



import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor





class  __magic_name__   (							A__						):






  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     self.config_class(**self.inputs_dict      )
        self.parent.assertTrue(hasattr(__A				,		"tf_padding"      )      )
        self.parent.assertTrue(hasattr(__A				,		"depth_multiplier"      )      )





class  __magic_name__   :






  def __init__(						self      :     Any				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     Optional[int]=13				,		UpperCamelCase__      :     Optional[Any]=3				,		UpperCamelCase__      :     Optional[Any]=32				,		UpperCamelCase__      :     Optional[Any]=0.25				,		UpperCamelCase__      :     Optional[Any]=8				,		UpperCamelCase__      :     Dict=8				,		UpperCamelCase__      :     str=6				,		UpperCamelCase__      :     Union[str, Any]=32				,		UpperCamelCase__      :     Any=True				,		UpperCamelCase__      :     int=True				,		UpperCamelCase__      :     List[Any]=True				,		UpperCamelCase__      :     Union[str, Any]="relu6"				,		UpperCamelCase__      :     Dict=12_80				,		UpperCamelCase__      :     Dict=0.1				,		UpperCamelCase__      :     List[str]=0.02				,		UpperCamelCase__      :     List[str]=True				,		UpperCamelCase__      :     List[Any]=True				,		UpperCamelCase__      :     Optional[Any]=10				,		UpperCamelCase__      :     Union[str, Any]=None				,		)				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     parent
        UpperCAmelCase         =     batch_size
        UpperCAmelCase         =     num_channels
        UpperCAmelCase         =     image_size
        UpperCAmelCase         =     depth_multiplier
        UpperCAmelCase         =     depth_divisible_by
        UpperCAmelCase         =     min_depth
        UpperCAmelCase         =     expand_ratio
        UpperCAmelCase         =     tf_padding
        UpperCAmelCase         =     output_stride
        UpperCAmelCase         =     first_layer_is_expansion
        UpperCAmelCase         =     finegrained_output
        UpperCAmelCase         =     hidden_act
        UpperCAmelCase         =     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier      )
        UpperCAmelCase         =     classifier_dropout_prob
        UpperCAmelCase         =     use_labels
        UpperCAmelCase         =     is_training
        UpperCAmelCase         =     num_labels
        UpperCAmelCase         =     initializer_range
        UpperCAmelCase         =     scope






  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					str:
        '''simple docstring'''







        UpperCAmelCase         =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        UpperCAmelCase         =     None
        UpperCAmelCase         =     None
        if self.use_labels:
              UpperCAmelCase         =     ids_tensor([self.batch_size]				,		self.num_labels      )
              UpperCAmelCase         =     ids_tensor([self.batch_size, self.image_size, self.image_size]				,		self.num_labels      )

        UpperCAmelCase         =     self.get_config()

        return config, pixel_values, labels, pixel_labels






  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[Any]:
        '''simple docstring'''







        return MobileNetVaConfig(
            num_channels=self.num_channels				,		image_size=self.image_size				,		depth_multiplier=self.depth_multiplier				,		depth_divisible_by=self.depth_divisible_by				,		min_depth=self.min_depth				,		expand_ratio=self.expand_ratio				,		output_stride=self.output_stride				,		first_layer_is_expansion=self.first_layer_is_expansion				,		finegrained_output=self.finegrained_output				,		hidden_act=self.hidden_act				,		tf_padding=self.tf_padding				,		classifier_dropout_prob=self.classifier_dropout_prob				,		initializer_range=self.initializer_range				,		)






  def SCREAMING_SNAKE_CASE_			(						self      :     List[str]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[Any]				,		UpperCamelCase__      :     int				,		UpperCamelCase__      :     Optional[int]      )				->					Optional[Any]:
        '''simple docstring'''







        UpperCAmelCase         =     MobileNetVaModel(config=__A      )
        model.to(__A      )
        model.eval()
        UpperCAmelCase         =     model(__A      )
        self.parent.assertEqual(
            result.last_hidden_state.shape				,		(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )				,		)
        self.parent.assertEqual(
            result.pooler_output.shape				,		(self.batch_size, self.last_hidden_size)				,		)






