



'''simple docstring'''


import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
  import textwrap

  from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
  import torch

  from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
  lowercase							       =			False


lowercase							       =			logging.get_logger(__name__)
lowercase							       =			'''ybelkada/fonts'''






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
          raise ImportError(
              F"""You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use """
              "Pix2StructImageProcessor. Please upgrade torch."       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     requires_backends(lowercase__       ,	["torch"]       )
     _check_torch_version()

     a_       		=image_tensor.unsqueeze(0       )
     a_       		=torch.nn.functional.unfold(lowercase__       ,	(patch_height, patch_width)       ,	stride=(patch_height, patch_width)       )
     a_       		=patches.reshape(image_tensor.size(0       )       ,	image_tensor.size(1       )       ,	lowercase__       ,	lowercase__       ,	-1       )
     a_       		=patches.permute(0       ,	4       ,	2       ,	3       ,	1       ).reshape(
         image_tensor.size(2       ) // patch_height       ,	image_tensor.size(3       ) // patch_width       ,	image_tensor.size(1       ) * patch_height * patch_width       ,	)
     return patches.unsqueeze(0       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__ = 3_6       ,	lowercase__ = "black"       ,	lowercase__ = "white"       ,	lowercase__ = 5       ,	lowercase__ = 5       ,	lowercase__ = 5       ,	lowercase__ = 5       ,	lowercase__ = None       ,	lowercase__ = None       ,	):


     '''simple docstring'''







     requires_backends(lowercase__       ,	"vision"       )
     # Add new lines so that each line is no more than 80 characters.

     a_       		=textwrap.TextWrapper(width=8_0       )
     a_       		=wrapper.wrap(text=lowercase__       )
     a_       		="\n".join(lowercase__       )

     if font_bytes is not None and font_path is None:
          a_       		=io.BytesIO(lowercase__       )
     elif font_path is not None:
          a_       		=font_path
     else:
          a_       		=hf_hub_download(lowercase__       ,	"Arial.TTF"       )
     a_       		=ImageFont.truetype(lowercase__       ,	encoding="UTF-8"       ,	size=lowercase__       )

     # Use a temporary canvas to determine the width and height in pixels when
     # rendering the text.
     a_       		=ImageDraw.Draw(Image.new("RGB"       ,	(1, 1)       ,	lowercase__       )       )
     a_		,						a_		,						a_		,						a_       		=temp_draw.textbbox((0, 0)       ,	lowercase__       ,	lowercase__       )

     # Create the actual image with a bit of padding around the text.
     a_       		=text_width + left_padding + right_padding
     a_       		=text_height + top_padding + bottom_padding
     a_       		=Image.new("RGB"       ,	(image_width, image_height)       ,	lowercase__       )
     a_       		=ImageDraw.Draw(lowercase__       )
     draw.text(xy=(left_padding, top_padding)       ,	text=lowercase__       ,	fill=lowercase__       ,	font=lowercase__       )
     return image






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	**lowercase__       ):


     '''simple docstring'''







     requires_backends(lowercase__       ,	"vision"       )

     # Convert to PIL image if necessary
     a_       		=to_pil_image(lowercase__       )

     a_       		=render_text(lowercase__       ,	**lowercase__       )
     a_       		=max(header_image.width       ,	image.width       )

     a_       		=int(image.height * (new_width / image.width)       )
     a_       		=int(header_image.height * (new_width / header_image.width)       )

     a_       		=Image.new("RGB"       ,	(new_width, new_height + new_header_height)       ,	"white"       )
     new_image.paste(header_image.resize((new_width, new_header_height)       )       ,	(0, 0)       )
     new_image.paste(image.resize((new_width, new_height)       )       ,	(0, new_header_height)       )

     # Convert back to the original framework if necessary
     a_       		=to_numpy_array(lowercase__       )

     if infer_channel_dimension_format(lowercase__       ) == ChannelDimension.LAST:
          a_       		=to_channel_dimension_format(lowercase__       ,	ChannelDimension.LAST       )

     return new_image







class 	UpperCAmelCase	(					__a):


 '''simple docstring'''




 __magic_name__					: int  =      ["flattened_patches"]




 def __init__(   self							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = True							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = 2_0_4_8							,  lowerCAmelCase_ = False							,  **lowerCAmelCase_							,  ) ->      None:



