from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 TFCvtForImageClassification, TFCvtModel
      from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   self.config_class(**self.inputs_dict )
     self.parent.assertTrue(hasattr(lowerCAmelCase			,			'''embed_dim''' ) )
     self.parent.assertTrue(hasattr(lowerCAmelCase			,			'''num_heads''' ) )






class 		_lowerCamelCase		:
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=13			,			lowerCAmelCase=64			,			lowerCAmelCase=3			,			lowerCAmelCase=[16, 48, 96]			,			lowerCAmelCase=[1, 3, 6]			,			lowerCAmelCase=[1, 2, 10]			,			lowerCAmelCase=[7, 3, 3]			,			lowerCAmelCase=[4, 2, 2]			,			lowerCAmelCase=[2, 1, 1]			,			lowerCAmelCase=[2, 2, 2]			,			lowerCAmelCase=[False, False, True]			,			lowerCAmelCase=[0.0, 0.0, 0.0]			,			lowerCAmelCase=0.02			,			lowerCAmelCase=1e-12			,			lowerCAmelCase=True			,			lowerCAmelCase=True			,			lowerCAmelCase=2			,			)							->       str:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   parent
     SCREAMING_SNAKE_CASE__: int=   batch_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   image_size
     SCREAMING_SNAKE_CASE__: List[Any]=   patch_sizes
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   patch_stride
     SCREAMING_SNAKE_CASE__: List[str]=   patch_padding
     SCREAMING_SNAKE_CASE__: str=   is_training
     SCREAMING_SNAKE_CASE__: int=   use_labels
     SCREAMING_SNAKE_CASE__: Optional[Any]=   num_labels
     SCREAMING_SNAKE_CASE__: int=   num_channels
     SCREAMING_SNAKE_CASE__: str=   embed_dim
     SCREAMING_SNAKE_CASE__: Optional[int]=   num_heads
     SCREAMING_SNAKE_CASE__: int=   stride_kv
     SCREAMING_SNAKE_CASE__: Dict=   depth
     SCREAMING_SNAKE_CASE__: Optional[Any]=   cls_token
     SCREAMING_SNAKE_CASE__: int=   attention_drop_rate
     SCREAMING_SNAKE_CASE__: Any=   initializer_range
     SCREAMING_SNAKE_CASE__: int=   layer_norm_eps
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     SCREAMING_SNAKE_CASE__: List[str]=   None
     if self.use_labels:
      # create a random int32 tensor of given shape
      SCREAMING_SNAKE_CASE__: Dict=   ids_tensor([self.batch_size]			,			self.num_labels )

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.get_config()
     return config, pixel_values, labels
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return CvtConfig(
         image_size=self.image_size			,			num_labels=self.num_labels			,			num_channels=self.num_channels			,			embed_dim=self.embed_dim			,			num_heads=self.num_heads			,			patch_sizes=self.patch_sizes			,			patch_padding=self.patch_padding			,			patch_stride=self.patch_stride			,			stride_kv=self.stride_kv			,			depth=self.depth			,			cls_token=self.cls_token			,			attention_drop_rate=self.attention_drop_rate			,			initializer_range=self.initializer_range			,			)
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     SCREAMING_SNAKE_CASE__: List[str]=   TFCvtModel(config=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[Any]=   model(lowerCAmelCase			,			training=lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[int]=   (self.image_size, self.image_size)
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[int]=   image_size[0], image_size[1]
     for i in range(len(self.depth ) ):
      SCREAMING_SNAKE_CASE__: List[Any]=   floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
     self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.embed_dim[-1], height, width) )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: List[str]=   self.num_labels
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   TFCvtForImageClassification(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: List[Any]=   model(lowerCAmelCase			,			labels=lowerCAmelCase			,			training=lowerCAmelCase )
     self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels) )



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






@require_tf
class 		_lowerCamelCase		( UpperCamelCase_     ,  UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
    __a						=   (
        {"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
        if is_tf_available()
        else {}
    )
    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    __a						=   False
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   TFCvtModelTester(self )
     SCREAMING_SNAKE_CASE__: List[Any]=   TFCvtConfigTester(self			,			config_class=lowerCAmelCase			,			has_text_modality=lowerCAmelCase			,			hidden_size=37 )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     self.config_tester.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()
    @unittest.skip(reason='''Cvt does not output attentions''' )
    def     UpperCamelCase_					(	self )							->       str:
     pass
    @unittest.skip(reason='''Cvt does not use inputs_embeds''' )
    def     UpperCamelCase_					(	self )							->       List[Any]:
     pass
    @unittest.skip(reason='''Cvt does not support input and output embeddings''' )
    def     UpperCamelCase_					(	self )							->       int:
     pass
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0			,			reason='''TF does not support backprop for grouped convolutions on CPU.'''			,			)
    def     UpperCamelCase_					(	self )							->       str:
     super().test_dataset_conversion()
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0			,			reason='''TF does not support backprop for grouped convolutions on CPU.'''			,			)
    @slow
    def     UpperCamelCase_					(	self )							->       List[str]:
     super().test_keras_fit()
    @unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8''' )
    def     UpperCamelCase_					(	self )							->       Any:
     SCREAMING_SNAKE_CASE__: Optional[int]=   tf.keras.mixed_precision.Policy('''mixed_float16''' )
     tf.keras.mixed_precision.set_global_policy(lowerCAmelCase )
     super().test_keras_fit()
     tf.keras.mixed_precision.set_global_policy('''float32''' )
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.model_tester.prepare_config_and_inputs_for_common()

