






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()


class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					StableDiffusionLDMaDPipeline
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					TEXT_TO_IMAGE_PARAMS
						_SCREAMING_SNAKE_CASE	:       int   =					TEXT_TO_IMAGE_BATCH_PARAMS
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					TEXT_TO_IMAGE_IMAGE_PARAMS






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												torch.manual_seed(0   )
												lowerCAmelCase__    						=						UNetaDConditionModel(
												    block_out_channels=(32, 64)     ,						layers_per_block=2     ,						sample_size=32     ,						in_channels=4     ,						out_channels=4     ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,						cross_attention_dim=32     ,						)
												lowerCAmelCase__    						=						DDIMScheduler(
												    beta_start=0.0_00_85     ,						beta_end=0.0_12     ,						beta_schedule='scaled_linear'     ,						clip_sample=_UpperCamelCase     ,						set_alpha_to_one=_UpperCamelCase     ,						)
												torch.manual_seed(0   )
												lowerCAmelCase__    						=						AutoencoderKL(
												    block_out_channels=[32, 64]     ,						in_channels=6     ,						out_channels=6     ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,						latent_channels=4     ,						)
												torch.manual_seed(0   )
												lowerCAmelCase__    						=						CLIPTextConfig(
												    bos_token_id=0     ,						eos_token_id=2     ,						hidden_size=32     ,						intermediate_size=37     ,						layer_norm_eps=1E-05     ,						num_attention_heads=4     ,						num_hidden_layers=5     ,						pad_token_id=1     ,						vocab_size=10_00     ,						)
												lowerCAmelCase__    						=						CLIPTextModel(_UpperCamelCase   )
												lowerCAmelCase__    						=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

												lowerCAmelCase__    						=						{
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}
												return components






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=0   ):






												"""simple docstring"""
												if str(_UpperCamelCase   ).startswith('mps'   ):
																		lowerCAmelCase__    						=						torch.manual_seed(_UpperCamelCase   )
												else:
																		lowerCAmelCase__    						=						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
												lowerCAmelCase__    						=						{
												    'prompt': 'A painting of a squirrel eating a burger',
												    'generator': generator,
												    'num_inference_steps': 2,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												}
												return inputs






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						'cpu'  # ensure determinism for the device-dependent torch.Generator

												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						StableDiffusionLDMaDPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						ldmad_pipe.to(_UpperCamelCase   )
												ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						ldmad_pipe(**_UpperCamelCase   )
												lowerCAmelCase__						,       lowerCAmelCase__    						=						output.rgb, output.depth

												lowerCAmelCase__    						=						rgb[0, -3:, -3:, -1]
												lowerCAmelCase__    						=						depth[0, -3:, -1]

												assert rgb.shape == (1, 64, 64, 3)
												assert depth.shape == (1, 64, 64)

												lowerCAmelCase__    						=						np.array(
												    [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]   )
												lowerCAmelCase__    						=						np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]   )

												assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1E-2
												assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1E-2






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						StableDiffusionLDMaDPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						ldmad_pipe.to(_UpperCamelCase   )
												ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						3 * [inputs['prompt']]

												# forward
												lowerCAmelCase__    						=						ldmad_pipe(**_UpperCamelCase   )
												lowerCAmelCase__						,       lowerCAmelCase__    						=						output.rgb, output.depth
												lowerCAmelCase__    						=						rgb_slice_a[0, -3:, -3:, -1]
												lowerCAmelCase__    						=						depth_slice_a[0, -3:, -1]

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						3 * [inputs.pop('prompt'   )]

												lowerCAmelCase__    						=						ldmad_pipe.tokenizer(
												    _UpperCamelCase     ,						padding='max_length'     ,						max_length=ldmad_pipe.tokenizer.model_max_length     ,						truncation=_UpperCamelCase     ,						return_tensors='pt'     ,						)
												lowerCAmelCase__    						=						text_inputs['input_ids'].to(_UpperCamelCase   )

												lowerCAmelCase__    						=						ldmad_pipe.text_encoder(_UpperCamelCase   )[0]

												lowerCAmelCase__    						=						prompt_embeds

												# forward
												lowerCAmelCase__    						=						ldmad_pipe(**_UpperCamelCase   )
												lowerCAmelCase__						,       lowerCAmelCase__    						=						output.rgb, output.depth
												lowerCAmelCase__    						=						rgb_slice_a[0, -3:, -3:, -1]
												lowerCAmelCase__    						=						depth_slice_a[0, -3:, -1]

												assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1E-4
												assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1E-4







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						'cpu'  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__    						=						self.get_dummy_components()
												lowerCAmelCase__    						=						PNDMScheduler(skip_prk_steps=_UpperCamelCase   )
												lowerCAmelCase__    						=						StableDiffusionLDMaDPipeline(**_UpperCamelCase   )
												lowerCAmelCase__    						=						ldmad_pipe.to(_UpperCamelCase   )
												ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_dummy_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						'french fries'
												lowerCAmelCase__    						=						ldmad_pipe(**_UpperCamelCase     ,						negative_prompt=_UpperCamelCase   )

												lowerCAmelCase__						,       lowerCAmelCase__    						=						output.rgb, output.depth
												lowerCAmelCase__    						=						rgb[0, -3:, -3:, -1]
												lowerCAmelCase__    						=						depth[0, -3:, -1]

												assert rgb.shape == (1, 64, 64, 3)
												assert depth.shape == (1, 64, 64)

												lowerCAmelCase__    						=						np.array(
												    [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]   )
												lowerCAmelCase__    						=						np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]   )
												assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1E-2
												assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1E-2




