



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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

set_seed(770)


__SCREAMING_SNAKE_CASE           =  {
    """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""",
}


__SCREAMING_SNAKE_CASE           =  {
    """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""",
    },
}

__SCREAMING_SNAKE_CASE           =  os.path.dirname(os.path.abspath(__file__))
__SCREAMING_SNAKE_CASE           =  os.path.join(os.path.expanduser("""~"""), """.cache""")
__SCREAMING_SNAKE_CASE           =  os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=False    ):
			A							:					List[str]              =						model_type
			if use_small:
						key += "_small"
			return os.path.join(_lowerCamelCase ,    REMOTE_MODEL_PATHS[key]["file_name"]    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			os.makedirs(_lowerCamelCase ,    exist_ok=_lowerCamelCase    )
			hf_hub_download(repo_id=_lowerCamelCase ,    filename=_lowerCamelCase ,    local_dir=_lowerCamelCase    )




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase=False ,    _lowerCamelCase="text"    ):
			if model_type == "text":
						A							:					Any              =						BarkSemanticModel
						A							:					Dict              =						BarkSemanticConfig
						A							:					Tuple              =						BarkSemanticGenerationConfig
			elif model_type == "coarse":
						A							:					Optional[int]              =						BarkCoarseModel
						A							:					Union[str, Any]              =						BarkCoarseConfig
						A							:					Optional[Any]              =						BarkCoarseGenerationConfig
			elif model_type == "fine":
						A							:					List[str]              =						BarkFineModel
						A							:					Optional[Any]              =						BarkFineConfig
						A							:					Any              =						BarkFineGenerationConfig
			else:
						raise NotImplementedError()
			A							:					List[Any]              =						f"""{model_type}_small""" if use_small else model_type
			A							:					Optional[int]              =						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"]    )
			A							:					Optional[Any]              =						torch.load(_lowerCamelCase ,    map_location=_lowerCamelCase    )
			# this is a hack
			A							:					Any              =						checkpoint["model_args"]
			if "input_vocab_size" not in model_args:
						A							:					Union[str, Any]              =						model_args["vocab_size"]
						A							:					Tuple              =						model_args["vocab_size"]
						del model_args["vocab_size"]

			# convert Bark model arguments to HF Bark model arguments
			A							:					Optional[Any]              =						model_args.pop("n_head"    )
			A							:					Optional[Any]              =						model_args.pop("n_embd"    )
			A							:					Any              =						model_args.pop("n_layer"    )

			A							:					int              =						ConfigClass(**checkpoint["model_args"]    )
			A							:					Optional[Any]              =						ModelClass(config=_lowerCamelCase    )
			A							:					int              =						GenerationConfigClass()

			A							:					Any              =						model_generation_config
			A							:					Optional[int]              =						checkpoint["model"]
			# fixup checkpoint
			A							:					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
									A							:					List[Any]              =						k[len(_lowerCamelCase    ) :]
									for old_layer_name in new_layer_name_dict:
												A							:					List[str]              =						new_k.replace(_lowerCamelCase ,    new_layer_name_dict[old_layer_name]    )

									A							:					Optional[int]              =						state_dict.pop(_lowerCamelCase    )

			A							:					Tuple              =						set(state_dict.keys()    ) - set(model.state_dict().keys()    )
			A							:					str              =						{k for k in extra_keys if not k.endswith(".attn.bias"    )}
			A							:					str              =						set(model.state_dict().keys()    ) - set(state_dict.keys()    )
			A							:					str              =						{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    )
			A							:					List[str]              =						model.num_parameters(exclude_embeddings=_lowerCamelCase    )
			A							:					Optional[Any]              =						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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase=False ,    _lowerCamelCase="text"    ):
			if model_type not in ("text", "coarse", "fine"):
						raise NotImplementedError()

			A							:					Dict              =						"cpu"  # do conversion on cpu

			A							:					Tuple              =						_get_ckpt_path(_lowerCamelCase ,    use_small=_lowerCamelCase    )
			A							:					str              =						_load_model(_lowerCamelCase ,    _lowerCamelCase ,    model_type=_lowerCamelCase ,    use_small=_lowerCamelCase    )

