

import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
     TFEfficientFormerForImageClassification,
     TFEfficientFormerForImageClassificationWithTeacher,
     TFEfficientFormerModel,
 )
 from transformers.models.efficientformer.modeling_tf_efficientformer import (
     TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
 )


if is_vision_available():
 from PIL import Image

 from transformers import EfficientFormerImageProcessor






class 						__magic_name__					:






     def __init__(				self		,						_lowercase		,						_lowercase = 13		,						_lowercase = 64		,						_lowercase = 2		,						_lowercase = 3		,						_lowercase = 3		,						_lowercase = True		,						_lowercase = True		,						_lowercase = 128		,						_lowercase=[16, 32, 64, 128]		,						_lowercase = 7		,						_lowercase = 4		,						_lowercase = 37		,						_lowercase = "gelu"		,						_lowercase = 0.1		,						_lowercase = 0.1		,						_lowercase = 10		,						_lowercase = 0.02		,						_lowercase = 2		,						_lowercase = 1		,						_lowercase = 128		,						_lowercase = [2, 2, 2, 2]		,						_lowercase = 2		,						_lowercase = 2		,						)->     int:
         UpperCamelCase_			  =						parent
         UpperCamelCase_			  =						batch_size
         UpperCamelCase_			  =						image_size
         UpperCamelCase_			  =						patch_size
         UpperCamelCase_			  =						num_channels
         UpperCamelCase_			  =						is_training
         UpperCamelCase_			  =						use_labels
         UpperCamelCase_			  =						hidden_size
         UpperCamelCase_			  =						num_hidden_layers
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						intermediate_size
         UpperCamelCase_			  =						hidden_act
         UpperCamelCase_			  =						hidden_dropout_prob
         UpperCamelCase_			  =						attention_probs_dropout_prob
         UpperCamelCase_			  =						type_sequence_label_size
         UpperCamelCase_			  =						initializer_range
         UpperCamelCase_			  =						encoder_stride
         UpperCamelCase_			  =						num_attention_outputs
         UpperCamelCase_			  =						embed_dim
         UpperCamelCase_			  =						embed_dim + 1
         UpperCamelCase_			  =						resolution
         UpperCamelCase_			  =						depths
         UpperCamelCase_			  =						hidden_sizes
         UpperCamelCase_			  =						dim
         UpperCamelCase_			  =						mlp_expansion_ratio






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

         UpperCamelCase_			  =						self.get_config()

         return config, pixel_values, labels






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         return EfficientFormerConfig(
             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		,						hidden_act=self.hidden_act		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						is_decoder=_lowercase		,						initializer_range=self.initializer_range		,						encoder_stride=self.encoder_stride		,						resolution=self.resolution		,						depths=self.depths		,						hidden_sizes=self.hidden_sizes		,						dim=self.dim		,						mlp_expansion_ratio=self.mlp_expansion_ratio		,						)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     int:
         UpperCamelCase_			  =						TFEfficientFormerModel(config=_lowercase	)
         UpperCamelCase_			  =						model(_lowercase		,						training=_lowercase	)
         self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.type_sequence_label_size
         UpperCamelCase_			  =						TFEfficientFormerForImageClassification(_lowercase	)
         UpperCamelCase_			  =						model(_lowercase		,						labels=_lowercase		,						training=_lowercase	)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)	)

         # test greyscale images
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						TFEfficientFormerForImageClassification(_lowercase	)

         UpperCamelCase_			  =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
         UpperCamelCase_			  =						model(_lowercase		,						labels=_lowercase	)
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.type_sequence_label_size)	)






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						self.prepare_config_and_inputs()
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						config_and_inputs
         UpperCamelCase_			  =						{"pixel_values": pixel_values}
         return config, inputs_dict






@require_tf
class 						__magic_name__					(							snake_case			,      snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:Optional[int]										=      (
         (
             TFEfficientFormerModel,
             TFEfficientFormerForImageClassificationWithTeacher,
             TFEfficientFormerForImageClassification,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase_	:Union[str, Any]										=      (
         {
             """feature-extraction""": TFEfficientFormerModel,
             """image-classification""": (
                 TFEfficientFormerForImageClassification,
                 TFEfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_tf_available()
         else {}
     )

     UpperCamelCase_	:Optional[int]										=      False

     UpperCamelCase_	:Optional[int]										=      False
     UpperCamelCase_	:str										=      False
     UpperCamelCase_	:List[Any]										=      False
     UpperCamelCase_	:str										=      False