@slow
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase="cpu"     ,						_UpperCamelCase=torch.floataa     ,						_UpperCamelCase=0   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
												lowerCAmelCase__    						=						np.random.RandomState(_UpperCamelCase   ).standard_normal((1, 4, 64, 64)   )
												lowerCAmelCase__    						=						torch.from_numpy(_UpperCamelCase   ).to(device=_UpperCamelCase     ,						dtype=_UpperCamelCase   )
												lowerCAmelCase__    						=						{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'latents': latents,
												    'generator': generator,
												    'num_inference_steps': 3,
												    'guidance_scale': 7.5,
												    'output_type': 'numpy',
												}
												return inputs







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'   )
												lowerCAmelCase__    						=						ldmad_pipe.to(_UpperCamelCase   )
												ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						ldmad_pipe(**_UpperCamelCase   )
												lowerCAmelCase__						,       lowerCAmelCase__    						=						output.rgb, output.depth
												lowerCAmelCase__    						=						rgb[0, -3:, -3:, -1].flatten()
												lowerCAmelCase__    						=						rgb[0, -3:, -1].flatten()

												assert rgb.shape == (1, 5_12, 5_12, 3)
												assert depth.shape == (1, 5_12, 5_12)

												lowerCAmelCase__    						=						np.array(
												    [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]   )
												lowerCAmelCase__    						=						np.array(
												    [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]   )
												assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3E-3
												assert np.abs(depth_slice - expected_slice_depth   ).max() < 3E-3




@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase="cpu"     ,						_UpperCamelCase=torch.floataa     ,						_UpperCamelCase=0   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
												lowerCAmelCase__    						=						np.random.RandomState(_UpperCamelCase   ).standard_normal((1, 4, 64, 64)   )
												lowerCAmelCase__    						=						torch.from_numpy(_UpperCamelCase   ).to(device=_UpperCamelCase     ,						dtype=_UpperCamelCase   )
												lowerCAmelCase__    						=						{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'latents': latents,
												    'generator': generator,
												    'num_inference_steps': 50,
												    'guidance_scale': 7.5,
												    'output_type': 'numpy',
												}
												return inputs






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'   ).to(_UpperCamelCase   )
												ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						ldmad_pipe(**_UpperCamelCase   )
												lowerCAmelCase__						,       lowerCAmelCase__    						=						output.rgb, output.depth

												lowerCAmelCase__    						=						0.49_55_86
												lowerCAmelCase__    						=						0.33_79_55_15
												lowerCAmelCase__    						=						1_12.4_85_18
												lowerCAmelCase__    						=						98.48_97_46
												assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
												assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
												assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
												assert np.abs(expected_depth_std - depth.std()   ) < 1E-3







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'   ).to(_UpperCamelCase   )
												ldmad_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

												lowerCAmelCase__    						=						self.get_inputs(_UpperCamelCase   )
												lowerCAmelCase__    						=						ldmad_pipe(**_UpperCamelCase   )
												lowerCAmelCase__						,       lowerCAmelCase__    						=						output.rgb, output.depth

												lowerCAmelCase__    						=						0.4_19_41_27
												lowerCAmelCase__    						=						0.35_37_55_86
												lowerCAmelCase__    						=						0.5_63_85_02
												lowerCAmelCase__    						=						0.34_68_61_03

												assert rgb.shape == (1, 5_12, 5_12, 3)
												assert depth.shape == (1, 5_12, 5_12, 1)
												assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
												assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
												assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
												assert np.abs(expected_depth_std - depth.std()   ) < 1E-3













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    :    int         =     logging.get_logger("""transformers.models.encodec""")

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Optional[int]		)  ->   List[str]:

						"""simple docstring"""



						for attribute in key.split('.'		):
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       UpperCamelCase_		)

						if weight_type is not None:
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       UpperCamelCase_		).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 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					List[str]		)  ->   Tuple:

						"""simple docstring"""



						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 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					str		)  ->   Union[str, Any]:

						"""simple docstring"""



						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(UpperCamelCase_    ,       UpperCamelCase_		):
																		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(UpperCamelCase_		)[0].split('.'		)[-2]
																														lowerCAmelCase__    						=						mapped_key.replace('*'    ,       UpperCamelCase_		)
																								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(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																		continue
												if not is_used:
																		unused_weights.append(UpperCamelCase_		)

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







@torch.no_grad()
def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Optional[int]=None    ,       UpperCamelCase_       :					List[Any]=None    ,       )  ->   Dict:

						"""simple docstring"""



						if config_path is not None:
												lowerCAmelCase__    						=						EncodecConfig.from_pretrained(UpperCamelCase_		)
						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__    						=						3_2000
												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__    						=						4_8000
												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(UpperCamelCase_		)

						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(UpperCamelCase_		)

						lowerCAmelCase__    						=						torch.load(UpperCamelCase_		)
						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(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
						model.save_pretrained(UpperCamelCase_		)

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


if __name__ == "__main__":
					__snake_case    :    Optional[int]         =     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    :    Dict         =     parser.parse_args()
					convert_checkpoint(
					    args.model,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)