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

      SCREAMING_SNAKE_CASE__: List[str]=   ['''pixel_values''']
      self.assertListEqual(arg_names[:1]			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     def check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase ):
      SCREAMING_SNAKE_CASE__: Optional[Any]=   model_class(lowerCAmelCase )

      SCREAMING_SNAKE_CASE__: int=   model(**self._prepare_for_class(lowerCAmelCase			,			lowerCAmelCase ) )
      SCREAMING_SNAKE_CASE__: List[Any]=   outputs.hidden_states

      SCREAMING_SNAKE_CASE__: str=   len(self.model_tester.depth )
      self.assertEqual(len(lowerCAmelCase )			,			lowerCAmelCase )

      # verify the first hidden states (first block)
      self.assertListEqual(
          list(hidden_states[0].shape[-3:] )			,			[
              self.model_tester.embed_dim[0],
              self.model_tester.image_size // 4,
              self.model_tester.image_size // 4,
          ]			,			)

     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[Any]=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   True
      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )

      # check that output_hidden_states also work using config
      del inputs_dict["output_hidden_states"]
      SCREAMING_SNAKE_CASE__: Optional[int]=   True

      check_hidden_states_output(lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: int=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: List[Any]=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )



    @slow
    def     UpperCamelCase_					(	self )							->       int:
     for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE__: Any=   TFCvtModel.from_pretrained(lowerCAmelCase )
      self.assertIsNotNone(lowerCAmelCase )







def       A__   (						):
 SCREAMING_SNAKE_CASE__: Optional[int]=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
 return image






@require_tf
@require_vision
class 		_lowerCamelCase		( unittest.TestCase     ):
    @cached_property
    def     UpperCamelCase_					(	self )							->       List[Any]:
     return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )



    @slow
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

     SCREAMING_SNAKE_CASE__: Tuple=   self.default_image_processor
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   prepare_img()
     SCREAMING_SNAKE_CASE__: Optional[int]=   image_processor(images=lowerCAmelCase			,			return_tensors='''tf''' )

     # forward pass
     SCREAMING_SNAKE_CASE__: List[str]=   model(**lowerCAmelCase )

     # verify the logits
     SCREAMING_SNAKE_CASE__: Optional[Any]=   tf.TensorShape((1, 1000) )
     self.assertEqual(outputs.logits.shape			,			lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: List[Any]=   tf.constant([0.9285, 0.9015, -0.3150] )
     self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,			lowerCAmelCase			,			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()
lowercase     :							List[str]       						=     logging.get_logger('transformers.models.encodec')

lowercase     :							str       						=     {
    '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',
}
lowercase     :							Optional[int]       						=     {
    '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',
}
lowercase     :							int       						=     {
    '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',
}
lowercase     :							int       						=     {
    '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',
}
lowercase     :							List[Any]       						=     {
    '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',
}
lowercase     :							Dict       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase     :							List[str]       						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase     :							Union[str, Any]       						=     []
lowercase     :							int       						=     []







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    for attribute in key.split('''.'''		):
        A			:  int   =		getattr(snake_case__						,							snake_case__		)

    if weight_type is not None:
        A			:  int   =		getattr(snake_case__						,							snake_case__		).shape
    else:
        A			:  List[str]   =		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":
        A			:  str   =		value
    elif weight_type == "weight_g":
        A			:  Union[str, Any]   =		value
    elif weight_type == "weight_v":
        A			:  List[Any]   =		value
    elif weight_type == "bias":
        A			:  Union[str, Any]   =		value
    elif weight_type == "running_mean":
        A			:  Union[str, Any]   =		value
    elif weight_type == "running_var":
        A			:  List[str]   =		value
    elif weight_type == "num_batches_tracked":
        A			:  List[Any]   =		value
    elif weight_type == "weight_ih_l0":
        A			:  Optional[int]   =		value
    elif weight_type == "weight_hh_l0":
        A			:  List[Any]   =		value
    elif weight_type == "bias_ih_l0":
        A			:  List[Any]   =		value
    elif weight_type == "bias_hh_l0":
        A			:  int   =		value
    elif weight_type == "weight_ih_l1":
        A			:  Tuple   =		value
    elif weight_type == "weight_hh_l1":
        A			:  List[str]   =		value
    elif weight_type == "bias_ih_l1":
        A			:  Any   =		value
    elif weight_type == "bias_hh_l1":
        A			:  Tuple   =		value
    else:
        A			:  str   =		value

