
'''simple docstring'''





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					:			List[str]			      =		logging.get_logger(__name__)

set_seed(770)


__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					:			str			      =		{
    "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					:			List[Any]			      =		os.path.dirname(os.path.abspath(__file__))
__lowerCAmelCase					:			Union[str, Any]			      =		os.path.join(os.path.expanduser("~"), ".cache")
__lowerCAmelCase					:			int			      =		os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase=False	) ->						Optional[Any]:
    __lowercase		:			str	    =       model_type
    if use_small:
        key += "_small"
    return os.path.join(__lowerCAmelCase	,					REMOTE_MODEL_PATHS[key]['''file_name''']	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						int:
    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"	) ->						int:
    if model_type == "text":
        __lowercase		:			str	    =       BarkSemanticModel
        __lowercase		:			Any	    =       BarkSemanticConfig
        __lowercase		:			Any	    =       BarkSemanticGenerationConfig
    elif model_type == "coarse":
        __lowercase		:			int	    =       BarkCoarseModel
        __lowercase		:			Tuple	    =       BarkCoarseConfig
        __lowercase		:			int	    =       BarkCoarseGenerationConfig
    elif model_type == "fine":
        __lowercase		:			List[Any]	    =       BarkFineModel
        __lowercase		:			List[Any]	    =       BarkFineConfig
        __lowercase		:			Any	    =       BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    __lowercase		:			Optional[int]	    =       F'{model_type}_small' if use_small else model_type
    __lowercase		:			List[str]	    =       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''']	)
    __lowercase		:			int	    =       torch.load(__lowerCAmelCase	,					map_location=__lowerCAmelCase	)
    # this is a hack
    __lowercase		:			str	    =       checkpoint['''model_args''']
    if "input_vocab_size" not in model_args:
        __lowercase		:			List[str]	    =       model_args['''vocab_size''']
        __lowercase		:			Any	    =       model_args['''vocab_size''']
        del model_args["vocab_size"]

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

    __lowercase		:			Optional[Any]	    =       ConfigClass(**checkpoint['''model_args''']	)
    __lowercase		:			Any	    =       ModelClass(config=__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       GenerationConfigClass()

    __lowercase		:			Tuple	    =       model_generation_config
    __lowercase		:			Any	    =       checkpoint['''model''']
    # fixup checkpoint
    __lowercase		:			Optional[int]	    =       '''_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
            __lowercase		:			List[str]	    =       k[len(__lowerCAmelCase	) :]
            for old_layer_name in new_layer_name_dict:
                __lowercase		:			List[Any]	    =       new_k.replace(__lowerCAmelCase	,					new_layer_name_dict[old_layer_name]	)

            __lowercase		:			Any	    =       state_dict.pop(__lowerCAmelCase	)

    __lowercase		:			str	    =       set(state_dict.keys()	) - set(model.state_dict().keys()	)
    __lowercase		:			Any	    =       {k for k in extra_keys if not k.endswith('''.attn.bias'''	)}
    __lowercase		:			int	    =       set(model.state_dict().keys()	) - set(state_dict.keys()	)
    __lowercase		:			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	)
    __lowercase		:			List[Any]	    =       model.num_parameters(exclude_embeddings=__lowerCAmelCase	)
    __lowercase		:			List[str]	    =       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"	) ->						Tuple:
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    __lowercase		:			List[str]	    =       '''cpu'''  # do conversion on cpu

    __lowercase		:			List[Any]	    =       _get_ckpt_path(__lowerCAmelCase	,					use_small=__lowerCAmelCase	)
    __lowercase		:			List[Any]	    =       _load_model(__lowerCAmelCase	,					__lowerCAmelCase	,					model_type=__lowerCAmelCase	,					use_small=__lowerCAmelCase	)

    # load bark initial model
    __lowercase		:			str	    =       _bark_load_model(__lowerCAmelCase	,					'''cpu'''	,					model_type=__lowerCAmelCase	,					use_small=__lowerCAmelCase	)

    if model_type == "text":
        __lowercase		:			List[Any]	    =       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
    __lowercase		:			str	    =       5
    __lowercase		:			Union[str, Any]	    =       10

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

        __lowercase		:			int	    =       model(__lowerCAmelCase	)

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

    else:
        __lowercase		:			Dict	    =       3
        __lowercase		:			Optional[Any]	    =       8
        __lowercase		:			Optional[int]	    =       torch.randint(256	,					(batch_size, sequence_length, n_codes_total)	,					dtype=torch.int	)

        __lowercase		:			Dict	    =       model(__lowerCAmelCase	,					__lowerCAmelCase	)
        __lowercase		:			int	    =       bark_model(__lowerCAmelCase	,					__lowerCAmelCase	)

        __lowercase		:			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	,					) ->						str:
    __lowercase		:			Optional[int]	    =       os.path.join(__lowerCAmelCase	,					__lowerCAmelCase	)