      """simple docstring"""







      super().__init__(**lowerCAmelCase_)
      a_       		=patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
      a_       		=do_normalize
      a_       		=do_convert_rgb
      a_       		=max_patches
      a_       		=is_vqa




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      np.ndarray:



      """simple docstring"""







      requires_backends(self.extract_flattened_patches							,  "torch")
      _check_torch_version()

      # convert to torch
      a_       		=to_channel_dimension_format(lowerCAmelCase_							,  ChannelDimension.FIRST)
      a_       		=torch.from_numpy(lowerCAmelCase_)

      a_		,						a_       		=patch_size["height"], patch_size["width"]
      a_		,						a_       		=get_image_size(lowerCAmelCase_)

      # maximize scale s.t.
      a_       		=math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width))
      a_       		=max(min(math.floor(scale * image_height / patch_height)							,  lowerCAmelCase_)							,  1)
      a_       		=max(min(math.floor(scale * image_width / patch_width)							,  lowerCAmelCase_)							,  1)
      a_       		=max(num_feasible_rows * patch_height							,  1)
      a_       		=max(num_feasible_cols * patch_width							,  1)

      a_       		=torch.nn.functional.interpolate(
          image.unsqueeze(0)							,  size=(resized_height, resized_width)							,  mode="bilinear"							,  align_corners=lowerCAmelCase_							,  antialias=lowerCAmelCase_							,  ).squeeze(0)

      # [1, rows, columns, patch_height * patch_width * image_channels]
      a_       		=torch_extract_patches(lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_)

      a_       		=patches.shape
      a_       		=patches_shape[1]
      a_       		=patches_shape[2]
      a_       		=patches_shape[3]

      # [rows * columns, patch_height * patch_width * image_channels]
      a_       		=patches.reshape([rows * columns, depth])

      # [rows * columns, 1]
      a_       		=torch.arange(lowerCAmelCase_).reshape([rows, 1]).repeat(1							,  lowerCAmelCase_).reshape([rows * columns, 1])
      a_       		=torch.arange(lowerCAmelCase_).reshape([1, columns]).repeat(lowerCAmelCase_							,  1).reshape([rows * columns, 1])

      # Offset by 1 so the ids do not contain zeros, which represent padding.
      row_ids += 1
      col_ids += 1

      # Prepare additional patch features.
      # [rows * columns, 1]
      a_       		=row_ids.to(torch.floataa)
      a_       		=col_ids.to(torch.floataa)

      # [rows * columns, 2 + patch_height * patch_width * image_channels]
      a_       		=torch.cat([row_ids, col_ids, patches]							,  -1)

      # [max_patches, 2 + patch_height * patch_width * image_channels]
      a_       		=torch.nn.functional.pad(lowerCAmelCase_							,  [0, 0, 0, max_patches - (rows * columns)]).float()

      a_       		=to_numpy_array(lowerCAmelCase_)

      return result




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  **lowerCAmelCase_) ->      np.ndarray:



      """simple docstring"""







      if image.dtype == np.uinta:
           a_       		=image.astype(np.floataa)

      # take mean across the whole `image`
      a_       		=np.mean(lowerCAmelCase_)
      a_       		=np.std(lowerCAmelCase_)
      a_       		=max(lowerCAmelCase_							,  1.0 / math.sqrt(np.prod(image.shape)))

      return normalize(lowerCAmelCase_							,  mean=lowerCAmelCase_							,  std=lowerCAmelCase_							,  **lowerCAmelCase_)




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = ChannelDimension.FIRST							,  **lowerCAmelCase_							,  ) ->      ImageInput:



      """simple docstring"""







      a_       		=do_normalize if do_normalize is not None else self.do_normalize
      a_       		=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
      a_       		=patch_size if patch_size is not None else self.patch_size
      a_       		=max_patches if max_patches is not None else self.max_patches
      a_       		=self.is_vqa

      if kwargs.get("data_format"							,  lowerCAmelCase_) is not None:
           raise ValueError("data_format is not an accepted input as the outputs are ")

      a_       		=make_list_of_images(lowerCAmelCase_)

      if not valid_images(lowerCAmelCase_):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray.")

