"""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						,      lowerCAmelCase						,      lowerCAmelCase=3						,      lowerCAmelCase=32						,      lowerCAmelCase=3						,      lowerCAmelCase=10						,      lowerCAmelCase=[8, 16, 32, 64]						,      lowerCAmelCase=[1, 1, 2, 1]						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase="relu"						,      lowerCAmelCase=3						,      lowerCAmelCase=None						,      lowerCAmelCase=["stage2", "stage3", "stage4"]						,      lowerCAmelCase=[2, 3, 4]						,      lowerCAmelCase=1						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     image_size
										snake_case        =     num_channels
										snake_case        =     embeddings_size
										snake_case        =     hidden_sizes
										snake_case        =     depths
										snake_case        =     is_training
										snake_case        =     use_labels
										snake_case        =     hidden_act
										snake_case        =     num_labels
										snake_case        =     scope
										snake_case        =     len(__A      )
										snake_case        =     out_features
										snake_case        =     out_indices
										snake_case        =     num_groups

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

										snake_case        =     self.get_config()

										return config, pixel_values, labels

				def 						snake_case	(			self      ):






										"""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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BitModel(config=__A      )
										model.to(__A      )
										model.eval()
										snake_case        =     model(__A      )
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     BitForImageClassification(__A      )
										model.to(__A      )
										model.eval()
										snake_case        =     model(__A						,      labels=__A      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BitBackbone(config=__A      )
										model.to(__A      )
										model.eval()
										snake_case        =     model(__A      )

										# 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        =     None
										snake_case        =     BitBackbone(config=__A      )
										model.to(__A      )
										model.eval()
										snake_case        =     model(__A      )

										# 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 						snake_case	(			self      ):






										"""simple docstring"""

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





@require_torch
class 					lowerCAmelCase_      (     lowerCamelCase__       ,      lowerCamelCase__       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      str     					=  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				_lowerCAmelCase       :      Union[str, Any]     					=  (
				    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_lowerCAmelCase       :      str     					=  False
				_lowerCAmelCase       :      int     					=  False
				_lowerCAmelCase       :      Optional[Any]     					=  False
				_lowerCAmelCase       :      Optional[Any]     					=  False
				_lowerCAmelCase       :      str     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BitModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=__A						,      has_text_modality=__A      )

				def 						snake_case	(			self      ):






										"""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 						snake_case	(			self      ):






										"""simple docstring"""

										return

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






										"""simple docstring"""

										pass

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






										"""simple docstring"""

										pass

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






										"""simple docstring"""

										pass

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case        =     ['''pixel_values''']
																self.assertListEqual(arg_names[:1]						,      __A      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__A      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*__A      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case        =     model_class(config=__A      )
																for name, module in model.named_modules():
																						if isinstance(__A						,      (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 						snake_case	(			self      ):






										"""simple docstring"""

										def check_hidden_states_output(lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):
																snake_case        =     model_class(__A      )
																model.to(__A      )
																model.eval()

																with torch.no_grad():
																						snake_case        =     model(**self._prepare_for_class(__A						,      __A      )      )

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

																snake_case        =     self.model_tester.num_stages
																self.assertEqual(len(__A      )						,      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        =     self.model_tester.prepare_config_and_inputs_for_common()
										snake_case        =     ['''preactivation''', '''bottleneck''']
										for model_class in self.all_model_classes:
																for layer_type in layers_type:
																						snake_case        =     layer_type
																						snake_case        =     True
																						check_hidden_states_output(__A						,      __A						,      __A      )

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

																						check_hidden_states_output(__A						,      __A						,      __A      )

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






										"""simple docstring"""

										pass

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__A      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case        =     BitModel.from_pretrained(__A      )
																self.assertIsNotNone(__A      )





def 				lowerCAmelCase__      (     )   ->			Union[str, Any]:





						"""simple docstring"""






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



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



				"""simple docstring"""

				@cached_property
				def 						snake_case	(			self      ):






										"""simple docstring"""

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

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(__A      )

										snake_case        =     self.default_image_processor
										snake_case        =     prepare_img()
										snake_case        =     image_processor(images=__A						,      return_tensors='pt'      ).to(__A      )

										# forward pass
										with torch.no_grad():
																snake_case        =     model(**__A      )

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

										snake_case        =     torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]      ).to(__A      )

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





@require_torch
class 					lowerCAmelCase_      (     lowerCamelCase__       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  (BitBackbone,) if is_torch_available() else ()
				_lowerCAmelCase       :      str     					=  BitConfig

				_lowerCAmelCase       :      Optional[Any]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BitModelTester(self      )



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecAudioForAudioFrameClassification",
					    "Data2VecAudioForCTC",
					    "Data2VecAudioForSequenceClassification",
					    "Data2VecAudioForXVector",
					    "Data2VecAudioModel",
					    "Data2VecAudioPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecTextForCausalLM",
					    "Data2VecTextForMaskedLM",
					    "Data2VecTextForMultipleChoice",
					    "Data2VecTextForQuestionAnswering",
					    "Data2VecTextForSequenceClassification",
					    "Data2VecTextForTokenClassification",
					    "Data2VecTextModel",
					    "Data2VecTextPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecVisionForImageClassification",
					    "Data2VecVisionForMaskedImageModeling",
					    "Data2VecVisionForSemanticSegmentation",
					    "Data2VecVisionModel",
					    "Data2VecVisionPreTrainedModel",
					]

if is_tf_available():
					SCREAMING_SNAKE_CASE__   =					[
					    "TFData2VecVisionForImageClassification",
					    "TFData2VecVisionForSemanticSegmentation",
					    "TFData2VecVisionModel",
					    "TFData2VecVisionPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