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

    __lowercase		:			Dict	    =       BarkSemanticModel.from_pretrained(__lowerCAmelCase	)
    __lowercase		:			Optional[Any]	    =       BarkCoarseModel.from_pretrained(__lowerCAmelCase	)
    __lowercase		:			Optional[Any]	    =       BarkFineModel.from_pretrained(__lowerCAmelCase	)
    __lowercase		:			Tuple	    =       EncodecModel.from_pretrained('''facebook/encodec_24khz'''	)

    __lowercase		:			Union[str, Any]	    =       BarkConfig.from_sub_model_configs(
        __lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)

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

    __lowercase		:			Dict	    =       BarkModel(__lowerCAmelCase	)

    __lowercase		:			Optional[int]	    =       semantic
    __lowercase		:			str	    =       coarseAcoustic
    __lowercase		:			Dict	    =       fineAcoustic
    __lowercase		:			Dict	    =       codec

    __lowercase		:			Optional[int]	    =       bark_generation_config

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


if __name__ == "__main__":
 __lowerCAmelCase					:			Tuple			      =		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					:			str			      =		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 huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import 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 transformers import UperNetForSemanticSegmentation
 from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       Dict				,       _snake_case		:       Optional[int]				,       _snake_case		:       List[Any]=13				,       _snake_case		:       int=32				,       _snake_case		:       int=3				,       _snake_case		:       Any=4				,       _snake_case		:       Optional[int]=[10, 20, 30, 40]				,       _snake_case		:       Optional[Any]=[2, 2, 3, 2]				,       _snake_case		:       Dict=True				,       _snake_case		:       List[Any]=True				,       _snake_case		:       int=37				,       _snake_case		:       Union[str, Any]="gelu"				,       _snake_case		:       Tuple=10				,       _snake_case		:       Tuple=0.02				,       _snake_case		:       List[str]=["stage2", "stage3", "stage4"]				,       _snake_case		:       Tuple=3				,       _snake_case		:       int=None				,       ):
          __lowercase		:			List[Any]	    =       parent
          __lowercase		:			Union[str, Any]	    =       batch_size
          __lowercase		:			Optional[Any]	    =       image_size
          __lowercase		:			Optional[Any]	    =       num_channels
          __lowercase		:			List[str]	    =       num_stages
          __lowercase		:			Union[str, Any]	    =       hidden_sizes
          __lowercase		:			Optional[Any]	    =       depths
          __lowercase		:			List[Any]	    =       is_training
          __lowercase		:			List[str]	    =       use_labels
          __lowercase		:			Tuple	    =       intermediate_size
          __lowercase		:			Union[str, Any]	    =       hidden_act
          __lowercase		:			Dict	    =       type_sequence_label_size
          __lowercase		:			Dict	    =       initializer_range
          __lowercase		:			str	    =       out_features
          __lowercase		:			Tuple	    =       num_labels
          __lowercase		:			Tuple	    =       scope
          __lowercase		:			Optional[Any]	    =       num_stages




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

          __lowercase		:			Union[str, Any]	    =       self.get_config()

          return config, pixel_values, labels




      def 		snake_case_			(      self		:       List[str]				):
          return ConvNextConfig(
              num_channels=self.num_channels				,       num_stages=self.num_stages				,       hidden_sizes=self.hidden_sizes				,       depths=self.depths				,       is_training=self.is_training				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       out_features=self.out_features				,       )




      def 		snake_case_			(      self		:       int				):
          return UperNetConfig(
              backbone_config=self.get_backbone_config()				,       hidden_size=512				,       pool_scales=[1, 2, 3, 6]				,       use_auxiliary_head=_snake_case				,       auxiliary_loss_weight=0.4				,       auxiliary_in_channels=40				,       auxiliary_channels=256				,       auxiliary_num_convs=1				,       auxiliary_concat_input=_snake_case				,       loss_ignore_index=255				,       num_labels=self.num_labels				,       )




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]				,       _snake_case		:       List[str]				):
          __lowercase		:			Optional[int]	    =       UperNetForSemanticSegmentation(config=_snake_case				)
          model.to(_snake_case				)
          model.eval()
          __lowercase		:			Optional[Any]	    =       model(_snake_case				)
          self.parent.assertEqual(
              result.logits.shape				,       (self.batch_size, self.num_labels, self.image_size, self.image_size)				)




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






@require_torch
class 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			List[str]        =    (UperNetForSemanticSegmentation,) if is_torch_available() else ()
      A__	:			Union[str, Any]        =    {'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
      A__	:			Union[str, Any]        =    False
      A__	:			Optional[Any]        =    False
      A__	:			int        =    False
      A__	:			Optional[int]        =    False
      A__	:			Optional[Any]        =    False
      A__	:			List[str]        =    False