     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						TFEfficientFormerModelTester(self	)
         UpperCamelCase_			  =						ConfigTester(
             self		,						config_class=_lowercase		,						has_text_modality=_lowercase		,						hidden_size=37	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         self.config_tester.run_common_tests()






     @unittest.skip(reason="EfficientFormer does not use inputs_embeds"	)
     def 	UpperCAmelCase_				(				self	)->     str:
         pass






     @unittest.skip(reason="EfficientFormer does not support input and output embeddings"	)
     def 	UpperCAmelCase_				(				self	)->     Any:
         pass






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase_			  =						["pixel_values"]
             self.assertListEqual(arg_names[:1]		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         def check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model(**self._prepare_for_class(_lowercase		,						_lowercase	)		,						training=_lowercase	)
             UpperCamelCase_			  =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             UpperCamelCase_			  =						getattr(
                 self.model_tester		,						"expected_num_hidden_layers"		,						self.model_tester.num_hidden_layers + 1	)
             self.assertEqual(len(_lowercase	)		,						_lowercase	)

             if hasattr(self.model_tester		,						"encoder_seq_length"	):
                 UpperCamelCase_			  =						self.model_tester.encoder_seq_length
                 if hasattr(self.model_tester		,						"chunk_length"	) and self.model_tester.chunk_length > 1:
                     UpperCamelCase_			  =						seq_length * self.model_tester.chunk_length
             else:
                 UpperCamelCase_			  =						self.model_tester.seq_length

             self.assertListEqual(
                 list(hidden_states[-1].shape[-2:]	)		,						[seq_length, self.model_tester.hidden_size]		,						)

             if config.is_encoder_decoder:
                 UpperCamelCase_			  =						outputs.decoder_hidden_states

                 self.asseretIsInstance(_lowercase		,						(list, tuple)	)
                 self.assertEqual(len(_lowercase	)		,						_lowercase	)
                 UpperCamelCase_			  =						getattr(self.model_tester		,						"seq_length"		,						_lowercase	)
                 UpperCamelCase_			  =						getattr(self.model_tester		,						"decoder_seq_length"		,						_lowercase	)

                 self.assertListEqual(
                     list(hidden_states[-1].shape[-2:]	)		,						[decoder_seq_length, self.model_tester.hidden_size]		,						)

         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(_lowercase		,						_lowercase		,						_lowercase	)

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

             check_hidden_states_output(_lowercase		,						_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase		,						_lowercase=False	)->     int:
         UpperCamelCase_			  =						super()._prepare_for_class(_lowercase		,						_lowercase		,						return_labels=_lowercase	)

         if return_labels:
             if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                 del inputs_dict["labels"]

         return inputs_dict






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase	)






     @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"	)
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowercase	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     int:
         for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_			  =						TFEfficientFormerModel.from_pretrained(_lowercase	)
             self.assertIsNotNone(_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_			  =						True

         UpperCamelCase_			  =						getattr(self.model_tester		,						"seq_length"		,						_lowercase	)
         UpperCamelCase_			  =						getattr(self.model_tester		,						"encoder_seq_length"		,						_lowercase	)
         UpperCamelCase_			  =						getattr(self.model_tester		,						"key_length"		,						_lowercase	)
         UpperCamelCase_			  =						getattr(self.model_tester		,						"chunk_length"		,						_lowercase	)

         if chunk_length is not None and hasattr(self.model_tester		,						"num_hashes"	):
             UpperCamelCase_			  =						encoder_seq_length * self.model_tester.num_hashes

         for model_class in self.all_model_classes:
             UpperCamelCase_			  =						True
             UpperCamelCase_			  =						False
             UpperCamelCase_			  =						True
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model(**self._prepare_for_class(_lowercase		,						_lowercase	)		,						training=_lowercase	)
             UpperCamelCase_			  =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(_lowercase	)		,						self.model_tester.num_attention_outputs	)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCamelCase_			  =						True
             UpperCamelCase_			  =						model_class(_lowercase	)
             UpperCamelCase_			  =						model(**self._prepare_for_class(_lowercase		,						_lowercase	)		,						training=_lowercase	)