			# load bark initial model
			A							:					Optional[Any]              =						_bark_load_model(_lowerCamelCase ,    "cpu" ,    model_type=_lowerCamelCase ,    use_small=_lowerCamelCase    )

			if model_type == "text":
						A							:					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
			A							:					Union[str, Any]              =						5
			A							:					Tuple              =						10

			if model_type in ["text", "coarse"]:
						A							:					int              =						torch.randint(256 ,    (batch_size, sequence_length) ,    dtype=torch.int    )
						A							:					int              =						bark_model(_lowerCamelCase    )[0]

						A							:					Dict              =						model(_lowerCamelCase    )

						# take last logits
						A							:					Dict              =						output_new_model_total.logits[:, [-1], :]

			else:
						A							:					str              =						3
						A							:					Optional[int]              =						8
						A							:					List[str]              =						torch.randint(256 ,    (batch_size, sequence_length, n_codes_total) ,    dtype=torch.int    )

						A							:					Optional[Any]              =						model(_lowerCamelCase ,    _lowerCamelCase    )
						A							:					Optional[int]              =						bark_model(_lowerCamelCase ,    _lowerCamelCase    )

						A							:					List[Any]              =						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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    ):
			A							:					str              =						os.path.join(_lowerCamelCase ,    _lowerCamelCase    )

			A							:					List[Any]              =						BarkSemanticConfig.from_pretrained(os.path.join(_lowerCamelCase ,    "config.json"    )    )
			A							:					Dict              =						BarkCoarseConfig.from_pretrained(os.path.join(_lowerCamelCase ,    "config.json"    )    )
			A							:					List[Any]              =						BarkFineConfig.from_pretrained(os.path.join(_lowerCamelCase ,    "config.json"    )    )
			A							:					Dict              =						EncodecConfig.from_pretrained("facebook/encodec_24khz"    )

			A							:					int              =						BarkSemanticModel.from_pretrained(_lowerCamelCase    )
			A							:					Tuple              =						BarkCoarseModel.from_pretrained(_lowerCamelCase    )
			A							:					Optional[Any]              =						BarkFineModel.from_pretrained(_lowerCamelCase    )
			A							:					Optional[int]              =						EncodecModel.from_pretrained("facebook/encodec_24khz"    )

			A							:					int              =						BarkConfig.from_sub_model_configs(
			    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

			A							:					Tuple              =						BarkGenerationConfig.from_sub_model_configs(
			    semantic.generation_config ,    coarseAcoustic.generation_config ,    fineAcoustic.generation_config    )

			A							:					Union[str, Any]              =						BarkModel(_lowerCamelCase    )

			A							:					Any              =						semantic
			A							:					Any              =						coarseAcoustic
			A							:					Any              =						fineAcoustic
			A							:					Tuple              =						codec

			A							:					Any              =						bark_generation_config

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


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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.""")

