






"""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__				:






      '''simple docstring'''

      def __init__(						self     :  List[str]						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Tuple=3						,	lowercase_     :  Union[str, Any]=32						,	lowercase_     :  Optional[Any]=3						,	lowercase_     :  Tuple=10						,	lowercase_     :  Dict=[8, 16, 32, 64]						,	lowercase_     :  List[Any]=[1, 1, 2, 1]						,	lowercase_     :  int=True						,	lowercase_     :  Any=True						,	lowercase_     :  List[str]="relu"						,	lowercase_     :  Dict=3						,	lowercase_     :  List[str]=None						,	lowercase_     :  Dict=["stage2", "stage3", "stage4"]						,	lowercase_     :  Optional[Any]=[2, 3, 4]						,	lowercase_     :  Any=1						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		parent
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		num_channels
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		embeddings_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		hidden_sizes
          SCREAMING_SNAKE_CASE_      :  str    						=		depths
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		is_training
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scope
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		len(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		out_features
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		out_indices
          SCREAMING_SNAKE_CASE_      :  str    						=		num_groups
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          SCREAMING_SNAKE_CASE_      :  str    						=		None
          if self.use_labels:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		ids_tensor([self.batch_size]						,	self.num_labels)

          SCREAMING_SNAKE_CASE_      :  str    						=		self.get_config()

          return config, pixel_values, labels
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          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 _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		BitModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		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 _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		BitForImageClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		BitBackbone(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		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
          SCREAMING_SNAKE_CASE_      :  Dict    						=		None
          SCREAMING_SNAKE_CASE_      :  str    						=		BitBackbone(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Any    						=		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 _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{'''pixel_values''': pixel_values}
          return config, inputs_dict




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      __UpperCamelCase	 =  (
          {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
          if is_torch_available()
          else {}
      )

      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		BitModelTester(self)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		ConfigTester(self						,	config_class=lowercase_						,	has_text_modality=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          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 _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return
      @unittest.skip(reason='''Bit does not output attentions''')
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Bit does not use inputs_embeds''')
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Bit does not support input and output embeddings''')
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE_      :  int    						=		['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		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 _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          def check_hidden_states_output(lowercase_     :  Tuple						,	lowercase_     :  List[Any]						,	lowercase_     :  List[str]):
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.eval()

              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_      :  int    						=		model(**self._prepare_for_class(lowercase_						,	lowercase_))

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              SCREAMING_SNAKE_CASE_      :  Dict    						=		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]						,	)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_      :  Any    						=		['''preactivation''', '''bottleneck''']
          for model_class in self.all_model_classes:
              for layer_type in layers_type:
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		layer_type
                  SCREAMING_SNAKE_CASE_      :  str    						=		True
                  check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True

                  check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)
      @unittest.skip(reason='''Bit does not use feedforward chunking''')
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_)



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		BitModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)



def 					_A			() ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image




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






      '''simple docstring'''

      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
          )



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase_)

          SCREAMING_SNAKE_CASE_      :  str    						=		self.default_image_processor
          SCREAMING_SNAKE_CASE_      :  Dict    						=		prepare_img()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image_processor(images=lowercase_						,	return_tensors='''pt''').to(lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  str    						=		model(**lowercase_)

          # verify the logits
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]).to(lowercase_)

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




@require_torch
class 							lowerCAmelCase__				(			UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  (BitBackbone,) if is_torch_available() else ()
      __UpperCamelCase	 =  BitConfig

      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		BitModelTester(self)




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

			lowerCAmelCase_						=	AutoTokenizer.from_pretrained("bert-base-cased"				)
			lowerCAmelCase_						=	{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			lowerCAmelCase_						=	load_dataset("csv"    ,							data_files=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							padding="max_length"				)
						if "label" in examples:
									lowerCAmelCase_						=	[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCAmelCase_						=	datasets.map(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