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
           a_       		=[convert_to_rgb(lowerCAmelCase_) for image in images]

      # All transformations expect numpy arrays.
      a_       		=[to_numpy_array(lowerCAmelCase_) for image in images]

      if is_vqa:
           if header_text is None:
                raise ValueError("A header text must be provided for VQA models.")
           a_       		=kwargs.pop("font_bytes"							,  lowerCAmelCase_)
           a_       		=kwargs.pop("font_path"							,  lowerCAmelCase_)

           if isinstance(lowerCAmelCase_							,  lowerCAmelCase_):
                a_       		=[header_text] * len(lowerCAmelCase_)

           a_       		=[
               render_header(lowerCAmelCase_							,  header_text[i]							,  font_bytes=lowerCAmelCase_							,  font_path=lowerCAmelCase_)
               for i, image in enumerate(lowerCAmelCase_)
           ]

      if do_normalize:
           a_       		=[self.normalize(image=lowerCAmelCase_) for image in images]

      # convert to torch tensor and permute
      a_       		=[
          self.extract_flattened_patches(image=lowerCAmelCase_							,  max_patches=lowerCAmelCase_							,  patch_size=lowerCAmelCase_)
          for image in images
      ]

      # create attention mask in numpy
      a_       		=[(image.sum(axis=-1) != 0).astype(np.floataa) for image in images]

      a_       		=BatchFeature(
          data={"flattened_patches": images, "attention_mask": attention_masks}							,  tensor_type=lowerCAmelCase_)

      return encoded_outputs







'''simple docstring'''


import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase							       =			logging.get_logger(__name__)

set_seed(770)


lowercase							       =			{
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


lowercase							       =			{
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

lowercase							       =			os.path.dirname(os.path.abspath(__file__))
lowercase							       =			os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowercase							       =			os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ):


     '''simple docstring'''







     a_       		=model_type
     if use_small:
          key += "_small"
     return os.path.join(lowercase__       ,	REMOTE_MODEL_PATHS[key]["file_name"]       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     os.makedirs(lowercase__       ,	exist_ok=lowercase__       )
     hf_hub_download(repo_id=lowercase__       ,	filename=lowercase__       ,	local_dir=lowercase__       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__=False       ,	lowercase__="text"       ):


     '''simple docstring'''







     if model_type == "text":
          a_       		=BarkSemanticModel
          a_       		=BarkSemanticConfig
          a_       		=BarkSemanticGenerationConfig
     elif model_type == "coarse":
          a_       		=BarkCoarseModel
          a_       		=BarkCoarseConfig
          a_       		=BarkCoarseGenerationConfig
     elif model_type == "fine":
          a_       		=BarkFineModel
          a_       		=BarkFineConfig
          a_       		=BarkFineGenerationConfig
     else:
          raise NotImplementedError()
     a_       		=F"""{model_type}_small""" if use_small else model_type
     a_       		=REMOTE_MODEL_PATHS[model_key]
     if not os.path.exists(lowercase__       ):
          logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""       )
          _download(model_info["repo_id"]       ,	model_info["file_name"]       )
     a_       		=torch.load(lowercase__       ,	map_location=lowercase__       )
     # this is a hack
     a_       		=checkpoint["model_args"]
     if "input_vocab_size" not in model_args:
          a_       		=model_args["vocab_size"]
          a_       		=model_args["vocab_size"]
          del model_args["vocab_size"]

     # convert Bark model arguments to HF Bark model arguments
     a_       		=model_args.pop("n_head"       )
     a_       		=model_args.pop("n_embd"       )
     a_       		=model_args.pop("n_layer"       )

     a_       		=ConfigClass(**checkpoint["model_args"]       )
     a_       		=ModelClass(config=lowercase__       )
     a_       		=GenerationConfigClass()

     a_       		=model_generation_config
     a_       		=checkpoint["model"]
     # fixup checkpoint
     a_       		="_orig_mod."
     for k, v in list(state_dict.items()       ):
          if k.startswith(lowercase__       ):
               # replace part of the key with corresponding layer name in HF implementation
               a_       		=k[len(lowercase__       ) :]
               for old_layer_name in new_layer_name_dict:
                    a_       		=new_k.replace(lowercase__       ,	new_layer_name_dict[old_layer_name]       )

               a_       		=state_dict.pop(lowercase__       )

     a_       		=set(state_dict.keys()       ) - set(model.state_dict().keys()       )
     a_       		={k for k in extra_keys if not k.endswith(".attn.bias"       )}
     a_       		=set(model.state_dict().keys()       ) - set(state_dict.keys()       )
     a_       		={k for k in missing_keys if not k.endswith(".attn.bias"       )}
     if len(lowercase__       ) != 0:
          raise ValueError(F"""extra keys found: {extra_keys}"""       )
     if len(lowercase__       ) != 0:
          raise ValueError(F"""missing keys: {missing_keys}"""       )
     model.load_state_dict(lowercase__       ,	strict=lowercase__       )
     a_       		=model.num_parameters(exclude_embeddings=lowercase__       )
     a_       		=checkpoint["best_val_loss"].item()
     logger.info(F"""model loaded: {round(n_params/1E6       ,	1       )}M params, {round(lowercase__       ,	3       )} loss"""       )
     model.eval()
     model.to(lowercase__       )
     del checkpoint, state_dict

     return model






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ,	lowercase__="text"       ):


     '''simple docstring'''







     if model_type not in ("text", "coarse", "fine"):
          raise NotImplementedError()

     a_       		="cpu"  # do conversion on cpu

     a_       		=_get_ckpt_path(lowercase__       ,	use_small=lowercase__       )
     a_       		=_load_model(lowercase__       ,	lowercase__       ,	model_type=lowercase__       ,	use_small=lowercase__       )

     # load bark initial model
     a_       		=_bark_load_model(lowercase__       ,	"cpu"       ,	model_type=lowercase__       ,	use_small=lowercase__       )

     if model_type == "text":
          a_       		=bark_model["model"]

     if model.num_parameters(exclude_embeddings=lowercase__       ) != bark_model.get_num_params():
          raise ValueError("initial and new models don't have the same number of parameters"       )

     # check if same output as the bark model
     a_       		=5
     a_       		=1_0

     if model_type in ["text", "coarse"]:
          a_       		=torch.randint(2_5_6       ,	(batch_size, sequence_length)       ,	dtype=torch.int       )
          a_       		=bark_model(lowercase__       )[0]

          a_       		=model(lowercase__       )

          # take last logits
          a_       		=output_new_model_total.logits[:, [-1], :]

     else:
          a_       		=3
          a_       		=8
          a_       		=torch.randint(2_5_6       ,	(batch_size, sequence_length, n_codes_total)       ,	dtype=torch.int       )

          a_       		=model(lowercase__       ,	lowercase__       )
          a_       		=bark_model(lowercase__       ,	lowercase__       )

          a_       		=output_new_model_total.logits

     # output difference should come from the difference of self-attention implementation design
     if output_new_model.shape != output_old_model.shape:
          raise ValueError("initial and new outputs don't have the same shape"       )
     if (output_new_model - output_old_model).abs().max().item() > 1E-3:
          raise ValueError("initial and new outputs are not equal"       )

     Path(lowercase__       ).mkdir(exist_ok=lowercase__       )
     model.save_pretrained(lowercase__       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	):


     '''simple docstring'''







     a_       		=os.path.join(lowercase__       ,	lowercase__       )

     a_       		=BarkSemanticConfig.from_pretrained(os.path.join(lowercase__       ,	"config.json"       )       )
     a_       		=BarkCoarseConfig.from_pretrained(os.path.join(lowercase__       ,	"config.json"       )       )
     a_       		=BarkFineConfig.from_pretrained(os.path.join(lowercase__       ,	"config.json"       )       )
     a_       		=EncodecConfig.from_pretrained("facebook/encodec_24khz"       )

     a_       		=BarkSemanticModel.from_pretrained(lowercase__       )
     a_       		=BarkCoarseModel.from_pretrained(lowercase__       )
     a_       		=BarkFineModel.from_pretrained(lowercase__       )
     a_       		=EncodecModel.from_pretrained("facebook/encodec_24khz"       )

     a_       		=BarkConfig.from_sub_model_configs(
         lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       )

     a_       		=BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config       ,	coarseAcoustic.generation_config       ,	fineAcoustic.generation_config       )

     a_       		=BarkModel(lowercase__       )

     a_       		=semantic
     a_       		=coarseAcoustic
     a_       		=fineAcoustic
     a_       		=codec

     a_       		=bark_generation_config

     Path(lowercase__       ).mkdir(exist_ok=lowercase__       )
     bark.save_pretrained(lowercase__       ,	repo_id=lowercase__       ,	push_to_hub=lowercase__       )


if __name__ == "__main__":
  lowercase							       =			argparse.ArgumentParser()
  # Required parameters

  parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
  parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

  lowercase							       =			parser.parse_args()

  load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)