							__SCREAMING_SNAKE_CASE           =  parser.parse_args()

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




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    DeiTForImageClassification,
							    DeiTForImageClassificationWithTeacher,
							    DeiTForMaskedImageModeling,
							    DeiTModel,
							)
							from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			Tuple	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Tuple=13	,	__lowerCamelCase      :			List[str]=30	,	__lowerCamelCase      :			Tuple=2	,	__lowerCamelCase      :			Any=3	,	__lowerCamelCase      :			Dict=True	,	__lowerCamelCase      :			Optional[int]=True	,	__lowerCamelCase      :			int=32	,	__lowerCamelCase      :			List[str]=5	,	__lowerCamelCase      :			Any=4	,	__lowerCamelCase      :			Optional[int]=37	,	__lowerCamelCase      :			List[Any]="gelu"	,	__lowerCamelCase      :			int=0.1	,	__lowerCamelCase      :			Union[str, Any]=0.1	,	__lowerCamelCase      :			Optional[Any]=10	,	__lowerCamelCase      :			Dict=0.02	,	__lowerCamelCase      :			Tuple=3	,	__lowerCamelCase      :			Union[str, Any]=None	,	__lowerCamelCase      :			List[Any]=2	,	)      ->				str:
									A							:					List[Any]              =						parent
									A							:					Optional[int]              =						batch_size
									A							:					Any              =						image_size
									A							:					Optional[Any]              =						patch_size
									A							:					Optional[Any]              =						num_channels
									A							:					Tuple              =						is_training
									A							:					Optional[Any]              =						use_labels
									A							:					Union[str, Any]              =						hidden_size
									A							:					Tuple              =						num_hidden_layers
									A							:					Union[str, Any]              =						num_attention_heads
									A							:					Union[str, Any]              =						intermediate_size
									A							:					Any              =						hidden_act
									A							:					Tuple              =						hidden_dropout_prob
									A							:					Dict              =						attention_probs_dropout_prob
									A							:					Any              =						type_sequence_label_size
									A							:					Tuple              =						initializer_range
									A							:					List[Any]              =						scope
									A							:					Optional[int]              =						encoder_stride

									# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
									A							:					List[str]              =						(image_size // patch_size) ** 2
									A							:					List[str]              =						num_patches + 2







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				int:
									A							:					Union[str, Any]              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

									A							:					List[Any]              =						None
									if self.use_labels:
												A							:					List[str]              =						ids_tensor([self.batch_size]	,	self.type_sequence_label_size )

									A							:					Dict              =						self.get_config()

									return config, pixel_values, labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Dict:
									return DeiTConfig(
									    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=__lowerCamelCase	,	initializer_range=self.initializer_range	,	encoder_stride=self.encoder_stride	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			List[str] )      ->				int:
									A							:					Optional[int]              =						DeiTModel(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					List[Any]              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Any )      ->				Any:
									A							:					List[Any]              =						DeiTForMaskedImageModeling(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Optional[int]              =						model(__lowerCamelCase )
									self.parent.assertEqual(
									    result.reconstruction.shape	,	(self.batch_size, self.num_channels, self.image_size, self.image_size) )

									# test greyscale images
									A							:					List[str]              =						1
									A							:					Optional[int]              =						DeiTForMaskedImageModeling(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()

									A							:					Dict              =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
									A							:					Optional[int]              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.reconstruction.shape	,	(self.batch_size, 1, self.image_size, self.image_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			int )      ->				Dict:
									A							:					str              =						self.type_sequence_label_size
									A							:					List[str]              =						DeiTForImageClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Tuple              =						model(__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.type_sequence_label_size) )

									# test greyscale images
									A							:					Any              =						1
									A							:					str              =						DeiTForImageClassification(__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()

									A							:					Tuple              =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
									A							:					Tuple              =						model(__lowerCamelCase	,	labels=__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.type_sequence_label_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[int]:
									A							:					Dict              =						self.prepare_config_and_inputs()
									(
									    (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , 
									)							:					Tuple              =						config_and_inputs
									A							:					int              =						{"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (
						    (
						        DeiTModel,
						        DeiTForImageClassification,
						        DeiTForImageClassificationWithTeacher,
						        DeiTForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						a__   =  (
						    {
						        "feature-extraction": DeiTModel,
						        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[int]:
									A							:					str              =						DeiTModelTester(self )
									A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase	,	hidden_size=37 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Dict:
									self.config_tester.run_common_tests()







						@unittest.skip(reason="DeiT does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				int:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A       , A							:					Dict              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Dict              =						model_class(__lowerCamelCase )
												self.assertIsInstance(model.get_input_embeddings()	,	(nn.Module) )
												A							:					Any              =						model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__lowerCamelCase	,	nn.Linear ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

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

												A							:					Any              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									A							:					Any              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Dict:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]=False )      ->				str:
									A							:					Union[str, Any]              =						super()._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )

									if return_labels:
												if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
															del inputs_dict["labels"]

									return inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[int]:
									if not self.model_tester.is_training:
												return

