



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 							_a							:

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

			def 			__snake_case    (self )    ->					str:
						UpperCAmelCase_:						Union[str, Any]					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

						UpperCAmelCase_:						Tuple					=			None
						if self.use_labels:
									UpperCAmelCase_:						Union[str, Any]					=			ids_tensor([self.batch_size],					self.num_labels )

						UpperCAmelCase_:						str					=			self.get_config()

						return config, pixel_values, labels

			def 			__snake_case    (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 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					List[Any]:
						UpperCAmelCase_:						Any					=			BitModel(config=lowercase_ )
						model.to(lowercase_ )
						model.eval()
						UpperCAmelCase_:						Any					=			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 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					List[Any]:
						UpperCAmelCase_:						Any					=			self.num_labels
						UpperCAmelCase_:						Tuple					=			BitForImageClassification(lowercase_ )
						model.to(lowercase_ )
						model.eval()
						UpperCAmelCase_:						Dict					=			model(lowercase_,					labels=lowercase_ )
						self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels) )

			def 			__snake_case    (self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )    ->					str:
						UpperCAmelCase_:						Union[str, Any]					=			BitBackbone(config=lowercase_ )
						model.to(lowercase_ )
						model.eval()
						UpperCAmelCase_:						List[Any]					=			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
						UpperCAmelCase_:						Any					=			None
						UpperCAmelCase_:						Optional[Any]					=			BitBackbone(config=lowercase_ )
						model.to(lowercase_ )
						model.eval()
						UpperCAmelCase_:						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 			__snake_case    (self )    ->					Optional[Any]:
						UpperCAmelCase_:						str					=			self.prepare_config_and_inputs()
						UpperCAmelCase_	,				UpperCAmelCase_	,				UpperCAmelCase_:						int					=			config_and_inputs
						UpperCAmelCase_:						int					=			{"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class 							_a							(					lowerCamelCase__      ,						lowerCamelCase__      ,						unittest.TestCase ):
			A							=     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			A							=     (
			    {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			A							=     False
			A							=     False
			A							=     False
			A							=     False
			A							=     False

			def 			__snake_case    (self )    ->					List[str]:
						UpperCAmelCase_:						Union[str, Any]					=			BitModelTester(self )
						UpperCAmelCase_:						Dict					=			ConfigTester(self,					config_class=lowercase_,					has_text_modality=lowercase_ )

			def 			__snake_case    (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 			__snake_case    (self )    ->					Tuple:
						return

			@unittest.skip(reason="""Bit does not output attentions""" )
			def 			__snake_case    (self )    ->					Tuple:
						pass

			@unittest.skip(reason="""Bit does not use inputs_embeds""" )
			def 			__snake_case    (self )    ->					Optional[int]:
						pass

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

			def 			__snake_case    (self )    ->					Union[str, Any]:
						UpperCAmelCase_	,				UpperCAmelCase_:						int					=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

			def 			__snake_case    (self )    ->					int:
						UpperCAmelCase_:						int					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase_ )

			def 			__snake_case    (self )    ->					str:
						UpperCAmelCase_:						Union[str, Any]					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*lowercase_ )

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_	,				UpperCAmelCase_:						Dict					=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:						Optional[int]					=			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 			__snake_case    (self )    ->					Union[str, Any]:






						def check_hidden_states_output(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ ):
									UpperCAmelCase_:						int					=			model_class(lowercase_ )
									model.to(lowercase_ )
									model.eval()

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

									UpperCAmelCase_:						Union[str, Any]					=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_:						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],					)

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

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

												check_hidden_states_output(lowercase_,					lowercase_,					lowercase_ )

			@unittest.skip(reason="""Bit does not use feedforward chunking""" )
			def 			__snake_case    (self )    ->					Tuple:
						pass

			def 			__snake_case    (self )    ->					Union[str, Any]:
						UpperCAmelCase_:						Dict					=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowercase_ )

			@slow
			def 			__snake_case    (self )    ->					Optional[int]:






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







def       lowerCAmelCase_     ():





			"""simple docstring"""


			UpperCAmelCase_:						Union[str, Any]					=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			return image







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

			@cached_property
			def 			__snake_case    (self )    ->					Tuple:
						return (
						    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
						)

			@slow
			def 			__snake_case    (self )    ->					Tuple:
						UpperCAmelCase_:						str					=			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowercase_ )

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

						# forward pass
						with torch.no_grad():
									UpperCAmelCase_:						Optional[Any]					=			model(**lowercase_ )

						# verify the logits
						UpperCAmelCase_:						Union[str, Any]					=			torch.Size((1, 1000) )
						self.assertEqual(outputs.logits.shape,					lowercase_ )

						UpperCAmelCase_:						Any					=			torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(lowercase_ )

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







@require_torch
class 							_a							(					lowerCamelCase__      ,						unittest.TestCase ):
			A							=     (BitBackbone,) if is_torch_available() else ()
			A							=     BitConfig

			A							=     False

			def 			__snake_case    (self )    ->					str:
						UpperCAmelCase_:						Dict					=			BitModelTester(self )









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	List[Any]    =   IFInpaintingSuperResolutionPipeline
   lowerCamelCase__ :	str    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
   lowerCamelCase__ :	List[Any]    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
   lowerCamelCase__ :	str    =   PipelineTesterMixin.required_optional_params - {'latents'}



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     return self._get_superresolution_dummy_components()



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0       )							->    Tuple:






     """simple docstring"""






     if str(lowercase_       ).startswith('''mps'''       ):
       a__  =torch.manual_seed(lowercase_       )
     else:
       a__  =torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

     a__  =floats_tensor((1, 3, 16, 16),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )

     a__  ={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_image,
         '''mask_image''': mask_image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs



   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available(),    reason='''XFormers attention is only available with CUDA and `xformers` installed''',    )
   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda''',    reason='''float16 requires CUDA'''       )
   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     super().test_save_load_floataa(expected_max_diff=1E-1       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     self._test_save_load_local()



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2,    )