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Union[str, Any]				,		UpperCamelCase__      :     List[str]      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     self.num_labels
        UpperCAmelCase         =     MobileNetVaForImageClassification(__A      )
        model.to(__A      )
        model.eval()
        UpperCAmelCase         =     model(__A				,		labels=__A      )
        self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Dict				,		UpperCamelCase__      :     List[Any]      )				->					Optional[Any]:
        '''simple docstring'''







        UpperCAmelCase         =     self.num_labels
        UpperCAmelCase         =     MobileNetVaForSemanticSegmentation(__A      )
        model.to(__A      )
        model.eval()
        UpperCAmelCase         =     model(__A      )
        self.parent.assertEqual(
            result.logits.shape				,		(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )				,		)
        UpperCAmelCase         =     model(__A				,		labels=__A      )
        self.parent.assertEqual(
            result.logits.shape				,		(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )				,		)







  def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[Any]:
        '''simple docstring'''







        UpperCAmelCase         =     self.prepare_config_and_inputs()
        UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     config_and_inputs
        UpperCAmelCase         =     {"pixel_values": pixel_values}
        return config, inputs_dict





@require_torch
class  __magic_name__   (							A__,  A__,  unittest.TestCase						):
  lowercase      :       Optional[Any]          =(
      (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
      if is_torch_available()
      else ()
  )
  lowercase      :       Dict          =(
      {
          '''feature-extraction''': MobileNetVaModel,
          '''image-classification''': MobileNetVaForImageClassification,
          '''image-segmentation''': MobileNetVaForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase      :       Tuple          =False
  lowercase      :       Any          =False
  lowercase      :       Tuple          =False
  lowercase      :       Dict          =False






  def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
        '''simple docstring'''







        UpperCAmelCase         =     MobileNetVaModelTester(self      )
        UpperCAmelCase         =     MobileNetVaConfigTester(self				,		config_class=__A				,		has_text_modality=__A      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Optional[Any]:
        '''simple docstring'''







        self.config_tester.run_common_tests()






  @unittest.skip(reason="MobileNetV2 does not use inputs_embeds"      )
  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					List[Any]:
        '''simple docstring'''







        pass






  @unittest.skip(reason="MobileNetV2 does not support input and output embeddings"      )
  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					str:
        '''simple docstring'''







        pass






  @unittest.skip(reason="MobileNetV2 does not output attentions"      )
  def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					int:
        '''simple docstring'''







        pass






  def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Union[str, Any]:
        '''simple docstring'''







        UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase         =     ["pixel_values"]
              self.assertListEqual(arg_names[:1]				,		__A      )






  def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					str:
        '''simple docstring'''







        UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A      )






  def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Optional[Any]:
        '''simple docstring'''







        def check_hidden_states_output(UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Optional[Any]      ):
              UpperCAmelCase         =     model_class(__A      )
              model.to(__A      )
              model.eval()

              with torch.no_grad():
                    UpperCAmelCase         =     model(**self._prepare_for_class(__A				,		__A      )      )

              UpperCAmelCase         =     outputs.hidden_states

              UpperCAmelCase         =     16
              self.assertEqual(len(__A      )				,		__A      )

        UpperCAmelCase       ,		UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCAmelCase         =     True
              check_hidden_states_output(__A				,		__A				,		__A      )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase         =     True

              check_hidden_states_output(__A				,		__A				,		__A      )






  def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Dict:
        '''simple docstring'''







        UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A      )






  def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__A      )







  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Optional[int]:
        '''simple docstring'''







        for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase         =     MobileNetVaModel.from_pretrained(__A      )
              self.assertIsNotNone(__A      )

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





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






  @cached_property
  def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Any:
        '''simple docstring'''







        return (
            MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"      ) if is_vision_available() else None
        )






  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Union[str, Any]:
        '''simple docstring'''







        UpperCAmelCase         =     MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"      ).to(__A      )

        UpperCAmelCase         =     self.default_image_processor
        UpperCAmelCase         =     prepare_img()
        UpperCAmelCase         =     image_processor(images=__A				,		return_tensors="pt"      ).to(__A      )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase         =     model(**__A      )

        # verify the logits
        UpperCAmelCase         =     torch.Size((1, 10_01)      )
        self.assertEqual(outputs.logits.shape				,		__A      )