									A       , A							:					Optional[int]              =						self.model_tester.prepare_config_and_inputs_for_common()
									A							:					Dict              =						True

									for model_class in self.all_model_classes:
												# DeiTForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(__lowerCamelCase )
												    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
												):
															continue
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												model.to(__lowerCamelCase )
												model.train()
												A							:					Union[str, Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Dict              =						model(**__lowerCamelCase ).loss
												loss.backward()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
												return

									A							:					Tuple              =						False
									A							:					Any              =						True

									for model_class in self.all_model_classes:
												if model_class in get_values(__lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
															continue
												# DeiTForImageClassificationWithTeacher supports inference-only
												if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
															continue
												A							:					List[str]              =						model_class(__lowerCamelCase )
												model.gradient_checkpointing_enable()
												model.to(__lowerCamelCase )
												model.train()
												A							:					Dict              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )
												A							:					Tuple              =						model(**__lowerCamelCase ).loss
												loss.backward()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Any:
									A       , A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs_for_common()

									A							:					int              =						[
									    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
									    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
									    {"title": "regression", "num_labels": 1, "dtype": torch.float},
									]

									for model_class in self.all_model_classes:
												if (
												    model_class
												    not in [
												        *get_values(__lowerCamelCase ),
												        *get_values(__lowerCamelCase ),
												    ]
												    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
												):
															continue

												for problem_type in problem_types:
															with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}""" ):
																		A							:					Tuple              =						problem_type["title"]
																		A							:					Optional[Any]              =						problem_type["num_labels"]

																		A							:					List[str]              =						model_class(__lowerCamelCase )
																		model.to(__lowerCamelCase )
																		model.train()

																		A							:					List[Any]              =						self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase	,	return_labels=__lowerCamelCase )

																		if problem_type["num_labels"] > 1:
																					A							:					List[str]              =						inputs["labels"].unsqueeze(1 ).repeat(1	,	problem_type["num_labels"] )

																		A							:					int              =						inputs["labels"].to(problem_type["dtype"] )

																		# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																		# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																		# they have the same size." which is a symptom something in wrong for the regression problem.
																		# See https://github.com/huggingface/transformers/issues/11780
																		with warnings.catch_warnings(record=__lowerCamelCase ) as warning_list:
																					A							:					Optional[Any]              =						model(**__lowerCamelCase ).loss
																		for w in warning_list:
																					if "Using a target size that is different to the input size" in str(w.message ):
																								raise ValueError(
																								    F"""Something is going wrong in the regression problem: intercepted {w.message}""" )

																		loss.backward()







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Optional[Any]              =						DeiTModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Optional[int]              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image






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






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									return (
									    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
									    if is_vision_available()
									    else None
									)







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[int]:
									A							:					Optional[int]              =						DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
									    __lowerCamelCase )

									A							:					List[Any]              =						self.default_image_processor
									A							:					List[Any]              =						prepare_img()
									A							:					Optional[Any]              =						image_processor(images=__lowerCamelCase	,	return_tensors="pt" ).to(__lowerCamelCase )

									# forward pass
									with torch.no_grad():
												A							:					List[str]              =						model(**__lowerCamelCase )

									# verify the logits
									A							:					str              =						torch.Size((1, 10_00) )
									self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

									A							:					List[str]              =						torch.tensor([-1.0266, 0.1912, -1.2861] ).to(__lowerCamelCase )

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







						@slow
						@require_accelerate
						@require_torch_gpu
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					str              =						DeiTModel.from_pretrained(
									    "facebook/deit-base-distilled-patch16-224"	,	torch_dtype=torch.floataa	,	device_map="auto" )
									A							:					Dict              =						self.default_image_processor

									A							:					Optional[int]              =						prepare_img()
									A							:					Union[str, Any]              =						image_processor(images=__lowerCamelCase	,	return_tensors="pt" )
									A							:					Union[str, Any]              =						inputs.pixel_values.to(__lowerCamelCase )

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
												A							:					List[str]              =						model(__lowerCamelCase )