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			str	    =       UperNetModelTester(self				)
          __lowercase		:			List[Any]	    =       ConfigTester(self				,       config_class=_snake_case				,       has_text_modality=_snake_case				,       hidden_size=37				)




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




      def 		snake_case_			(      self		:       str				):
          return




      def 		snake_case_			(      self		:       str				):
          __lowercase    ,      __lowercase		:			Optional[Any]	    =       self.model_tester.prepare_config_and_inputs_for_common()

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

              __lowercase		:			Dict	    =       ['''pixel_values''']
              self.assertListEqual(arg_names[:1]				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			int	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_snake_case				)




      @unittest.skip(reason='''UperNet does not use inputs_embeds'''				)
      def 		snake_case_			(      self		:       List[Any]				):
          pass




      @unittest.skip(reason='''UperNet does not support input and output embeddings'''				)
      def 		snake_case_			(      self		:       List[Any]				):
          pass




      @unittest.skip(reason='''UperNet does not have a base model'''				)
      def 		snake_case_			(      self		:       str				):
          pass




      @unittest.skip(reason='''UperNet does not have a base model'''				)
      def 		snake_case_			(      self		:       int				):
          pass




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




      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
      def 		snake_case_			(      self		:       List[str]				):
          pass




      def 		snake_case_			(      self		:       List[Any]				):
          def check_hidden_states_output(_snake_case		:       int				,       _snake_case		:       Optional[int]				,       _snake_case		:       Dict				):
              __lowercase		:			Dict	    =       model_class(_snake_case				)
              model.to(_snake_case				)
              model.eval()

              with torch.no_grad():
                  __lowercase		:			Optional[int]	    =       model(**self._prepare_for_class(_snake_case				,       _snake_case				)				)

              __lowercase		:			Tuple	    =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

              __lowercase		:			Tuple	    =       self.model_tester.num_stages
              self.assertEqual(len(_snake_case				)				,       expected_num_stages + 1				)

              # ConvNext'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]				,       )

          __lowercase    ,      __lowercase		:			str	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase		:			int	    =       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"]
              __lowercase		:			Any	    =       True

              check_hidden_states_output(_snake_case				,       _snake_case				,       _snake_case				)




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

          __lowercase		:			Dict	    =       _config_zero_init(_snake_case				)
          __lowercase		:			Dict	    =       _config_zero_init(configs_no_init.backbone_config				)
          for model_class in self.all_model_classes:
              __lowercase		:			Union[str, Any]	    =       model_class(config=_snake_case				)
              for name, param in model.named_parameters():
                  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'				,       )




      @unittest.skip(reason='''UperNet does not have tied weights'''				)
      def 		snake_case_			(      self		:       str				):
          pass




      @slow
      def 		snake_case_			(      self		:       Dict				):
          for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase		:			List[Any]	    =       UperNetForSemanticSegmentation.from_pretrained(_snake_case				)
              self.assertIsNotNone(_snake_case				)






def     UpperCAmelCase_  (						) ->						Optional[int]:
    __lowercase		:			Optional[int]	    =       hf_hub_download(
        repo_id='''hf-internal-testing/fixtures_ade20k'''	,					repo_type='''dataset'''	,					filename='''ADE_val_00000001.jpg'''	)
    __lowercase		:			int	    =       Image.open(__lowerCAmelCase	).convert('''RGB'''	)
    return image





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



      """simple docstring"""







      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			List[Any]	    =       AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''				)
          __lowercase		:			Optional[Any]	    =       UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''				).to(_snake_case				)

          __lowercase		:			Tuple	    =       prepare_img()
          __lowercase		:			Optional[int]	    =       processor(images=_snake_case				,       return_tensors='''pt'''				).to(_snake_case				)

          with torch.no_grad():
              __lowercase		:			Dict	    =       model(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       torch.Size((1, model.config.num_labels, 512, 512)				)
          self.assertEqual(outputs.logits.shape				,       _snake_case				)

          __lowercase		:			List[Any]	    =       torch.tensor(
              [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]				).to(_snake_case				)
          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,       _snake_case				,       atol=1E-4				)				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[str]	    =       AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''				)
          __lowercase		:			Dict	    =       UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''				).to(_snake_case				)

          __lowercase		:			Any	    =       prepare_img()
          __lowercase		:			Union[str, Any]	    =       processor(images=_snake_case				,       return_tensors='''pt'''				).to(_snake_case				)

          with torch.no_grad():
              __lowercase		:			Tuple	    =       model(**_snake_case				)

          __lowercase		:			Tuple	    =       torch.Size((1, model.config.num_labels, 512, 512)				)
          self.assertEqual(outputs.logits.shape				,       _snake_case				)

          __lowercase		:			List[Any]	    =       torch.tensor(
              [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]				).to(_snake_case				)
          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,       _snake_case				,       atol=1E-4				)				)


