
import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel



def 						lowercase_			(   A__ ,		A__=1		)							->							Optional[Any]:
    """simple docstring"""

    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("."		)[n_shave_prefix_segments:]		)
    else:
        return ".".join(path.split("."		)[:n_shave_prefix_segments]		)



def 						lowercase_			(   A__ ,		A__=0		)							->							Optional[Any]:
    """simple docstring"""

    snake_case      =					[]
    for old_item in old_list:
        snake_case      =					old_item
        snake_case      =					new_item.replace("block." ,		"resnets."		)
        snake_case      =					new_item.replace("conv_shorcut" ,		"conv1"		)
        snake_case      =					new_item.replace("in_shortcut" ,		"conv_shortcut"		)
        snake_case      =					new_item.replace("temb_proj" ,		"time_emb_proj"		)

        snake_case      =					shave_segments(A__ ,		n_shave_prefix_segments=A__		)

        mapping.append({"old": old_item, "new": new_item}		)

    return mapping



def 						lowercase_			(   A__ ,		A__=0 ,		A__=False		)							->							Any:
    """simple docstring"""

    snake_case      =					[]
    for old_item in old_list:
        snake_case      =					old_item

        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
            snake_case      =					new_item.replace("attn" ,		"attentions"		)
        snake_case      =					new_item.replace(".k." ,		".key."		)
        snake_case      =					new_item.replace(".v." ,		".value."		)
        snake_case      =					new_item.replace(".q." ,		".query."		)

        snake_case      =					new_item.replace("proj_out" ,		"proj_attn"		)
        snake_case      =					new_item.replace("norm" ,		"group_norm"		)

        snake_case      =					shave_segments(A__ ,		n_shave_prefix_segments=A__		)
        mapping.append({"old": old_item, "new": new_item}		)

    return mapping



def 						lowercase_			(   A__ ,		A__ ,		A__ ,		A__=None ,		A__=None ,		A__=None		)							->							Optional[Any]:
    """simple docstring"""

    assert isinstance(A__ ,		A__		), "Paths should be a list of dicts containing 'old' and 'new' keys."

    if attention_paths_to_split is not None:
        if config is None:
            raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."		)

        for path, path_map in attention_paths_to_split.items():
            snake_case      =					old_checkpoint[path]
            snake_case      =					old_tensor.shape[0] // 3

            snake_case      =					(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

            snake_case      =					old_tensor.shape[0] // config.get("num_head_channels" ,		1		) // 3

            snake_case      =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
            snake_case     ,							snake_case     ,							snake_case      =					old_tensor.split(channels // num_heads ,		dim=1		)

            snake_case      =					query.reshape(A__		).squeeze()
            snake_case      =					key.reshape(A__		).squeeze()
            snake_case      =					value.reshape(A__		).squeeze()

    for path in paths:
        snake_case      =					path["new"]

        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        snake_case      =					new_path.replace("down." ,		"down_blocks."		)
        snake_case      =					new_path.replace("up." ,		"up_blocks."		)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                snake_case      =					new_path.replace(replacement["old"] ,		replacement["new"]		)

        if "attentions" in new_path:
            snake_case      =					old_checkpoint[path["old"]].squeeze()
        else:
            snake_case      =					old_checkpoint[path["old"]]



def 						lowercase_			(   A__ ,		A__		)							->							List[str]:
    """simple docstring"""

    snake_case      =					{}

    snake_case      =					checkpoint["temb.dense.0.weight"]
    snake_case      =					checkpoint["temb.dense.0.bias"]
    snake_case      =					checkpoint["temb.dense.1.weight"]
    snake_case      =					checkpoint["temb.dense.1.bias"]

    snake_case      =					checkpoint["norm_out.weight"]
    snake_case      =					checkpoint["norm_out.bias"]

    snake_case      =					checkpoint["conv_in.weight"]
    snake_case      =					checkpoint["conv_in.bias"]
    snake_case      =					checkpoint["conv_out.weight"]
    snake_case      =					checkpoint["conv_out.bias"]

    snake_case      =					len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "down" in layer}		)
    snake_case      =					{
        layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(A__		)
    }

    snake_case      =					len({".".join(layer.split("."		)[:2]		) for layer in checkpoint if "up" in layer}		)
    snake_case      =					{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(A__		)}

    for i in range(A__		):
        snake_case      =					(i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]		):
            snake_case      =					checkpoint[
                F'down.{i}.downsample.op.weight'
            ]
            snake_case      =					checkpoint[F'down.{i}.downsample.op.bias']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

        if any("block" in layer for layer in down_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		2		).split("."		)[:2]		) for layer in down_blocks[i] if "block" in layer}		)
            snake_case      =					{
                layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                for layer_id in range(A__		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]		):
                    snake_case      =					renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__		)

        if any("attn" in layer for layer in down_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		2		).split("."		)[:2]		) for layer in down_blocks[i] if "attn" in layer}		)
            snake_case      =					{
                layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                for layer_id in range(A__		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]		):
                    snake_case      =					renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__ ,		config=A__		)

