





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       :






			def __init__(self					,							__magic_name__					,							__magic_name__=3					,							__magic_name__=32					,							__magic_name__=3					,							__magic_name__=10					,							__magic_name__=[8, 16, 32, 64]					,							__magic_name__=[1, 1, 2, 1]					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__="relu"					,							__magic_name__=3					,							__magic_name__=None					,							__magic_name__=["stage2", "stage3", "stage4"]					,							__magic_name__=[2, 3, 4]					,							__magic_name__=1					,							)     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Any				=  parent
							snake_case_						:    Any				=  batch_size
							snake_case_						:    Union[str, Any]				=  image_size
							snake_case_						:    List[str]				=  num_channels
							snake_case_						:    Tuple				=  embeddings_size
							snake_case_						:    Dict				=  hidden_sizes
							snake_case_						:    List[str]				=  depths
							snake_case_						:    Dict				=  is_training
							snake_case_						:    Tuple				=  use_labels
							snake_case_						:    Optional[Any]				=  hidden_act
							snake_case_						:    List[Any]				=  num_labels
							snake_case_						:    int				=  scope
							snake_case_						:    Any				=  len(__magic_name__    )
							snake_case_						:    List[Any]				=  out_features
							snake_case_						:    Optional[Any]				=  out_indices
							snake_case_						:    List[Any]				=  num_groups






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


							'''simple docstring'''



							snake_case_						:    List[str]				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

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

							return config, pixel_values, labels






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''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 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  BitModel(config=__magic_name__    )
							model.to(__magic_name__    )
							model.eval()
							snake_case_						:    Optional[Any]				=  model(__magic_name__    )
							self.parent.assertEqual(
							    result.last_hidden_state.shape					,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,							)






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    str				=  self.num_labels
							snake_case_						:    str				=  BitForImageClassification(__magic_name__    )
							model.to(__magic_name__    )
							model.eval()
							snake_case_						:    str				=  model(__magic_name__					,							labels=__magic_name__    )
							self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.num_labels)    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Any				=  BitBackbone(config=__magic_name__    )
							model.to(__magic_name__    )
							model.eval()
							snake_case_						:    Union[str, Any]				=  model(__magic_name__    )

							# 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_						:    Optional[int]				=  None
							snake_case_						:    Optional[int]				=  BitBackbone(config=__magic_name__    )
							model.to(__magic_name__    )
							model.eval()
							snake_case_						:    List[str]				=  model(__magic_name__    )

							# 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 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



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




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

			lowerCamelCase_     :     Optional[Any]       			=      False
			lowerCamelCase_     :     int       			=      False
			lowerCamelCase_     :     Optional[int]       			=      False
			lowerCamelCase_     :     str       			=      False
			lowerCamelCase_     :     List[Any]       			=      False






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  BitModelTester(self    )
							snake_case_						:    int				=  ConfigTester(self					,							config_class=__magic_name__					,							has_text_modality=__magic_name__    )






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


							'''simple docstring'''



							return






			@unittest.skip(reason='''Bit does not output attentions'''    )
			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							pass






			@unittest.skip(reason='''Bit does not use inputs_embeds'''    )
			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							pass






			@unittest.skip(reason='''Bit does not support input and output embeddings'''    )
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_			,							snake_case_						:    List[Any]				=  self.model_tester.prepare_config_and_inputs_for_common()

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

											snake_case_						:    str				=  ['''pixel_values''']
											self.assertListEqual(arg_names[:1]					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__magic_name__    )






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


							'''simple docstring'''



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

							for model_class in self.all_model_classes:
											snake_case_						:    Dict				=  model_class(config=__magic_name__    )
											for name, module in model.named_modules():
															if isinstance(__magic_name__					,							(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 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							def check_hidden_states_output(__magic_name__					,							__magic_name__					,							__magic_name__    ):
											snake_case_						:    Tuple				=  model_class(__magic_name__    )
											model.to(__magic_name__    )
											model.eval()

											with torch.no_grad():
															snake_case_						:    Dict				=  model(**self._prepare_for_class(__magic_name__					,							__magic_name__    )    )

											snake_case_						:    List[str]				=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											snake_case_						:    Dict				=  self.model_tester.num_stages
											self.assertEqual(len(__magic_name__    )					,							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_			,							snake_case_						:    Tuple				=  self.model_tester.prepare_config_and_inputs_for_common()
							snake_case_						:    Optional[int]				=  ['''preactivation''', '''bottleneck''']
							for model_class in self.all_model_classes:
											for layer_type in layers_type:
															snake_case_						:    Optional[Any]				=  layer_type
															snake_case_						:    List[str]				=  True
															check_hidden_states_output(__magic_name__					,							__magic_name__					,							__magic_name__    )

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

															check_hidden_states_output(__magic_name__					,							__magic_name__					,							__magic_name__    )






			@unittest.skip(reason='''Bit does not use feedforward chunking'''    )
			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__magic_name__    )






			@slow
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_						:    Optional[int]				=  BitModel.from_pretrained(__magic_name__    )
											self.assertIsNotNone(__magic_name__    )






def      lowerCamelCase_						(				)		->						List[Any]:






				"""simple docstring"""


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



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






			@cached_property
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



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






			@slow
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(__magic_name__    )

							snake_case_						:    List[Any]				=  self.default_image_processor
							snake_case_						:    List[str]				=  prepare_img()
							snake_case_						:    Any				=  image_processor(images=__magic_name__					,							return_tensors='''pt'''    ).to(__magic_name__    )

							# forward pass
							with torch.no_grad():
											snake_case_						:    List[Any]				=  model(**__magic_name__    )

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

							snake_case_						:    str				=  torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]    ).to(__magic_name__    )

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




@require_torch
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Dict       			=      (BitBackbone,) if is_torch_available() else ()
			lowerCamelCase_     :     Dict       			=      BitConfig

			lowerCamelCase_     :     Union[str, Any]       			=      False






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BitModelTester(self    )







import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()

	parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

	parser.add_argument(
	    '''--txt2img_unclip''',
	    default='''kakaobrain/karlo-v1-alpha''',
	    type=str,
	    required=False,
	    help='''The pretrained txt2img unclip.''',
	)

	lowerCAmelCase_    =				parser.parse_args()

	lowerCAmelCase_    =				UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

	lowerCAmelCase_    =				CLIPImageProcessor()
	lowerCAmelCase_    =				CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

	lowerCAmelCase_    =				UnCLIPImageVariationPipeline(
	    decoder=txtaimg.decoder,
	    text_encoder=txtaimg.text_encoder,
	    tokenizer=txtaimg.tokenizer,
	    text_proj=txtaimg.text_proj,
	    feature_extractor=feature_extractor,
	    image_encoder=image_encoder,
	    super_res_first=txtaimg.super_res_first,
	    super_res_last=txtaimg.super_res_last,
	    decoder_scheduler=txtaimg.decoder_scheduler,
	    super_res_scheduler=txtaimg.super_res_scheduler,
	)

	imgaimg.save_pretrained(args.dump_path)
