


'''simple docstring'''




from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor

class 		UpperCAmelCase_      :





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      str=1_3    ,      UpperCAmelCase__       :      Dict=3_0    ,      UpperCAmelCase__       :      Optional[Any]=2    ,      UpperCAmelCase__       :      Dict=3    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      Dict=True    ,      UpperCAmelCase__       :      int=3_2    ,      UpperCAmelCase__       :      Optional[Any]=2    ,      UpperCAmelCase__       :      Dict=4    ,      UpperCAmelCase__       :      List[Any]=3_7    ,      UpperCAmelCase__       :      Any="gelu"    ,      UpperCAmelCase__       :      Union[str, Any]=0.1    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      UpperCAmelCase__       :      Optional[Any]=1_0    ,      UpperCAmelCase__       :      Any=0.02    ,      UpperCAmelCase__       :      Union[str, Any]=3    ,      UpperCAmelCase__       :      Optional[Any]=0.6    ,      UpperCAmelCase__       :      str=None    ,      )				->  Any:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			image_size
        lowerCAmelCase     =			patch_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			mask_ratio
        lowerCAmelCase     =			scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        lowerCAmelCase     =			(image_size // patch_size) ** 2
        lowerCAmelCase     =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Tuple:
        lowerCAmelCase     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        lowerCAmelCase     =			None
        if self.use_labels:
           lowerCAmelCase     =			ids_tensor([self.batch_size]    ,      self.type_sequence_label_size    )

        lowerCAmelCase     =			self.get_config()

        return config, pixel_values, labels





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
        return ViTMAEConfig(
            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    ,      decoder_hidden_size=self.hidden_size    ,      decoder_num_hidden_layers=self.num_hidden_layers    ,      decoder_num_attention_heads=self.num_attention_heads    ,      decoder_intermediate_size=self.intermediate_size    ,      hidden_act=self.hidden_act    ,      hidden_dropout_prob=self.hidden_dropout_prob    ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,      is_decoder=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      mask_ratio=self.mask_ratio    ,      )





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Any    )				->  Tuple:
        lowerCAmelCase     =			TFViTMAEModel(config=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      training=UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      str    )				->  List[str]:
        lowerCAmelCase     =			TFViTMAEForPreTraining(UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      training=UpperCAmelCase__    )
        # expected sequence length = num_patches
        lowerCAmelCase     =			(self.image_size // self.patch_size) ** 2
        lowerCAmelCase     =			self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, num_patches, expected_num_channels)    )

        # test greyscale images
        lowerCAmelCase     =			1
        lowerCAmelCase     =			TFViTMAEForPreTraining(UpperCAmelCase__    )

        lowerCAmelCase     =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      training=UpperCAmelCase__    )
        lowerCAmelCase     =			self.patch_size**2
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, num_patches, expected_num_channels)    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        ((lowerCAmelCase)     ,  (lowerCAmelCase)     ,  (lowerCAmelCase))     =			config_and_inputs
        lowerCAmelCase     =			{'pixel_values': pixel_values}
        return config, inputs_dict




@require_tf
class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	str		=     (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     lowerCamelCase				:	List[str]		=     {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

     lowerCamelCase				:	List[str]		=     False
     lowerCamelCase				:	Optional[int]		=     False
     lowerCamelCase				:	Optional[int]		=     False
     lowerCamelCase				:	str		=     False





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        lowerCAmelCase     =			TFViTMAEModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      has_text_modality=UpperCAmelCase__    ,      hidden_size=3_7    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  str:
        self.config_tester.run_common_tests()





     @unittest.skip(reason='ViTMAE does not use inputs_embeds'    )
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        pass





     def 							__UpperCAmelCase      ( self       :      int    )				->  Dict:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           self.assertIsInstance(model.get_input_embeddings()    ,      (tf.keras.layers.Layer)    )
           lowerCAmelCase     =			model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCAmelCase__    ,      tf.keras.layers.Layer    )    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

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

           lowerCAmelCase     =			['pixel_values']
           self.assertListEqual(arg_names[:1]    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  int:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  List[Any]:
        # make the mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

           lowerCAmelCase     =			copy.deepcopy(self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )    )
           lowerCAmelCase     =			model(**UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           lowerCAmelCase     =			outputs_dict[0].numpy()
           lowerCAmelCase     =			outputs_keywords[0].numpy()

           self.assertLess(np.sum(np.abs(output_dict - output_keywords    )    )    ,      1E-6    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Optional[int]:
        # make the mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        def prepare_numpy_arrays(UpperCAmelCase__       :      Optional[int]    ):
           lowerCAmelCase     =			{}
           for k, v in inputs_dict.items():
              if tf.is_tensor(UpperCAmelCase__    ):
                 lowerCAmelCase     =			v.numpy()
              else:
                 lowerCAmelCase     =			np.array(UpperCAmelCase__    )

           return inputs_np_dict

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )

           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			prepare_numpy_arrays(UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           lowerCAmelCase     =			model(**UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           self.assert_outputs_same(UpperCAmelCase__    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Tuple:
        # make masks reproducible
        np.random.seed(2    )

        lowerCAmelCase     =			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
        lowerCAmelCase     =			tf.constant(UpperCAmelCase__    )

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        lowerCAmelCase     =			tf_noise

        super().check_pt_tf_models(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Tuple:
        # make mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			{
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__    ),)
            for module_member_name in dir(UpperCAmelCase__    )
            if module_member_name.endswith('MainLayer'    )
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len('MainLayer'    )] == model_class.__name__[: -len('Model'    )]
            for module_member in (getattr(UpperCAmelCase__    ,      UpperCAmelCase__    ),)
            if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(UpperCAmelCase__    ,      '_keras_serializable'    ,      UpperCAmelCase__    )
        }

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
        lowerCAmelCase     =			tf.convert_to_tensor(UpperCAmelCase__    )
        inputs_dict.update({'noise': noise}    )

        for main_layer_class in tf_main_layer_classes:
           lowerCAmelCase     =			main_layer_class(UpperCAmelCase__    )

           lowerCAmelCase     =			{
               name: tf.keras.Input(tensor.shape[1:]    ,      dtype=tensor.dtype    ) for name, tensor in inputs_dict.items()
           }

           lowerCAmelCase     =			tf.keras.Model(UpperCAmelCase__    ,      outputs=main_layer(UpperCAmelCase__    )    )
           lowerCAmelCase     =			model(UpperCAmelCase__    )

           with tempfile.TemporaryDirectory() as tmpdirname:
              lowerCAmelCase     =			os.path.join(UpperCAmelCase__    ,      'keras_model.h5'    )
              model.save(UpperCAmelCase__    )
              lowerCAmelCase     =			tf.keras.models.load_model(
                  UpperCAmelCase__    ,      custom_objects={main_layer_class.__name__: main_layer_class}    )
              assert isinstance(UpperCAmelCase__    ,      tf.keras.Model    )
              lowerCAmelCase     =			model(UpperCAmelCase__    )
              self.assert_outputs_same(UpperCAmelCase__    ,      UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  List[str]:
        # make mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

           if model_class.__name__ == "TFViTMAEModel":
              lowerCAmelCase     =			outputs.last_hidden_state.numpy()
              lowerCAmelCase     =			0
           else:
              lowerCAmelCase     =			outputs.logits.numpy()
              lowerCAmelCase     =			0

           with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(UpperCAmelCase__    ,      saved_model=UpperCAmelCase__    )
              lowerCAmelCase     =			model_class.from_pretrained(UpperCAmelCase__    )
              lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

              if model_class.__name__ == "TFViTMAEModel":
                 lowerCAmelCase     =			after_outputs['last_hidden_state'].numpy()
                 lowerCAmelCase     =			0
              else:
                 lowerCAmelCase     =			after_outputs['logits'].numpy()
                 lowerCAmelCase     =			0

              lowerCAmelCase     =			np.amax(np.abs(out_a - out_a    )    )
              self.assertLessEqual(UpperCAmelCase__    ,      1E-5    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Union[str, Any]:
        # make mask reproducible
        np.random.seed(2    )

        lowerCAmelCase     ,  lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase     =			int((config.image_size // config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

        for model_class in self.all_model_classes:
           lowerCAmelCase     =			model_class(UpperCAmelCase__    )
           lowerCAmelCase     =			self._prepare_for_class(UpperCAmelCase__    ,      UpperCAmelCase__    )

           lowerCAmelCase     =			model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )
           lowerCAmelCase     =			model.get_config()
           # make sure that returned config is jsonifiable, which is required by keras
           json.dumps(UpperCAmelCase__    )
           lowerCAmelCase     =			model_class.from_config(model.get_config()    )
           # make sure it also accepts a normal config
           lowerCAmelCase     =			model_class.from_config(model.config    )
           lowerCAmelCase     =			new_model(UpperCAmelCase__    )  # Build model
           new_model.set_weights(model.get_weights()    )
           lowerCAmelCase     =			new_model(UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

           self.assert_outputs_same(UpperCAmelCase__    ,      UpperCAmelCase__    )





     @unittest.skip(
         reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.'    )
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Tuple:
        pass