    snake_case      =					[key for key in checkpoint if "mid.block_1" in key]
    snake_case      =					[key for key in checkpoint if "mid.block_2" in key]
    snake_case      =					[key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    snake_case      =					renew_resnet_paths(A__		)
    assign_to_checkpoint(
        A__ ,		A__ ,		A__ ,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,		)

    snake_case      =					renew_resnet_paths(A__		)
    assign_to_checkpoint(
        A__ ,		A__ ,		A__ ,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,		)

    snake_case      =					renew_attention_paths(A__ ,		in_mid=A__		)
    assign_to_checkpoint(
        A__ ,		A__ ,		A__ ,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,		)

    for i in range(A__		):
        snake_case      =					num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]		):
            snake_case      =					checkpoint[
                F'up.{i}.upsample.conv.weight'
            ]
            snake_case      =					checkpoint[F'up.{i}.upsample.conv.bias']

        if any("block" in layer for layer in up_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		2		).split("."		)[:2]		) for layer in up_blocks[i] if "block" in layer}		)
            snake_case      =					{
                layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(A__		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1		):
                    snake_case      =					{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
                    snake_case      =					renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__ ,		additional_replacements=[replace_indices]		)

        if any("attn" in layer for layer in up_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		2		).split("."		)[:2]		) for layer in up_blocks[i] if "attn" in layer}		)
            snake_case      =					{
                layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(A__		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1		):
                    snake_case      =					{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
                    snake_case      =					renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__ ,		additional_replacements=[replace_indices]		)

    snake_case      =					{k.replace("mid_new_2" ,		"mid_block"		): v for k, v in new_checkpoint.items()}
    return new_checkpoint



def 						lowercase_			(   A__ ,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case      =					{}

    snake_case      =					checkpoint["encoder.norm_out.weight"]
    snake_case      =					checkpoint["encoder.norm_out.bias"]

    snake_case      =					checkpoint["encoder.conv_in.weight"]
    snake_case      =					checkpoint["encoder.conv_in.bias"]
    snake_case      =					checkpoint["encoder.conv_out.weight"]
    snake_case      =					checkpoint["encoder.conv_out.bias"]

    snake_case      =					checkpoint["decoder.norm_out.weight"]
    snake_case      =					checkpoint["decoder.norm_out.bias"]

    snake_case      =					checkpoint["decoder.conv_in.weight"]
    snake_case      =					checkpoint["decoder.conv_in.bias"]
    snake_case      =					checkpoint["decoder.conv_out.weight"]
    snake_case      =					checkpoint["decoder.conv_out.bias"]

    snake_case      =					len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "down" in layer}		)
    snake_case      =					{
        layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(A__		)
    }

    snake_case      =					len({".".join(layer.split("."		)[:3]		) for layer in checkpoint if "up" in layer}		)
    snake_case      =					{layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(A__		)}

    for i in range(A__		):
        snake_case      =					(i - 1) // (config["layers_per_block"] + 1)

        if any("downsample" in layer for layer in down_blocks[i]		):
            snake_case      =					checkpoint[
                F'encoder.down.{i}.downsample.conv.weight'
            ]
            snake_case      =					checkpoint[
                F'encoder.down.{i}.downsample.conv.bias'
            ]

        if any("block" in layer for layer in down_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		3		).split("."		)[:3]		) for layer in down_blocks[i] if "block" in layer}		)
            snake_case      =					{
                layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                for layer_id in range(A__		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"]		):
                    snake_case      =					renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__		)

        if any("attn" in layer for layer in down_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		3		).split("."		)[:3]		) for layer in down_blocks[i] if "attn" in layer}		)
            snake_case      =					{
                layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                for layer_id in range(A__		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"]		):
                    snake_case      =					renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__ ,		config=A__		)

    snake_case      =					[key for key in checkpoint if "mid.block_1" in key]
    snake_case      =					[key for key in checkpoint if "mid.block_2" in key]
    snake_case      =					[key for key in checkpoint if "mid.attn_1" in key]

    # Mid new 2
    snake_case      =					renew_resnet_paths(A__		)
    assign_to_checkpoint(
        A__ ,		A__ ,		A__ ,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}] ,		)

    snake_case      =					renew_resnet_paths(A__		)
    assign_to_checkpoint(
        A__ ,		A__ ,		A__ ,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}] ,		)

    snake_case      =					renew_attention_paths(A__ ,		in_mid=A__		)
    assign_to_checkpoint(
        A__ ,		A__ ,		A__ ,		additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}] ,		)

    for i in range(A__		):
        snake_case      =					num_up_blocks - 1 - i

        if any("upsample" in layer for layer in up_blocks[i]		):
            snake_case      =					checkpoint[
                F'decoder.up.{i}.upsample.conv.weight'
            ]
            snake_case      =					checkpoint[
                F'decoder.up.{i}.upsample.conv.bias'
            ]

