

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__								=		logging.get_logger(__name__)

set_seed(7_7_0)


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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Any=False	):




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						model_type
	if use_small:
		key += "_small"
	return os.path.join(UpperCAmelCase__							,       REMOTE_MODEL_PATHS[key]['file_name']	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     str	):




	'''simple docstring'''




	os.makedirs(UpperCAmelCase__							,       exist_ok=UpperCAmelCase__	)
	hf_hub_download(repo_id=UpperCAmelCase__							,       filename=UpperCAmelCase__							,       local_dir=UpperCAmelCase__	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Union[str, Any]=False							,       UpperCAmelCase_							:     Union[str, Any]="text"	):




	'''simple docstring'''




	if model_type == "text":
		_UpperCamelCase   :		List[Any]			=						BarkSemanticModel
		_UpperCamelCase   :		str			=						BarkSemanticConfig
		_UpperCamelCase   :		Union[str, Any]			=						BarkSemanticGenerationConfig
	elif model_type == "coarse":
		_UpperCamelCase   :		Union[str, Any]			=						BarkCoarseModel
		_UpperCamelCase   :		List[str]			=						BarkCoarseConfig
		_UpperCamelCase   :		int			=						BarkCoarseGenerationConfig
	elif model_type == "fine":
		_UpperCamelCase   :		Tuple			=						BarkFineModel
		_UpperCamelCase   :		int			=						BarkFineConfig
		_UpperCamelCase   :		Optional[int]			=						BarkFineGenerationConfig
	else:
		raise NotImplementedError()
	_UpperCamelCase   :		List[str]			=						F'''{model_type}_small''' if use_small else model_type
	_UpperCamelCase   :		Optional[int]			=						REMOTE_MODEL_PATHS[model_key]
	if not os.path.exists(UpperCAmelCase__	):
		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(UpperCAmelCase__							,       map_location=UpperCAmelCase__	)
	# this is a hack
	_UpperCamelCase   :		int			=						checkpoint["""model_args"""]
	if "input_vocab_size" not in model_args:
		_UpperCamelCase   :		Optional[Any]			=						model_args["""vocab_size"""]
		_UpperCamelCase   :		Dict			=						model_args["""vocab_size"""]
		del model_args["vocab_size"]

	# convert Bark model arguments to HF Bark model arguments
	_UpperCamelCase   :		Dict			=						model_args.pop('n_head'	)
	_UpperCamelCase   :		Optional[int]			=						model_args.pop('n_embd'	)
	_UpperCamelCase   :		Optional[Any]			=						model_args.pop('n_layer'	)

	_UpperCamelCase   :		Union[str, Any]			=						ConfigClass(**checkpoint['model_args']	)
	_UpperCamelCase   :		List[str]			=						ModelClass(config=UpperCAmelCase__	)
	_UpperCamelCase   :		int			=						GenerationConfigClass()

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

			_UpperCamelCase   :		Dict			=						state_dict.pop(UpperCAmelCase__	)

	_UpperCamelCase   :		str			=						set(state_dict.keys()	) - set(model.state_dict().keys()	)
	_UpperCamelCase   :		str			=						{k for k in extra_keys if not k.endswith('.attn.bias'	)}
	_UpperCamelCase   :		List[Any]			=						set(model.state_dict().keys()	) - set(state_dict.keys()	)
	_UpperCamelCase   :		List[str]			=						{k for k in missing_keys if not k.endswith('.attn.bias'	)}
	if len(UpperCAmelCase__	) != 0:
		raise ValueError(F'''extra keys found: {extra_keys}'''	)
	if len(UpperCAmelCase__	) != 0:
		raise ValueError(F'''missing keys: {missing_keys}'''	)
	model.load_state_dict(UpperCAmelCase__							,       strict=UpperCAmelCase__	)
	_UpperCamelCase   :		Dict			=						model.num_parameters(exclude_embeddings=UpperCAmelCase__	)
	_UpperCamelCase   :		Union[str, Any]			=						checkpoint["""best_val_loss"""].item()
	logger.info(F'''model loaded: {round(n_params/1e6							,       1	)}M params, {round(UpperCAmelCase__							,       3	)} loss'''	)
	model.eval()
	model.to(UpperCAmelCase__	)
	del checkpoint, state_dict

	return model






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Optional[int]=False							,       UpperCAmelCase_							:     Dict="text"	):




	'''simple docstring'''




	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	_UpperCamelCase   :		str			=						"""cpu"""  # do conversion on cpu

	_UpperCamelCase   :		List[str]			=						_get_ckpt_path(UpperCAmelCase__							,       use_small=UpperCAmelCase__	)
	_UpperCamelCase   :		Union[str, Any]			=						_load_model(UpperCAmelCase__							,       UpperCAmelCase__							,       model_type=UpperCAmelCase__							,       use_small=UpperCAmelCase__	)

	# load bark initial model
	_UpperCamelCase   :		Union[str, Any]			=						_bark_load_model(UpperCAmelCase__							,       'cpu'							,       model_type=UpperCAmelCase__							,       use_small=UpperCAmelCase__	)

	if model_type == "text":
		_UpperCamelCase   :		List[Any]			=						bark_model["""model"""]

	if model.num_parameters(exclude_embeddings=UpperCAmelCase__	) != 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   :		str			=						5
	_UpperCamelCase   :		Any			=						1_0

	if model_type in ["text", "coarse"]:
		_UpperCamelCase   :		List[Any]			=						torch.randint(2_5_6							,       (batch_size, sequence_length)							,       dtype=torch.int	)
		_UpperCamelCase   :		Tuple			=						bark_model(UpperCAmelCase__	)[0]

		_UpperCamelCase   :		List[Any]			=						model(UpperCAmelCase__	)

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

	else:
		_UpperCamelCase   :		Dict			=						3
		_UpperCamelCase   :		Dict			=						8
		_UpperCamelCase   :		Optional[Any]			=						torch.randint(2_5_6							,       (batch_size, sequence_length, n_codes_total)							,       dtype=torch.int	)

		_UpperCamelCase   :		int			=						model(UpperCAmelCase__							,       UpperCAmelCase__	)
		_UpperCamelCase   :		Tuple			=						bark_model(UpperCAmelCase__							,       UpperCAmelCase__	)

		_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(UpperCAmelCase__	).mkdir(exist_ok=UpperCAmelCase__	)
	model.save_pretrained(UpperCAmelCase__	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Tuple							,       ):




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						os.path.join(UpperCAmelCase__							,       UpperCAmelCase__	)

	_UpperCamelCase   :		str			=						BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase__							,       'config.json'	)	)
	_UpperCamelCase   :		Optional[Any]			=						BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase__							,       'config.json'	)	)
	_UpperCamelCase   :		List[Any]			=						BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase__							,       'config.json'	)	)
	_UpperCamelCase   :		Any			=						EncodecConfig.from_pretrained('facebook/encodec_24khz'	)

	_UpperCamelCase   :		List[str]			=						BarkSemanticModel.from_pretrained(UpperCAmelCase__	)
	_UpperCamelCase   :		str			=						BarkCoarseModel.from_pretrained(UpperCAmelCase__	)
	_UpperCamelCase   :		List[Any]			=						BarkFineModel.from_pretrained(UpperCAmelCase__	)
	_UpperCamelCase   :		Tuple			=						EncodecModel.from_pretrained('facebook/encodec_24khz'	)

	_UpperCamelCase   :		Union[str, Any]			=						BarkConfig.from_sub_model_configs(
	    UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__							,       UpperCAmelCase__	)

	_UpperCamelCase   :		List[Any]			=						BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config							,       coarseAcoustic.generation_config							,       fineAcoustic.generation_config	)

	_UpperCamelCase   :		Union[str, Any]			=						BarkModel(UpperCAmelCase__	)

