



"""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 			snake_case_						:






							def __init__(   self			,     a_			,     a_=3			,     a_=3_2			,     a_=3			,     a_=1_0			,     a_=[8, 1_6, 3_2, 6_4]			,     a_=[1, 1, 2, 1]			,     a_=True			,     a_=True			,     a_="relu"			,     a_=3			,     a_=None			,     a_=["stage2", "stage3", "stage4"]			,     a_=[2, 3, 4]			,     a_=1			,     ):
									a_     :      List[Any] 						=     parent
									a_     :      Optional[int] 						=     batch_size
									a_     :      Union[str, Any] 						=     image_size
									a_     :      Dict 						=     num_channels
									a_     :      Any 						=     embeddings_size
									a_     :      Any 						=     hidden_sizes
									a_     :      int 						=     depths
									a_     :      List[str] 						=     is_training
									a_     :      Any 						=     use_labels
									a_     :      Union[str, Any] 						=     hidden_act
									a_     :      Union[str, Any] 						=     num_labels
									a_     :      Union[str, Any] 						=     scope
									a_     :      Union[str, Any] 						=     len(a_      )
									a_     :      Union[str, Any] 						=     out_features
									a_     :      Any 						=     out_indices
									a_     :      Any 						=     num_groups






							def snake_case_  (   self      ):
									a_     :      int 						=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

									a_     :      List[str] 						=     self.get_config()

									return config, pixel_values, labels






							def snake_case_  (   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 snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      List[Any] 						=     BitModel(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      List[str] 						=     model(a_      )
									self.parent.assertEqual(
									    result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)			,     )






							def snake_case_  (   self			,     a_			,     a_			,     a_      ):
									a_     :      Tuple 						=     self.num_labels
									a_     :      Any 						=     BitForImageClassification(a_      )
									model.to(a_      )
									model.eval()
									a_     :      Optional[Any] 						=     model(a_			,     labels=a_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






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




@require_torch
class 			snake_case_						(						a_     ,a_     ,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 snake_case_  (   self      ):
									a_     :      Optional[int] 						=     BitModelTester(self      )
									a_     :      Tuple 						=     ConfigTester(self			,     config_class=a_			,     has_text_modality=a_      )






							def snake_case_  (   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 snake_case_  (   self      ):
									return






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






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






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






							def snake_case_  (   self      ):
									a_						,				a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs_for_common()

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

											a_     :      int 						=     ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,     a_      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a_      )






							def snake_case_  (   self      ):
									a_     :      str 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*a_      )






							def snake_case_  (   self      ):
									a_						,				a_     :      str 						=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      List[str] 						=     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      ):
									def check_hidden_states_output(a_			,     a_			,     a_      ):
											a_     :      Optional[int] 						=     model_class(a_      )
											model.to(a_      )
											model.eval()

											with torch.no_grad():
													a_     :      Optional[Any] 						=     model(**self._prepare_for_class(a_			,     a_      )      )

											a_     :      List[Any] 						=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											a_     :      str 						=     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]			,     )

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

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

													check_hidden_states_output(a_			,     a_			,     a_      )






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






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*a_      )






							@slow
							def snake_case_  (   self      ):
									for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_     :      str 						=     BitModel.from_pretrained(a_      )
											self.assertIsNotNone(a_      )



def        lowerCAmelCase_						(							)			->      Optional[Any]:
		a_     :      Optional[Any] 						=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image




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






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






							@slow
							def snake_case_  (   self      ):
									a_     :      Tuple 						=     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(a_      )

									a_     :      Optional[Any] 						=     self.default_image_processor
									a_     :      Optional[Any] 						=     prepare_img()
									a_     :      Optional[int] 						=     image_processor(images=a_			,     return_tensors="pt"      ).to(a_      )

									# forward pass
									with torch.no_grad():
											a_     :      Tuple 						=     model(**a_      )

									# verify the logits
									a_     :      str 						=     torch.Size((1, 1_0_0_0)      )
									self.assertEqual(outputs.logits.shape			,     a_      )

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

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




@require_torch
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(BitBackbone,) if is_torch_available() else ()
							__lowerCAmelCase    					=					BitConfig

							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     BitModelTester(self      )




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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

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