        if any("block" in layer for layer in up_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		3		).split("."		)[:3]		) for layer in up_blocks[i] if "block" in layer}		)
            snake_case      =					{
                layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(A__		)
            }

            if num_blocks > 0:
                for j in range(config["layers_per_block"] + 1		):
                    snake_case      =					{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
                    snake_case      =					renew_resnet_paths(blocks[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__ ,		additional_replacements=[replace_indices]		)

        if any("attn" in layer for layer in up_blocks[i]		):
            snake_case      =					len(
                {".".join(shave_segments(A__ ,		3		).split("."		)[:3]		) for layer in up_blocks[i] if "attn" in layer}		)
            snake_case      =					{
                layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(A__		)
            }

            if num_attn > 0:
                for j in range(config["layers_per_block"] + 1		):
                    snake_case      =					{"old": F'up_blocks.{i}', "new": F'up_blocks.{block_id}'}
                    snake_case      =					renew_attention_paths(attns[j]		)
                    assign_to_checkpoint(A__ ,		A__ ,		A__ ,		additional_replacements=[replace_indices]		)

    snake_case      =					{k.replace("mid_new_2" ,		"mid_block"		): v for k, v in new_checkpoint.items()}
    snake_case      =					checkpoint["quant_conv.weight"]
    snake_case      =					checkpoint["quant_conv.bias"]
    if "quantize.embedding.weight" in checkpoint:
        snake_case      =					checkpoint["quantize.embedding.weight"]
    snake_case      =					checkpoint["post_quant_conv.weight"]
    snake_case      =					checkpoint["post_quant_conv.bias"]

    return new_checkpoint


if __name__ == "__main__":
   _A  					= argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
   )

   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help="The config json file corresponding to the architecture.",
   )

   parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

   _A  					= parser.parse_args()
   _A  					= torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      _A  					= json.loads(f.read())

   # unet case
   _A  					= {key.split(".")[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
      _A  					= convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
      _A  					= convert_ddpm_checkpoint(checkpoint, config)

   if "ddpm" in config:
      del config["ddpm"]

   if config["_class_name"] == "VQModel":
      _A  					= VQModel(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
      _A  					= AutoencoderKL(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   else:
      _A  					= UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      _A  					= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

      _A  					= DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)



import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





     def __init__(self      :    Dict				,    _A      :    Optional[Any]=1_0_0_0_0				,    _A      :    List[Any]=True				,    _A      :    Any=7_6_8				,    _A      :    Union[str, Any]=False				,    _A      :    List[Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    str=0.7				,    _A      :    Optional[int]=True				,    _A      :    Any=1_0_0_4_8				,    _A      :    Optional[int]=1_2_9_5_9_5				,    _A      :    Union[str, Any]=1_2_9_5_9_9				,    _A      :    Any=1_0_0_0_0				,    _A      :    str=2_5_6				,    _A      :    int=49.9				,    **_A      :    Union[str, Any]				,    )						->						Any:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    eos_token_id=_A				,    renormalize_logits=_A				,    max_new_tokens=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         snake_case      =					text_encoding_offset
         snake_case      =					text_pad_token
         snake_case      =					eos_token_id
         snake_case      =					semantic_infer_token
         snake_case      =					semantic_vocab_size
         snake_case      =					max_input_semantic_length
         snake_case      =					semantic_rate_hz







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"coarse_acoustics"





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						Union[str, Any]:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    renormalize_logits=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

         snake_case      =					coarse_semantic_pad_token
         snake_case      =					coarse_rate_hz
         snake_case      =					n_coarse_codebooks
         snake_case      =					coarse_infer_token
         snake_case      =					max_coarse_input_length
         snake_case      =					max_coarse_history
         snake_case      =					sliding_window_len







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





     def __init__(self      :    Tuple				,    _A      :    Optional[int]=0.5				,    _A      :    List[str]=5_1_2				,    _A      :    Union[str, Any]=1_0_2_4				,    _A      :    Tuple=8				,    **_A      :    Tuple				,    )						->						Tuple:
         super().__init__(temperature=_A   )

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         if semantic_config is None:
             snake_case      =					{}
             logger.info("semantic_config is None. initializing the semantic model with default values."   )

         if coarse_acoustics_config is None:
             snake_case      =					{}
             logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."   )

         if fine_acoustics_config is None:
             snake_case      =					{}
             logger.info("fine_acoustics_config is None. initializing the fine model with default values."   )

         snake_case      =					BarkSemanticGenerationConfig(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, Any]:
         return cls(
             semantic_config=semantic_config.to_dict()				,    coarse_acoustics_config=coarse_acoustics_config.to_dict()				,    fine_acoustics_config=fine_acoustics_config.to_dict()				,    **_A				,    )





     def 		UpperCAmelCase(self      :    List[str]   )						->						int:
         snake_case      =					copy.deepcopy(self.__dict__   )

         snake_case      =					self.semantic_config.to_dict()
         snake_case      =					self.coarse_acoustics_config.to_dict()
         snake_case      =					self.fine_acoustics_config.to_dict()

         snake_case      =					self.__class__.model_type
         return output

