				,    config_class=lowercase_				,    has_text_modality=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													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 	UpperCamelCase	(						self    ):
													return



							@unittest.skip(reason="Bit does not output attentions"    )
							def 	UpperCamelCase	(						self    ):
													pass



							@unittest.skip(reason="Bit does not use inputs_embeds"    )
							def 	UpperCamelCase	(						self    ):
													pass



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



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					Optional[int]    =   self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case						:					Union[str, Any]    =   model_class(lowercase_    )
																			_snake_case						:					str    =   inspect.signature(model.forward    )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			_snake_case						:					List[str]    =   [*signature.parameters.keys()]

																			_snake_case						:					Optional[int]    =   ["pixel_values"]
																			self.assertListEqual(arg_names[:1]				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case    ,_snake_case						:					List[Any]    =   self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case						:					Any    =   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 	UpperCamelCase	(						self    ):
													def check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    ):
																			_snake_case						:					str    =   model_class(lowercase_    )
																			model.to(lowercase_    )
																			model.eval()

																			with torch.no_grad():
																									_snake_case						:					Optional[int]    =   model(**self._prepare_for_class(lowercase_				,    lowercase_    )    )

																			_snake_case						:					str    =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			_snake_case						:					Union[str, Any]    =   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]				,    )

													_snake_case    ,_snake_case						:					List[str]    =   self.model_tester.prepare_config_and_inputs_for_common()
													_snake_case						:					Tuple    =   ["preactivation", "bottleneck"]
													for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																									_snake_case						:					List[Any]    =   layer_type
																									_snake_case						:					List[str]    =   True
																									check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    )

																									# check that output_hidden_states also work using config
																									del inputs_dict["output_hidden_states"]
																									_snake_case						:					Any    =   True

																									check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_    )



							@unittest.skip(reason="Bit does not use feedforward chunking"    )
							def 	UpperCamelCase	(						self    ):
													pass



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*lowercase_    )



							@slow
							def 	UpperCamelCase	(						self    ):
													for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:					Optional[Any]    =   BitModel.from_pretrained(lowercase_    )
																			self.assertIsNotNone(lowercase_    )




def  snake_case						()	->	Optional[Any]:

						'''simple docstring'''



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



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



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



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(lowercase_    )

													_snake_case						:					Dict    =   self.default_image_processor
													_snake_case						:					List[Any]    =   prepare_img()
													_snake_case						:					Dict    =   image_processor(images=lowercase_				,    return_tensors="pt"    ).to(lowercase_    )

													# forward pass
													with torch.no_grad():
																			_snake_case						:					Union[str, Any]    =   model(**lowercase_    )

													# verify the logits
													_snake_case						:					Tuple    =   torch.Size((1, 1_000)    )
													self.assertEqual(outputs.logits.shape				,    lowercase_    )

													_snake_case						:					Optional[int]    =   torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]    ).to(lowercase_    )

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




@require_torch
class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		(BitBackbone,) if is_torch_available() else ()
							_lowerCamelCase						=		BitConfig

							_lowerCamelCase						=		False



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   BitModelTester(self    )
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


__SCREAMING_SNAKE_CASE						:   str         =			logging.get_logger(__name__)



@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		[
							    'no_inference',
							    'no_cuda',
							    'no_tpu',
							    'no_speed',
							    'no_memory',
							    'no_env_print',
							    'no_multi_process',
							]



							def __init__(						self				,    **lowercase_    ):
													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									_snake_case						:					List[str]    =   deprecated_arg[3:]
																									_snake_case						:					Optional[int]    =   not kwargs.pop(lowercase_    )
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""    )
													_snake_case						:					Tuple    =   kwargs.pop("tpu_name"				,    self.tpu_name    )
													_snake_case						:					Any    =   kwargs.pop("device_idx"				,    self.device_idx    )
													_snake_case						:					List[str]    =   kwargs.pop("eager_mode"				,    self.eager_mode    )
													_snake_case						:					List[str]    =   kwargs.pop("use_xla"				,    self.use_xla    )
													super().__init__(**lowercase_    )

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



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													_snake_case						:					str    =   None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															_snake_case						:					Optional[Any]    =   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
																									else:
																															_snake_case						:					List[str]    =   tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									_snake_case						:					Union[str, Any]    =   None
													return tpu



							@cached_property
							def 	UpperCamelCase	(						self    ):
													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    )

																			_snake_case						:					List[str]    =   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"    )
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""    )
																			else:
																									tf.config.set_visible_devices([]				,    "GPU"    )  # disable GPU
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""    )

													return strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_tpu is not None



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return tf.config.list_physical_devices("GPU"    )



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.cuda:
																			return len(self.gpu_list    )
													return 0



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