     @unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'    )
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Union[str, Any]:
        pass





     @slow
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Union[str, Any]:
        lowerCAmelCase     =			TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'    )
        self.assertIsNotNone(UpperCAmelCase__    )





def 	a_					( ):
   lowerCAmelCase     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
   return image




@require_tf
@require_vision
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @cached_property
     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Tuple:
        return ViTImageProcessor.from_pretrained('facebook/vit-mae-base'    ) if is_vision_available() else None





     @slow
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  List[str]:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2    )

        lowerCAmelCase     =			TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'    )

        lowerCAmelCase     =			self.default_image_processor
        lowerCAmelCase     =			prepare_img()
        lowerCAmelCase     =			image_processor(images=UpperCAmelCase__    ,      return_tensors='tf'    )

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        lowerCAmelCase     =			ViTMAEConfig()
        lowerCAmelCase     =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
        lowerCAmelCase     =			np.random.uniform(size=(1, num_patches)    )

        # forward pass
        lowerCAmelCase     =			model(**UpperCAmelCase__    ,      noise=UpperCAmelCase__    )

        # verify the logits
        lowerCAmelCase     =			tf.convert_to_tensor([1, 1_9_6, 7_6_8]    )
        self.assertEqual(outputs.logits.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			tf.convert_to_tensor(
            [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]    )

        tf.debugging.assert_near(outputs.logits[0, :3, :3]    ,      UpperCAmelCase__    ,      atol=1E-4    )









'''simple docstring'''




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__snake_case    						=logging.get_logger("""transformers.models.encodec""")

__snake_case    						={
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
__snake_case    						={
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
__snake_case    						={
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
__snake_case    						={
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
__snake_case    						={
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
__snake_case    						={
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case    						={
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case    						=[]
__snake_case    						=[]





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     int	, lowerCamelCase	:     Any	, lowerCamelCase	:     List[str]				):
   for attribute in key.split('.'				):
      lowerCAmelCase     =			getattr(lowerCamelCase	, lowerCamelCase				)

   if weight_type is not None:
      lowerCAmelCase     =			getattr(lowerCamelCase	, lowerCamelCase				).shape
   else:
      lowerCAmelCase     =			hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
          f''' {value.shape} for {full_name}'''				)

   if weight_type == "weight":
      lowerCAmelCase     =			value
   elif weight_type == "weight_g":
      lowerCAmelCase     =			value
   elif weight_type == "weight_v":
      lowerCAmelCase     =			value
   elif weight_type == "bias":
      lowerCAmelCase     =			value
   elif weight_type == "running_mean":
      lowerCAmelCase     =			value
   elif weight_type == "running_var":
      lowerCAmelCase     =			value
   elif weight_type == "num_batches_tracked":
      lowerCAmelCase     =			value
   elif weight_type == "weight_ih_l0":
      lowerCAmelCase     =			value
   elif weight_type == "weight_hh_l0":
      lowerCAmelCase     =			value
   elif weight_type == "bias_ih_l0":
      lowerCAmelCase     =			value
   elif weight_type == "bias_hh_l0":
      lowerCAmelCase     =			value
   elif weight_type == "weight_ih_l1":
      lowerCAmelCase     =			value
   elif weight_type == "weight_hh_l1":
      lowerCAmelCase     =			value
   elif weight_type == "bias_ih_l1":
      lowerCAmelCase     =			value
   elif weight_type == "bias_hh_l1":
      lowerCAmelCase     =			value
   else:
      lowerCAmelCase     =			value

   logger.info(f'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''				)





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Optional[Any]				):
   for key in ignore_keys:
      if key.endswith('.*'				):
         if name.startswith(key[:-1]				):
            return True
      elif ".*." in key:
         lowerCAmelCase     ,  lowerCAmelCase     =			key.split('.*.'				)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Any	, lowerCamelCase	:     str				):
   lowerCAmelCase     =			[]

   if model_name == "encodec_24khz" or "encodec_32khz":
      lowerCAmelCase     =			MAPPING_24K
   elif model_name == "encodec_48khz":
      lowerCAmelCase     =			MAPPING_48K
   else:
      raise ValueError(f'''Unsupported model: {model_name}'''				)

   for name, value in orig_dict.items():
      if should_ignore(lowerCamelCase	, lowerCamelCase				):
         logger.info(f'''{name} was ignored'''				)
         continue

      lowerCAmelCase     =			False
      for key, mapped_key in MAPPING.items():
         if "*" in key:
            lowerCAmelCase     ,  lowerCAmelCase     =			key.split('.*.'				)
            if prefix in name and suffix in name:
               lowerCAmelCase     =			suffix