	_UpperCamelCase   :		str			=						semantic
	_UpperCamelCase   :		Optional[int]			=						coarseAcoustic
	_UpperCamelCase   :		Tuple			=						fineAcoustic
	_UpperCamelCase   :		Union[str, Any]			=						codec

	_UpperCamelCase   :		Tuple			=						bark_generation_config

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


if __name__ == "__main__":
							lowerCAmelCase__								=		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__								=		parser.parse_args()

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







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 	lowercase    :


		"""simple docstring"""




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


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

			_UpperCamelCase   :		str			=						self.get_config()

			return config, pixel_values, labels


		def 		A__					(				self):
			return BitConfig(
			    num_channels=self.num_channels	,       embeddings_size=self.embeddings_size	,       hidden_sizes=self.hidden_sizes	,       depths=self.depths	,       hidden_act=self.hidden_act	,       num_labels=self.num_labels	,       out_features=self.out_features	,       out_indices=self.out_indices	,       num_groups=self.num_groups	,       )


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


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Dict			=						self.num_labels
			_UpperCamelCase   :		Dict			=						BitForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Dict			=						model(__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						BitBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		List[Any]			=						model(__snake_case)

			# 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   :		str			=						BitBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case)

			# 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 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						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 	lowercase    (				_lowercase    ,							_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		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 		A__					(				self):
			_UpperCamelCase   :		Dict			=						BitModelTester(self)
			_UpperCamelCase   :		Optional[Any]			=						ConfigTester(self	,       config_class=__snake_case	,       has_text_modality=__snake_case)


		def 		A__					(				self):
			self.create_and_test_config_common_properties()
			self.config_tester.create_and_test_config_to_json_string()
			self.config_tester.create_and_test_config_to_json_file()
			self.config_tester.create_and_test_config_from_and_save_pretrained()
			self.config_tester.create_and_test_config_with_num_labels()
			self.config_tester.check_config_can_be_init_without_params()
			self.config_tester.check_config_arguments_init()


		def 		A__					(				self):
			return


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


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


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


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

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

				_UpperCamelCase   :		List[Any]			=						['pixel_values']
				self.assertListEqual(arg_names[:1]	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		Union[str, Any]			=						model_class(config=__snake_case)
				for name, module in model.named_modules():
					if isinstance(__snake_case	,       (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 		A__					(				self):
			def check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case):
				_UpperCamelCase   :		str			=						model_class(__snake_case)
				model.to(__snake_case)
				model.eval()

				with torch.no_grad():
					_UpperCamelCase   :		Union[str, Any]			=						model(**self._prepare_for_class(__snake_case	,       __snake_case))

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

				_UpperCamelCase   :		str			=						self.model_tester.num_stages
				self.assertEqual(len(__snake_case)	,       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   :		List[str]			=						['preactivation', 'bottleneck']
			for model_class in self.all_model_classes:
				for layer_type in layers_type:
					_UpperCamelCase   :		Any			=						layer_type
					_UpperCamelCase   :		Tuple			=						True
					check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case)

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

					check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case)


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


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)






		@slow
		def 		A__					(				self):
			for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase   :		Optional[Any]			=						BitModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






def 					lowerCamelCase_     (							)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		List[Any]			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







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


		"""simple docstring"""




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






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__snake_case)

			_UpperCamelCase   :		str			=						self.default_image_processor
			_UpperCamelCase   :		List[str]			=						prepare_img()
			_UpperCamelCase   :		int			=						image_processor(images=__snake_case	,       return_tensors='pt').to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase   :		Any			=						model(**__snake_case)

			# verify the logits
			_UpperCamelCase   :		Dict			=						torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape	,       __snake_case)

			_UpperCamelCase   :		Optional[int]			=						torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]).to(__snake_case)

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







@require_torch
class 	lowercase    (				_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (BitBackbone,) if is_torch_available() else ()
		a__					       =  BitConfig

		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						BitModelTester(self)