             UpperCamelCase_			  =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(_lowercase	)		,						self.model_tester.num_attention_outputs	)

             if chunk_length is not None:
                 self.assertListEqual(
                     list(attentions[0].shape[-4:]	)		,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]		,						)
             else:
                 self.assertListEqual(
                     list(attentions[0].shape[-3:]	)		,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]		,						)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         # We use a simplified version of this test for EfficientFormer because it requires training=False
         # and Keras refuses to let us force that during functional construction
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             # Prepare our model
             UpperCamelCase_			  =						model_class(_lowercase	)
             # These are maximally general inputs for the model, with multiple None dimensions
             # Hopefully this will catch any conditionals that fail for flexible shapes
             UpperCamelCase_			  =						{
                 key: tf.keras.Input(shape=val.shape[1:]		,						dtype=val.dtype		,						name=_lowercase	)
                 for key, val in model.input_signature.items()
                 if key in model.dummy_inputs
             }
             UpperCamelCase_			  =						model(_lowercase	)
             self.assertTrue(outputs_dict is not None	)




def 			lowerCAmelCase(      )->						Optional[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
    return image






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






     @cached_property
     def 	UpperCAmelCase_				(				self	)->     int:
         return (
             EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"	)
             if is_vision_available()
             else None
         )






     @slow
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_			  =						TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"	)
         UpperCamelCase_			  =						self.default_image_processor
         UpperCamelCase_			  =						prepare_img()
         UpperCamelCase_			  =						image_processor(images=_lowercase		,						return_tensors="tf"	)
         # forward pass
         UpperCamelCase_			  =						model(**_lowercase		,						training=_lowercase	)
         # verify the logits
         UpperCamelCase_			  =						tf.TensorShape((1, 1_000)	)
         self.assertEqual(outputs.logits.shape		,						_lowercase	)
         UpperCamelCase_			  =						tf.constant([-0.0_555, 0.4_825, -0.0_852]	)
         self.assertTrue(np.allclose(outputs.logits[0, :3]		,						_lowercase		,						atol=1e-4	)	)






     @slow
     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
             "snap-research/efficientformer-l1-300"	)
         UpperCamelCase_			  =						self.default_image_processor
         UpperCamelCase_			  =						prepare_img()
         UpperCamelCase_			  =						image_processor(images=_lowercase		,						return_tensors="tf"	)
         # forward pass
         UpperCamelCase_			  =						model(**_lowercase		,						training=_lowercase	)
         # verify the logits
         UpperCamelCase_			  =						tf.TensorShape((1, 1_000)	)
         self.assertEqual(outputs.logits.shape		,						_lowercase	)
         UpperCamelCase_			  =						tf.constant([-0.1_312, 0.4_353, -1.0_499]	)
         self.assertTrue(np.allclose(outputs.logits[0, :3]		,						_lowercase		,						atol=1e-4	)	)



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()
SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger("""transformers.models.encodec""")

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    for attribute in key.split("."			):
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    if weight_type is not None:
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			).shape
    else:
        UpperCamelCase_			  =						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":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_g":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_v":
        UpperCamelCase_			  =						value
    elif weight_type == "bias":
        UpperCamelCase_			  =						value
    elif weight_type == "running_mean":
        UpperCamelCase_			  =						value
    elif weight_type == "running_var":
        UpperCamelCase_			  =						value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l1":
        UpperCamelCase_			  =						value
    else:
        UpperCamelCase_			  =						value

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[]

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

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

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

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

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

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




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			,						SCREAMING_SNAKE_CASE_=None			,						)->						Union[str, Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_			  =						EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
    else:
        UpperCamelCase_			  =						EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        UpperCamelCase_			  =						[8, 5, 4, 4]
        UpperCamelCase_			  =						[2.2]
        UpperCamelCase_			  =						6_4
        UpperCamelCase_			  =						3_2_0_0_0
        UpperCamelCase_			  =						2_0_4_8
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						[8, 5, 4, 2]
        UpperCamelCase_			  =						[3.0, 6.0, 12.0, 24.0]
        UpperCamelCase_			  =						4_8_0_0_0
        UpperCamelCase_			  =						2
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						"time_group_norm"
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						1.0
        UpperCamelCase_			  =						0.01
    else:
        raise ValueError(f"Unknown model name: {model_name}"			)

    UpperCamelCase_			  =						EncodecModel(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						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(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						torch.load(SCREAMING_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
        UpperCamelCase_			  =						original_checkpoint["best_state"]
    recursively_load_weights(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if repo_id:
        print("Pushing to the hub..."			)
        feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_			)
        model.push_to_hub(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     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."""
 )

 SCREAMING_SNAKE_CASE		:Dict						=     parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )
