
import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_MAPPING,
							    BeitForImageClassification,
							    BeitForMaskedImageModeling,
							    BeitForSemanticSegmentation,
							    BeitModel,
							)
							from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							import PIL
							from PIL import Image

							from transformers import BeitImageProcessor

class     _UpperCamelCase			:






			'''simple docstring'''

			def __init__(    self	:    Dict  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Union[str, Any]=100  ,		snake_case_	:    Any=13  ,		snake_case_	:    Optional[int]=30  ,		snake_case_	:    str=2  ,		snake_case_	:    int=3  ,		snake_case_	:    Optional[int]=True  ,		snake_case_	:    int=True  ,		snake_case_	:    Optional[Any]=32  ,		snake_case_	:    str=4  ,		snake_case_	:    List[Any]=4  ,		snake_case_	:    Any=37  ,		snake_case_	:    List[str]="gelu"  ,		snake_case_	:    Optional[int]=0.1  ,		snake_case_	:    int=0.1  ,		snake_case_	:    Tuple=10  ,		snake_case_	:    Any=0.02  ,		snake_case_	:    Any=3  ,		snake_case_	:    Tuple=None  ,		snake_case_	:    str=[0, 1, 2, 3]  ,		):
							UpperCamelCase_:							Any		=  parent
							UpperCamelCase_:							str		=  100
							UpperCamelCase_:							Optional[Any]		=  batch_size
							UpperCamelCase_:							int		=  image_size
							UpperCamelCase_:							str		=  patch_size
							UpperCamelCase_:							Dict		=  num_channels
							UpperCamelCase_:							str		=  is_training
							UpperCamelCase_:							List[str]		=  use_labels
							UpperCamelCase_:							Optional[Any]		=  hidden_size
							UpperCamelCase_:							str		=  num_hidden_layers
							UpperCamelCase_:							List[Any]		=  num_attention_heads
							UpperCamelCase_:							str		=  intermediate_size
							UpperCamelCase_:							str		=  hidden_act
							UpperCamelCase_:							Optional[Any]		=  hidden_dropout_prob
							UpperCamelCase_:							int		=  attention_probs_dropout_prob
							UpperCamelCase_:							int		=  type_sequence_label_size
							UpperCamelCase_:							Any		=  initializer_range
							UpperCamelCase_:							int		=  scope
							UpperCamelCase_:							Tuple		=  out_indices
							UpperCamelCase_:							List[Any]		=  num_labels

							# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							UpperCamelCase_:							int		=  (image_size // patch_size) ** 2
							UpperCamelCase_:							str		=  num_patches + 1

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							List[Any]		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Dict		=  None
							if self.use_labels:
											UpperCamelCase_:							List[str]		=  ids_tensor([self.batch_size]  ,		self.type_sequence_label_size )
											UpperCamelCase_:							Optional[Any]		=  ids_tensor([self.batch_size, self.image_size, self.image_size]  ,		self.num_labels )

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

							return config, pixel_values, labels, pixel_labels

			def  lowerCAmelCase__		(    self	:    int ):
							return BeitConfig(
							    vocab_size=self.vocab_size  ,		image_size=self.image_size  ,		patch_size=self.patch_size  ,		num_channels=self.num_channels  ,		hidden_size=self.hidden_size  ,		num_hidden_layers=self.num_hidden_layers  ,		num_attention_heads=self.num_attention_heads  ,		intermediate_size=self.intermediate_size  ,		hidden_act=self.hidden_act  ,		hidden_dropout_prob=self.hidden_dropout_prob  ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,		is_decoder=UpperCamelCase_  ,		initializer_range=self.initializer_range  ,		out_indices=self.out_indices  ,		)