        UpperCAmelCase         =     torch.tensor([0.24_45, -1.19_93, 0.19_05]      ).to(__A      )

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







  @slow
  def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Tuple:
        '''simple docstring'''







        UpperCAmelCase         =     MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"      )
        UpperCAmelCase         =     model.to(__A      )

        UpperCAmelCase         =     MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"      )

        UpperCAmelCase         =     prepare_img()
        UpperCAmelCase         =     image_processor(images=__A				,		return_tensors="pt"      ).to(__A      )

        # forward pass
        with torch.no_grad():
              UpperCAmelCase         =     model(**__A      )
        UpperCAmelCase         =     outputs.logits

        # verify the logits
        UpperCAmelCase         =     torch.Size((1, 21, 65, 65)      )
        self.assertEqual(logits.shape				,		__A      )

        UpperCAmelCase         =     torch.tensor(
            [
                [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
            ]				,		device=__A				,		)

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








import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase     :							Dict  							=				logging.get_logger(__name__)


__lowerCamelCase     :							Tuple  							=				["model.decoder.embed_positions.weights"]

def    lowerCamelCase_(lowerCamelCase_    ) ->       Any:
						if "emb" in name:
												UpperCAmelCase         =     name.replace("emb"    ,   "model.decoder.embed_tokens"    )
						if "transformer" in name:
												UpperCAmelCase         =     name.replace("transformer"    ,   "model.decoder"    )
						if "cross_attention" in name:
												UpperCAmelCase         =     name.replace("cross_attention"    ,   "encoder_attn"    )
						if "linear1" in name:
												UpperCAmelCase         =     name.replace("linear1"    ,   "fc1"    )
						if "linear2" in name:
												UpperCAmelCase         =     name.replace("linear2"    ,   "fc2"    )
						if "norm1" in name:
												UpperCAmelCase         =     name.replace("norm1"    ,   "self_attn_layer_norm"    )
						if "norm_cross" in name:
												UpperCAmelCase         =     name.replace("norm_cross"    ,   "encoder_attn_layer_norm"    )
						if "norm2" in name:
												UpperCAmelCase         =     name.replace("norm2"    ,   "final_layer_norm"    )
						if "out_norm" in name:
												UpperCAmelCase         =     name.replace("out_norm"    ,   "model.decoder.layer_norm"    )
						if "linears" in name:
												UpperCAmelCase         =     name.replace("linears"    ,   "lm_heads"    )
						if "condition_provider.conditioners.description.output_proj" in name:
												UpperCAmelCase         =     name.replace("condition_provider.conditioners.description.output_proj"    ,   "enc_to_dec_proj"    )
						return name

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ) ->       Tuple[Dict, Dict]:
						UpperCAmelCase         =     list(state_dict.keys()    )
						UpperCAmelCase         =     {}
						for key in keys:
												UpperCAmelCase         =     state_dict.pop(lowerCamelCase_    )
												UpperCAmelCase         =     rename_keys(lowerCamelCase_    )
												if "in_proj_weight" in key:
																		# split fused qkv proj
																		UpperCAmelCase         =     val[:hidden_size, :]
																		UpperCAmelCase         =     val[hidden_size : 2 * hidden_size, :]
																		UpperCAmelCase         =     val[-hidden_size:, :]
												elif "enc_to_dec_proj" in key:
																		UpperCAmelCase         =     val
												else:
																		UpperCAmelCase         =     val
						return state_dict, enc_dec_proj_state_dict

def    lowerCamelCase_(lowerCamelCase_    ) ->       MusicgenDecoderConfig:
						if checkpoint == "small":
												# default config values
												UpperCAmelCase         =     1_024
												UpperCAmelCase         =     24
												UpperCAmelCase         =     16
						elif checkpoint == "medium":
												UpperCAmelCase         =     1_536
												UpperCAmelCase         =     48
												UpperCAmelCase         =     24
						elif checkpoint == "large":
												UpperCAmelCase         =     2_048
												UpperCAmelCase         =     48
												UpperCAmelCase         =     32
						else:
												raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'    )
						UpperCAmelCase         =     MusicgenDecoderConfig(
						    hidden_size=lowerCamelCase_    ,   ffn_dim=hidden_size * 4    ,   num_hidden_layers=lowerCamelCase_    ,   num_attention_heads=lowerCamelCase_    ,   )
						return config