         if key in name:
            # HACK otherwise .embed gets initialized with .embed_avg too
            if key.endswith('embed'				) and name.endswith('embed_avg'				):
               continue

            lowerCAmelCase     =			True
            if "*" in mapped_key:
               lowerCAmelCase     =			name.split(lowerCamelCase				)[0].split('.'				)[-2]
               lowerCAmelCase     =			mapped_key.replace('*'	, lowerCamelCase				)
            if "weight_g" in name:
               lowerCAmelCase     =			'weight_g'
            elif "weight_v" in name:
               lowerCAmelCase     =			'weight_v'
            elif "weight_ih_l0" in name:
               lowerCAmelCase     =			'weight_ih_l0'
            elif "weight_hh_l0" in name:
               lowerCAmelCase     =			'weight_hh_l0'
            elif "bias_ih_l0" in name:
               lowerCAmelCase     =			'bias_ih_l0'
            elif "bias_hh_l0" in name:
               lowerCAmelCase     =			'bias_hh_l0'
            elif "weight_ih_l1" in name:
               lowerCAmelCase     =			'weight_ih_l1'
            elif "weight_hh_l1" in name:
               lowerCAmelCase     =			'weight_hh_l1'
            elif "bias_ih_l1" in name:
               lowerCAmelCase     =			'bias_ih_l1'
            elif "bias_hh_l1" in name:
               lowerCAmelCase     =			'bias_hh_l1'
            elif "bias" in name:
               lowerCAmelCase     =			'bias'
            elif "weight" in name:
               lowerCAmelCase     =			'weight'
            elif "running_mean" in name:
               lowerCAmelCase     =			'running_mean'
            elif "running_var" in name:
               lowerCAmelCase     =			'running_var'
            elif "num_batches_tracked" in name:
               lowerCAmelCase     =			'num_batches_tracked'
            else:
               lowerCAmelCase     =			None
            set_recursively(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
         continue
      if not is_used:
         unused_weights.append(lowerCamelCase				)

   logger.warning(f'''Unused weights: {unused_weights}'''				)





@torch.no_grad()
def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     str	, lowerCamelCase	:     Dict=None	, lowerCamelCase	:     Union[str, Any]=None	, ):
   if config_path is not None:
      lowerCAmelCase     =			EncodecConfig.from_pretrained(lowerCamelCase				)
   else:
      lowerCAmelCase     =			EncodecConfig()

   if model_name == "encodec_24khz":
      pass  # config is already correct
   elif model_name == "encodec_32khz":
      lowerCAmelCase     =			[8, 5, 4, 4]
      lowerCAmelCase     =			[2.2]
      lowerCAmelCase     =			64
      lowerCAmelCase     =			32000
      lowerCAmelCase     =			2048
      lowerCAmelCase     =			False
      lowerCAmelCase     =			False
      lowerCAmelCase     =			False
   elif model_name == "encodec_48khz":
      lowerCAmelCase     =			[8, 5, 4, 2]
      lowerCAmelCase     =			[3.0, 6.0, 12.0, 24.0]
      lowerCAmelCase     =			48000
      lowerCAmelCase     =			2
      lowerCAmelCase     =			False
      lowerCAmelCase     =			'time_group_norm'
      lowerCAmelCase     =			True
      lowerCAmelCase     =			1.0
      lowerCAmelCase     =			0.01
   else:
      raise ValueError(f'''Unknown model name: {model_name}'''				)

   lowerCAmelCase     =			EncodecModel(lowerCamelCase				)

   lowerCAmelCase     =			EncodecFeatureExtractor(
       feature_size=config.audio_channels	, sampling_rate=config.sampling_rate	, chunk_length_s=config.chunk_length_s	, overlap=config.overlap	, )
   feature_extractor.save_pretrained(lowerCamelCase				)

   lowerCAmelCase     =			torch.load(lowerCamelCase				)
   if "best_state" in original_checkpoint:
      # we might have a training state saved, in which case discard the yaml results and just retain the weights
      lowerCAmelCase     =			original_checkpoint['best_state']
   recursively_load_weights(lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   model.save_pretrained(lowerCamelCase				)

   if repo_id:
      print('Pushing to the hub...'				)
      feature_extractor.push_to_hub(lowerCamelCase				)
      model.push_to_hub(lowerCamelCase				)


if __name__ == "__main__":
       __snake_case    						=argparse.ArgumentParser()
       parser.add_argument(
           """--model""",
           default="""encodec_24khz""",
           type=str,
           help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
       )
       parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
       parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
       parser.add_argument(
           """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
       )

       __snake_case    						=parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )