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    int ):
							UpperCamelCase_:							Union[str, Any]		=  BeitModel(config=UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()
							UpperCamelCase_:							Optional[Any]		=  model(UpperCamelCase_ )
							self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size) )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		snake_case_	:    int  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Dict ):
							UpperCamelCase_:							Any		=  BeitForMaskedImageModeling(config=UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()
							UpperCamelCase_:							Optional[int]		=  model(UpperCamelCase_ )
							self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length - 1, self.vocab_size) )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    List[Any]  ,		snake_case_	:    str  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    List[Any] ):
							UpperCamelCase_:							Dict		=  self.type_sequence_label_size
							UpperCamelCase_:							Optional[int]		=  BeitForImageClassification(UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()
							UpperCamelCase_:							str		=  model(UpperCamelCase_  ,		labels=UpperCamelCase_ )
							self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.type_sequence_label_size) )

							# test greyscale images
							UpperCamelCase_:							List[Any]		=  1
							UpperCamelCase_:							int		=  BeitForImageClassification(UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()

							UpperCamelCase_:							Union[str, Any]		=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
							UpperCamelCase_:							Any		=  model(UpperCamelCase_  ,		labels=UpperCamelCase_ )
							self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.type_sequence_label_size) )

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    Dict  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							str		=  self.num_labels
							UpperCamelCase_:							Union[str, Any]		=  BeitForSemanticSegmentation(UpperCamelCase_ )
							model.to(UpperCamelCase_ )
							model.eval()
							UpperCamelCase_:							List[str]		=  model(UpperCamelCase_ )
							self.parent.assertEqual(
							    result.logits.shape  ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
							UpperCamelCase_:							Any		=  model(UpperCamelCase_  ,		labels=UpperCamelCase_ )
							self.parent.assertEqual(
							    result.logits.shape  ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )




			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							Optional[int]		=  self.prepare_config_and_inputs()
							UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							Any		=  config_and_inputs
							UpperCamelCase_:							List[str]		=  {"""pixel_values""": pixel_values}
							return config, inputs_dict




@require_torch
class     _UpperCamelCase			(   UpperCamelCase__       ,    UpperCamelCase__       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[Any]    =	(
			    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__UpperCamelCase :  int    =	(
			    {
			        '''feature-extraction''': BeitModel,
			        '''image-classification''': BeitForImageClassification,
			        '''image-segmentation''': BeitForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

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

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							Dict		=  BeitModelTester(self )
							UpperCamelCase_:							str		=  ConfigTester(self  ,		config_class=UpperCamelCase_  ,		has_text_modality=UpperCamelCase_  ,		hidden_size=37 )

			def  lowerCAmelCase__		(    self	:    List[Any] ):
							self.config_tester.run_common_tests()

			@unittest.skip(reason="""BEiT does not use inputs_embeds""" )
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							pass

			@require_torch_multi_gpu
			@unittest.skip(reason="""BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`""" )
			def  lowerCAmelCase__		(    self	:    Dict ):
							pass

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:							Optional[Any]		=  model_class(UpperCamelCase_ )
											self.assertIsInstance(model.get_input_embeddings()  ,		(nn.Module) )
											UpperCamelCase_:							Any		=  model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(UpperCamelCase_  ,		nn.Linear ) )

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_,				UpperCamelCase_:							Union[str, Any]		=  self.model_tester.prepare_config_and_inputs_for_common()

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

											UpperCamelCase_:							Union[str, Any]		=  ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]  ,		UpperCamelCase_ )

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							str		=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_ )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							int		=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_ )

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[Any]		=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )

			def  lowerCAmelCase__		(    self	:    str ):
							UpperCamelCase_:							Any		=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_ )

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):







							if not self.model_tester.is_training:
											return

							UpperCamelCase_,				UpperCamelCase_:							Union[str, Any]		=  self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase_:							Optional[int]		=  True

							for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if model_class in [*get_values(UpperCamelCase_ ), BeitForMaskedImageModeling]:
															continue

											UpperCamelCase_:							Union[str, Any]		=  model_class(UpperCamelCase_ )
											model.to(UpperCamelCase_ )
											model.train()
											UpperCamelCase_:							List[str]		=  self._prepare_for_class(UpperCamelCase_  ,		UpperCamelCase_  ,		return_labels=UpperCamelCase_ )
											UpperCamelCase_:							int		=  model(**UpperCamelCase_ ).loss
											loss.backward()

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_,				UpperCamelCase_:							Union[str, Any]		=  self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
											return

							UpperCamelCase_:							Union[str, Any]		=  False
							UpperCamelCase_:							int		=  True

							for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if (
											    model_class in [*get_values(UpperCamelCase_ ), BeitForMaskedImageModeling]
											    or not model_class.supports_gradient_checkpointing
											):
															continue

											UpperCamelCase_:							Union[str, Any]		=  model_class(UpperCamelCase_ )
											model.gradient_checkpointing_enable()
											model.to(UpperCamelCase_ )
											model.train()
											UpperCamelCase_:							Tuple		=  self._prepare_for_class(UpperCamelCase_  ,		UpperCamelCase_  ,		return_labels=UpperCamelCase_ )
											UpperCamelCase_:							Tuple		=  model(**UpperCamelCase_ ).loss
											loss.backward()

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_,				UpperCamelCase_:							str		=  self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase_:							Any		=  _config_zero_init(UpperCamelCase_ )
							for model_class in self.all_model_classes:
											UpperCamelCase_:							Optional[int]		=  model_class(config=UpperCamelCase_ )
											for name, param in model.named_parameters():
															# we skip lambda parameters as these require special initial values
															# determined by config.layer_scale_init_value
															if "lambda" in name:
																			continue
															if param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1e9).round() / 1e9).item()  ,		[0.0, 1.0]  ,		msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''  ,		)




			@slow
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):







							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCamelCase_:							Any		=  BeitModel.from_pretrained(UpperCamelCase_ )
											self.assertIsNotNone(UpperCamelCase_ )







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





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






			'''simple docstring'''

			@cached_property
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224""" ) if is_vision_available() else None

			@slow
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							List[str]		=  BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""" ).to(UpperCamelCase_ )

							UpperCamelCase_:							int		=  self.default_image_processor
							UpperCamelCase_:							Any		=  prepare_img()
							UpperCamelCase_:							Tuple		=  image_processor(images=UpperCamelCase_  ,		return_tensors="""pt""" ).pixel_values.to(UpperCamelCase_ )

							# prepare bool_masked_pos
							UpperCamelCase_:							Union[str, Any]		=  torch.ones((1, 196)  ,		dtype=torch.bool ).to(UpperCamelCase_ )

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:							Union[str, Any]		=  model(pixel_values=UpperCamelCase_  ,		bool_masked_pos=UpperCamelCase_ )
							UpperCamelCase_:							Any		=  outputs.logits

							# verify the logits
							UpperCamelCase_:							List[Any]		=  torch.Size((1, 196, 8192) )
							self.assertEqual(logits.shape  ,		UpperCamelCase_ )

							UpperCamelCase_:							int		=  torch.tensor(
							    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(UpperCamelCase_ )

							self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]  ,		UpperCamelCase_  ,		atol=1e-2 ) )

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""" ).to(UpperCamelCase_ )

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

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:							Dict		=  model(**UpperCamelCase_ )
							UpperCamelCase_:							str		=  outputs.logits

							# verify the logits
							UpperCamelCase_:							str		=  torch.Size((1, 1000) )
							self.assertEqual(logits.shape  ,		UpperCamelCase_ )

							UpperCamelCase_:							int		=  torch.tensor([-1.2385, -1.0987, -1.0108] ).to(UpperCamelCase_ )

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

							UpperCamelCase_:							int		=  281
							self.assertEqual(logits.argmax(-1 ).item()  ,		UpperCamelCase_ )

			@slow
			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Any		=  BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""" ).to(
							    UpperCamelCase_ )

							UpperCamelCase_:							Union[str, Any]		=  self.default_image_processor
							UpperCamelCase_:							Any		=  prepare_img()
							UpperCamelCase_:							int		=  image_processor(images=UpperCamelCase_  ,		return_tensors="""pt""" ).to(UpperCamelCase_ )

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:							Union[str, Any]		=  model(**UpperCamelCase_ )
							UpperCamelCase_:							int		=  outputs.logits

							# verify the logits
							UpperCamelCase_:							Union[str, Any]		=  torch.Size((1, 2_1841) )
							self.assertEqual(logits.shape  ,		UpperCamelCase_ )

							UpperCamelCase_:							List[str]		=  torch.tensor([1.6881, -0.2787, 0.5901] ).to(UpperCamelCase_ )

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

							UpperCamelCase_:							str		=  2396
							self.assertEqual(logits.argmax(-1 ).item()  ,		UpperCamelCase_ )

			@slow
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							str		=  BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
							UpperCamelCase_:							str		=  model.to(UpperCamelCase_ )

							UpperCamelCase_:							Union[str, Any]		=  BeitImageProcessor(do_resize=UpperCamelCase_  ,		size=640  ,		do_center_crop=UpperCamelCase_ )

							UpperCamelCase_:							Any		=  load_dataset("""hf-internal-testing/fixtures_ade20k"""  ,		split="""test""" )
							UpperCamelCase_:							int		=  Image.open(ds[0]["""file"""] )
							UpperCamelCase_:							Tuple		=  image_processor(images=UpperCamelCase_  ,		return_tensors="""pt""" ).to(UpperCamelCase_ )

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:							List[str]		=  model(**UpperCamelCase_ )
							UpperCamelCase_:							Tuple		=  outputs.logits

							# verify the logits
							UpperCamelCase_:							Dict		=  torch.Size((1, 150, 160, 160) )
							self.assertEqual(logits.shape  ,		UpperCamelCase_ )

							UpperCamelCase_:							str		=  version.parse(PIL.__version__ ) < version.parse("""9.0.0""" )

							if is_pillow_less_than_a:
											UpperCamelCase_:							int		=  torch.tensor(
											    [
											        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
											        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
											        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
											    ]  ,		device=UpperCamelCase_  ,		)
							else:
											UpperCamelCase_:							Optional[Any]		=  torch.tensor(
											    [
											        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
											        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
											        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
											    ]  ,		device=UpperCamelCase_  ,		)

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




			@slow
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							Optional[int]		=  BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640""" )
							UpperCamelCase_:							List[Any]		=  model.to(UpperCamelCase_ )

							UpperCamelCase_:							Optional[int]		=  BeitImageProcessor(do_resize=UpperCamelCase_  ,		size=640  ,		do_center_crop=UpperCamelCase_ )

							UpperCamelCase_:							Optional[int]		=  load_dataset("""hf-internal-testing/fixtures_ade20k"""  ,		split="""test""" )
							UpperCamelCase_:							str		=  Image.open(ds[0]["""file"""] )
							UpperCamelCase_:							str		=  image_processor(images=UpperCamelCase_  ,		return_tensors="""pt""" ).to(UpperCamelCase_ )

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:							str		=  model(**UpperCamelCase_ )

							UpperCamelCase_:							int		=  outputs.logits.detach().cpu()

							UpperCamelCase_:							Any		=  image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_  ,		target_sizes=[(500, 300)] )
							UpperCamelCase_:							Optional[Any]		=  torch.Size((500, 300) )
							self.assertEqual(segmentation[0].shape  ,		UpperCamelCase_ )

							UpperCamelCase_:							Any		=  image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_ )
							UpperCamelCase_:							Union[str, Any]		=  torch.Size((160, 160) )
							self.assertEqual(segmentation[0].shape  ,		UpperCamelCase_ )



import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_  :	Union[str, Any] 				=					logging.get_logger(__name__)

set_seed(7_70)


lowerCamelCase_  :	str 				=					{
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


lowerCamelCase_  :	Any 				=					{
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

lowerCamelCase_  :	str 				=					os.path.dirname(os.path.abspath(__file__))
lowerCamelCase_  :	Any 				=					os.path.join(os.path.expanduser("""~"""), """.cache""")
lowerCamelCase_  :	Dict 				=					os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False  )					->		int:
				UpperCamelCase_:							Union[str, Any]		=  model_type
				if use_small:
								key += "_small"
				return os.path.join(lowerCamelCase       ,      REMOTE_MODEL_PATHS[key]["""file_name"""]  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		Tuple:
				os.makedirs(lowerCamelCase       ,      exist_ok=lowerCamelCase  )
				hf_hub_download(repo_id=lowerCamelCase       ,      filename=lowerCamelCase       ,      local_dir=lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Optional[int]:
				if model_type == "text":
								UpperCamelCase_:							str		=  BarkSemanticModel
								UpperCamelCase_:							Dict		=  BarkSemanticConfig
								UpperCamelCase_:							int		=  BarkSemanticGenerationConfig
				elif model_type == "coarse":
								UpperCamelCase_:							str		=  BarkCoarseModel
								UpperCamelCase_:							int		=  BarkCoarseConfig
								UpperCamelCase_:							Any		=  BarkCoarseGenerationConfig
				elif model_type == "fine":
								UpperCamelCase_:							Optional[Any]		=  BarkFineModel
								UpperCamelCase_:							int		=  BarkFineConfig
								UpperCamelCase_:							Dict		=  BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				UpperCamelCase_:							str		=  F'''{model_type}_small''' if use_small else model_type
				UpperCamelCase_:							List[Any]		=  REMOTE_MODEL_PATHS[model_key]
				if not os.path.exists(lowerCamelCase  ):
								logger.info(F'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''  )
								_download(model_info["""repo_id"""]       ,      model_info["""file_name"""]  )
				UpperCamelCase_:							int		=  torch.load(lowerCamelCase       ,      map_location=lowerCamelCase  )
				# this is a hack
				UpperCamelCase_:							Tuple		=  checkpoint["""model_args"""]
				if "input_vocab_size" not in model_args:
								UpperCamelCase_:							int		=  model_args["""vocab_size"""]
								UpperCamelCase_:							Optional[int]		=  model_args["""vocab_size"""]
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				UpperCamelCase_:							Tuple		=  model_args.pop("""n_head"""  )
				UpperCamelCase_:							Dict		=  model_args.pop("""n_embd"""  )
				UpperCamelCase_:							List[str]		=  model_args.pop("""n_layer"""  )

				UpperCamelCase_:							Optional[Any]		=  ConfigClass(**checkpoint["""model_args"""]  )
				UpperCamelCase_:							Optional[Any]		=  ModelClass(config=lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GenerationConfigClass()

				UpperCamelCase_:							Optional[Any]		=  model_generation_config
				UpperCamelCase_:							Optional[int]		=  checkpoint["""model"""]
				# fixup checkpoint
				UpperCamelCase_:							Dict		=  """_orig_mod."""
				for k, v in list(state_dict.items()  ):
								if k.startswith(lowerCamelCase  ):
												# replace part of the key with corresponding layer name in HF implementation
												UpperCamelCase_:							Optional[int]		=  k[len(lowerCamelCase  ) :]
												for old_layer_name in new_layer_name_dict:
																UpperCamelCase_:							Dict		=  new_k.replace(lowerCamelCase       ,      new_layer_name_dict[old_layer_name]  )

												UpperCamelCase_:							List[str]		=  state_dict.pop(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  set(state_dict.keys()  ) - set(model.state_dict().keys()  )
				UpperCamelCase_:							Dict		=  {k for k in extra_keys if not k.endswith(""".attn.bias"""  )}
				UpperCamelCase_:							Optional[Any]		=  set(model.state_dict().keys()  ) - set(state_dict.keys()  )
				UpperCamelCase_:							Union[str, Any]		=  {k for k in missing_keys if not k.endswith(""".attn.bias"""  )}
				if len(lowerCamelCase  ) != 0:
								raise ValueError(F'''extra keys found: {extra_keys}'''  )
				if len(lowerCamelCase  ) != 0:
								raise ValueError(F'''missing keys: {missing_keys}'''  )
				model.load_state_dict(lowerCamelCase       ,      strict=lowerCamelCase  )
				UpperCamelCase_:							str		=  model.num_parameters(exclude_embeddings=lowerCamelCase  )
				UpperCamelCase_:							int		=  checkpoint["""best_val_loss"""].item()
				logger.info(F'''model loaded: {round(n_params/1E6       ,      1  )}M params, {round(lowerCamelCase       ,      3  )} loss'''  )
				model.eval()
				model.to(lowerCamelCase  )
				del checkpoint, state_dict

				return model







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False       ,      lowerCamelCase="text"  )					->		Any:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				UpperCamelCase_:							Union[str, Any]		=  """cpu"""  # do conversion on cpu

				UpperCamelCase_:							int		=  _get_ckpt_path(lowerCamelCase       ,      use_small=lowerCamelCase  )
				UpperCamelCase_:							Dict		=  _load_model(lowerCamelCase       ,      lowerCamelCase       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				# load bark initial model
				UpperCamelCase_:							List[Any]		=  _bark_load_model(lowerCamelCase       ,      """cpu"""       ,      model_type=lowerCamelCase       ,      use_small=lowerCamelCase  )

				if model_type == "text":
								UpperCamelCase_:							Tuple		=  bark_model["""model"""]

				if model.num_parameters(exclude_embeddings=lowerCamelCase  ) != bark_model.get_num_params():
								raise ValueError("""initial and new models don't have the same number of parameters"""  )

				# check if same output as the bark model
				UpperCamelCase_:							Optional[Any]		=  5
				UpperCamelCase_:							List[str]		=  10

				if model_type in ["text", "coarse"]:
								UpperCamelCase_:							int		=  torch.randint(2_56       ,      (batch_size, sequence_length)       ,      dtype=torch.int  )
								UpperCamelCase_:							Tuple		=  bark_model(lowerCamelCase  )[0]

								UpperCamelCase_:							Optional[Any]		=  model(lowerCamelCase  )

								# take last logits
								UpperCamelCase_:							Union[str, Any]		=  output_new_model_total.logits[:, [-1], :]

				else:
								UpperCamelCase_:							Tuple		=  3
								UpperCamelCase_:							List[Any]		=  8
								UpperCamelCase_:							List[str]		=  torch.randint(2_56       ,      (batch_size, sequence_length, n_codes_total)       ,      dtype=torch.int  )

								UpperCamelCase_:							int		=  model(lowerCamelCase       ,      lowerCamelCase  )
								UpperCamelCase_:							Any		=  bark_model(lowerCamelCase       ,      lowerCamelCase  )

								UpperCamelCase_:							Optional[int]		=  output_new_model_total.logits

				# output difference should come from the difference of self-attention implementation design
				if output_new_model.shape != output_old_model.shape:
								raise ValueError("""initial and new outputs don't have the same shape"""  )
				if (output_new_model - output_old_model).abs().max().item() > 1E-3:
								raise ValueError("""initial and new outputs are not equal"""  )

				Path(lowerCamelCase  ).mkdir(exist_ok=lowerCamelCase  )
				model.save_pretrained(lowerCamelCase  )







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      )					->		str:
				UpperCamelCase_:							List[str]		=  os.path.join(lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							List[Any]		=  BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Optional[int]		=  BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase       ,      """config.json"""  )  )
				UpperCamelCase_:							Any		=  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							Optional[Any]		=  BarkSemanticModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  BarkCoarseModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  BarkFineModel.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  EncodecModel.from_pretrained("""facebook/encodec_24khz"""  )

				UpperCamelCase_:							int		=  BarkConfig.from_sub_model_configs(
				    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config       ,      coarseAcoustic.generation_config       ,      fineAcoustic.generation_config  )

				UpperCamelCase_:							Optional[Any]		=  BarkModel(lowerCamelCase  )

				UpperCamelCase_:							int		=  semantic
				UpperCamelCase_:							Tuple		=  coarseAcoustic
				UpperCamelCase_:							Optional[int]		=  fineAcoustic
				UpperCamelCase_:							Any		=  codec

				UpperCamelCase_:							Dict		=  bark_generation_config

				Path(lowerCamelCase  ).mkdir(exist_ok=lowerCamelCase  )
				bark.save_pretrained(lowerCamelCase       ,      repo_id=lowerCamelCase       ,      push_to_hub=lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :	List[Any] 				=					argparse.ArgumentParser()
							# Required parameters

							parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

							lowerCamelCase_  :	Dict 				=					parser.parse_args()

							load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)