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    for key in ignore_keys:
        if key.endswith('''.*'''		):
            if name.startswith(key[:-1]		):
                return True
        elif ".*." in key:
            A,    A			:  List[str]   =		key.split('''.*.'''		)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        A			:  str   =		MAPPING_24K
    elif model_name == "encodec_48khz":
        A			:  List[Any]   =		MAPPING_48K
    else:
        raise ValueError(F'Unsupported model: {model_name}'		)

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

        A			:  int   =		False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                A,    A			:  Optional[int]   =		key.split('''.*.'''		)
                if prefix in name and suffix in name:
                    A			:  Dict   =		suffix

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

                A			:  Union[str, Any]   =		True
                if "*" in mapped_key:
                    A			:  List[str]   =		name.split(snake_case__		)[0].split('''.'''		)[-2]
                    A			:  Optional[Any]   =		mapped_key.replace('''*'''						,							snake_case__		)
                if "weight_g" in name:
                    A			:  Dict   =		'''weight_g'''
                elif "weight_v" in name:
                    A			:  List[str]   =		'''weight_v'''
                elif "weight_ih_l0" in name:
                    A			:  int   =		'''weight_ih_l0'''
                elif "weight_hh_l0" in name:
                    A			:  Any   =		'''weight_hh_l0'''
                elif "bias_ih_l0" in name:
                    A			:  Dict   =		'''bias_ih_l0'''
                elif "bias_hh_l0" in name:
                    A			:  List[Any]   =		'''bias_hh_l0'''
                elif "weight_ih_l1" in name:
                    A			:  Any   =		'''weight_ih_l1'''
                elif "weight_hh_l1" in name:
                    A			:  Tuple   =		'''weight_hh_l1'''
                elif "bias_ih_l1" in name:
                    A			:  Dict   =		'''bias_ih_l1'''
                elif "bias_hh_l1" in name:
                    A			:  List[str]   =		'''bias_hh_l1'''
                elif "bias" in name:
                    A			:  int   =		'''bias'''
                elif "weight" in name:
                    A			:  Optional[Any]   =		'''weight'''
                elif "running_mean" in name:
                    A			:  Optional[Any]   =		'''running_mean'''
                elif "running_var" in name:
                    A			:  Dict   =		'''running_var'''
                elif "num_batches_tracked" in name:
                    A			:  List[str]   =		'''num_batches_tracked'''
                else:
                    A			:  int   =		None
                set_recursively(snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		)
            continue
        if not is_used:
            unused_weights.append(snake_case__		)

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







@torch.no_grad()
def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None						,							snake_case__=None						,							):




    '''simple docstring'''



    if config_path is not None:
        A			:  Dict   =		EncodecConfig.from_pretrained(snake_case__		)
    else:
        A			:  Dict   =		EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        A			:  Optional[Any]   =		[8, 5, 4, 4]
        A			:  List[Any]   =		[2.2]
        A			:  Tuple   =		64
        A			:  str   =		3_2000
        A			:  Optional[Any]   =		2048
        A			:  Optional[int]   =		False
        A			:  Optional[int]   =		False
        A			:  List[Any]   =		False
    elif model_name == "encodec_48khz":
        A			:  List[str]   =		[8, 5, 4, 2]
        A			:  Any   =		[3.0, 6.0, 12.0, 24.0]
        A			:  Tuple   =		4_8000
        A			:  Optional[int]   =		2
        A			:  Dict   =		False
        A			:  Union[str, Any]   =		'''time_group_norm'''
        A			:  List[Any]   =		True
        A			:  Union[str, Any]   =		1.0
        A			:  Union[str, Any]   =		0.01
    else:
        raise ValueError(F'Unknown model name: {model_name}'		)

    A			:  Optional[Any]   =		EncodecModel(snake_case__		)

    A			:  Optional[Any]   =		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(snake_case__		)

    A			:  Optional[int]   =		torch.load(snake_case__		)
    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
        A			:  Union[str, Any]   =		original_checkpoint['''best_state''']
    recursively_load_weights(snake_case__						,							snake_case__						,							snake_case__		)
    model.save_pretrained(snake_case__		)

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


if __name__ == "__main__":
      lowercase     :							str       						=     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.'
      )

      lowercase     :							Union[str, Any]       						=     parser.parse_args()
      convert_checkpoint(
          args.model,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )




