

"""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 			_UpperCAmelCase					:



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



	def     a   (   self			:    str		):
		__UpperCAmelCase							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

		__UpperCAmelCase							=      None
		if self.use_labels:
			__UpperCAmelCase							=      ids_tensor([self.batch_size]       ,  self.num_labels		)

		__UpperCAmelCase							=      self.get_config()

		return config, pixel_values, labels



	def     a   (   self			:    Union[str, 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			:    List[Any]       ,  _lowercase			:    Optional[Any]       ,  _lowercase			:    Optional[Any]       ,  _lowercase			:    Tuple		):
		__UpperCAmelCase							=      BitModel(config=UpperCAmelCase__		)
		model.to(UpperCAmelCase__		)
		model.eval()
		__UpperCAmelCase							=      model(UpperCAmelCase__		)
		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			:    Optional[Any]       ,  _lowercase			:    Dict       ,  _lowercase			:    Dict       ,  _lowercase			:    List[Any]		):
		__UpperCAmelCase							=      self.num_labels
		__UpperCAmelCase							=      BitForImageClassification(UpperCAmelCase__		)
		model.to(UpperCAmelCase__		)
		model.eval()
		__UpperCAmelCase							=      model(UpperCAmelCase__       ,  labels=UpperCAmelCase__		)
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)		)



	def     a   (   self			:    Any       ,  _lowercase			:    Optional[Any]       ,  _lowercase			:    Dict       ,  _lowercase			:    str		):
		__UpperCAmelCase							=      BitBackbone(config=UpperCAmelCase__		)
		model.to(UpperCAmelCase__		)
		model.eval()
		__UpperCAmelCase							=      model(UpperCAmelCase__		)

		# 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
		__UpperCAmelCase							=      None
		__UpperCAmelCase							=      BitBackbone(config=UpperCAmelCase__		)
		model.to(UpperCAmelCase__		)
		model.eval()
		__UpperCAmelCase							=      model(UpperCAmelCase__		)

		# 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			:    Optional[Any]		):
		__UpperCAmelCase							=      self.prepare_config_and_inputs()
		__UpperCAmelCase						,				__UpperCAmelCase						,				__UpperCAmelCase							=      config_and_inputs
		__UpperCAmelCase							=      {'''pixel_values''': pixel_values}
		return config, inputs_dict






@require_torch
class 			_UpperCAmelCase					(				__lowercase						,  __lowercase						,  unittest.TestCase    ):
	a__						:      List[str]						       = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	a__						:      Union[str, Any]						       = (
	    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	a__						:      str						       = False
	a__						:      List[Any]						       = False
	a__						:      Dict						       = False
	a__						:      List[str]						       = False
	a__						:      List[Any]						       = False



	def     a   (   self			:    Optional[Any]		):
		__UpperCAmelCase							=      BitModelTester(self		)
		__UpperCAmelCase							=      ConfigTester(self       ,  config_class=UpperCAmelCase__       ,  has_text_modality=UpperCAmelCase__		)



	def     a   (   self			:    Dict		):
		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			:    Optional[Any]		):
		return



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



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



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



	def     a   (   self			:    Union[str, Any]		):
		__UpperCAmelCase						,				__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()

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

			__UpperCAmelCase							=      ['''pixel_values''']
			self.assertListEqual(arg_names[:1]       ,  UpperCAmelCase__		)



	def     a   (   self			:    str		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCAmelCase__		)



	def     a   (   self			:    List[Any]		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*UpperCAmelCase__		)



	def     a   (   self			:    Tuple		):
		__UpperCAmelCase						,				__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase							=      model_class(config=UpperCAmelCase__		)
			for name, module in model.named_modules():
				if isinstance(UpperCAmelCase__       ,  (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			:    Optional[Any]       ,  _lowercase			:    Tuple		):
			__UpperCAmelCase							=      model_class(UpperCAmelCase__		)
			model.to(UpperCAmelCase__		)
			model.eval()

			with torch.no_grad():
				__UpperCAmelCase							=      model(**self._prepare_for_class(UpperCAmelCase__       ,  UpperCAmelCase__		)		)

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

			__UpperCAmelCase							=      self.model_tester.num_stages
			self.assertEqual(len(UpperCAmelCase__		)       ,  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]       ,  )

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

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

				check_hidden_states_output(UpperCAmelCase__       ,  UpperCAmelCase__       ,  UpperCAmelCase__		)



	@unittest.skip(reason='''Bit does not use feedforward chunking'''		)
	def     a   (   self			:    Tuple		):
		pass



	def     a   (   self			:    str		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)



	@slow
	def     a   (   self			:    Optional[Any]		):
		for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__UpperCAmelCase							=      BitModel.from_pretrained(UpperCAmelCase__		)
			self.assertIsNotNone(UpperCAmelCase__		)






def 	lowercase__		(    ):
	__UpperCAmelCase							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
	return image
@require_torch
@require_vision
class 			_UpperCAmelCase					(				unittest.TestCase    ):



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



	@slow
	def     a   (   self			:    Optional[Any]		):
		__UpperCAmelCase							=      BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(UpperCAmelCase__		)

		__UpperCAmelCase							=      self.default_image_processor
		__UpperCAmelCase							=      prepare_img()
		__UpperCAmelCase							=      image_processor(images=UpperCAmelCase__       ,  return_tensors='''pt'''		).to(UpperCAmelCase__		)

		# forward pass
		with torch.no_grad():
			__UpperCAmelCase							=      model(**UpperCAmelCase__		)

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

		__UpperCAmelCase							=      torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]		).to(UpperCAmelCase__		)

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






@require_torch
class 			_UpperCAmelCase					(				__lowercase						,  unittest.TestCase    ):
	a__						:      Optional[Any]						       = (BitBackbone,) if is_torch_available() else ()
	a__						:      Optional[int]						       = BitConfig

	a__						:      Optional[Any]						       = False



	def     a   (   self			:    Dict		):
		__UpperCAmelCase							=      BitModelTester(self		)




'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