@torch.no_grad()
def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_=None    ,   lowerCamelCase_=None    ,   lowerCamelCase_="cpu"    ) ->       List[str]:
						UpperCAmelCase         =     MusicGen.get_pretrained(lowerCamelCase_    ,   device=lowerCamelCase_    )
						UpperCAmelCase         =     decoder_config_from_checkpoint(lowerCamelCase_    )

						UpperCAmelCase         =     fairseq_model.lm.state_dict()
						UpperCAmelCase       ,		UpperCAmelCase         =     rename_state_dict(
						    lowerCamelCase_    ,   hidden_size=decoder_config.hidden_size    )

						UpperCAmelCase         =     TaEncoderModel.from_pretrained("t5-base"    )
						UpperCAmelCase         =     EncodecModel.from_pretrained("facebook/encodec_32khz"    )
						UpperCAmelCase         =     MusicgenForCausalLM(lowerCamelCase_    ).eval()

						# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
						UpperCAmelCase       ,		UpperCAmelCase         =     decoder.load_state_dict(lowerCamelCase_    ,   strict=lowerCamelCase_    )

						for key in missing_keys.copy():
												if key.startswith(("text_encoder", "audio_encoder")    ) or key in EXPECTED_MISSING_KEYS:
																		missing_keys.remove(lowerCamelCase_    )

						if len(lowerCamelCase_    ) > 0:
												raise ValueError(F'Missing key(s) in state_dict: {missing_keys}'    )

						if len(lowerCamelCase_    ) > 0:
												raise ValueError(F'Unexpected key(s) in state_dict: {unexpected_keys}'    )

						# init the composite model
						UpperCAmelCase         =     MusicgenForConditionalGeneration(text_encoder=lowerCamelCase_    ,   audio_encoder=lowerCamelCase_    ,   decoder=lowerCamelCase_    )

						# load the pre-trained enc-dec projection (from the decoder state dict)
						model.enc_to_dec_proj.load_state_dict(lowerCamelCase_    )

						# check we can do a forward pass
						UpperCAmelCase         =     torch.arange(0    ,   8    ,   dtype=torch.long    ).reshape(2    ,   -1    )
						UpperCAmelCase         =     input_ids.reshape(2 * 4    ,   -1    )

						with torch.no_grad():
												UpperCAmelCase         =     model(input_ids=lowerCamelCase_    ,   decoder_input_ids=lowerCamelCase_    ).logits

						if logits.shape != (8, 1, 2_048):
												raise ValueError("Incorrect shape for logits"    )

						# now construct the processor
						UpperCAmelCase         =     AutoTokenizer.from_pretrained("t5-base"    )
						UpperCAmelCase         =     AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"    ,   padding_side="left"    )

						UpperCAmelCase         =     MusicgenProcessor(feature_extractor=lowerCamelCase_    ,   tokenizer=lowerCamelCase_    )

						# set the appropriate bos/pad token ids
						UpperCAmelCase         =     2_048
						UpperCAmelCase         =     2_048

						# set other default generation config params
						UpperCAmelCase         =     int(30 * audio_encoder.config.frame_rate    )
						UpperCAmelCase         =     True
						UpperCAmelCase         =     3.0

						if pytorch_dump_folder is not None:
												Path(lowerCamelCase_    ).mkdir(exist_ok=lowerCamelCase_    )
												logger.info(F'Saving model {checkpoint} to {pytorch_dump_folder}'    )
												model.save_pretrained(lowerCamelCase_    )
												processor.save_pretrained(lowerCamelCase_    )

						if repo_id:
												logger.info(F'Pushing model {checkpoint} to {repo_id}'    )
												model.push_to_hub(lowerCamelCase_    )
												processor.push_to_hub(lowerCamelCase_    )


if __name__ == "__main__":
		__lowerCamelCase     :							List[Any]  							=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint",
		    default="small",
		    type=str,
		    help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder",
		    required=True,
		    default=None,
		    type=str,
		    help="Path to the output PyTorch model directory.",
		)
		parser.add_argument(
		    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
		)
		parser.add_argument(
		    "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
		)

		__lowerCamelCase     :							Optional[int]  							=				parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



