

"""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:
   _a       =							False


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




def   _A  (  )	->						int:
   '''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   _A  (  UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       Optional[Any])	->						int:
   '''simple docstring'''


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

   __lowercase								=						image_tensor.unsqueeze(0)
   __lowercase								=						torch.nn.functional.unfold(UpperCamelCase_,			(patch_height, patch_width),			stride=(patch_height, patch_width))
   __lowercase								=						patches.reshape(image_tensor.size(0),			image_tensor.size(1),			UpperCamelCase_,			UpperCamelCase_,			-1)
   __lowercase								=						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   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       int = 36,			UpperCamelCase_     :       str = "black",			UpperCamelCase_     :       str = "white",			UpperCamelCase_     :       int = 5,			UpperCamelCase_     :       int = 5,			UpperCamelCase_     :       int = 5,			UpperCamelCase_     :       int = 5,			UpperCamelCase_     :       Optional[bytes] = None,			UpperCamelCase_     :       Optional[str] = None,			)	->						Image.Image:
   '''simple docstring'''


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

   __lowercase								=						textwrap.TextWrapper(width=80)
   __lowercase								=						wrapper.wrap(text=UpperCamelCase_)
   __lowercase								=						"\n".join(UpperCamelCase_)

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

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

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




def   _A  (  UpperCamelCase_     :       np.ndarray,			UpperCamelCase_     :       str,			**UpperCamelCase_     :       Union[str, Any])	->						List[str]:
   '''simple docstring'''


   requires_backends(UpperCamelCase_,			"vision")

   # Convert to PIL image if necessary
   __lowercase								=						to_pil_image(UpperCamelCase_)

   __lowercase								=						render_text(UpperCamelCase_,			**UpperCamelCase_)
   __lowercase								=						max(header_image.width,			image.width)

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

   __lowercase								=						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
   __lowercase								=						to_numpy_array(UpperCamelCase_)

   if infer_channel_dimension_format(UpperCamelCase_) == ChannelDimension.LAST:
      __lowercase								=						to_channel_dimension_format(UpperCamelCase_,			ChannelDimension.LAST)

   return new_image




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					List[Any]   		=	["flattened_patches"]


 def __init__(			self							:			List[str],					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Dict[str, int] = None,					UpperCAmelCase__							:			int = 2_0_4_8,					UpperCAmelCase__							:			bool = False,					**UpperCAmelCase__							:			Dict,					):
    super().__init__(**UpperCAmelCase__	)
    __lowercase								=						patch_size if patch_size is not None else {"height": 1_6, "width": 1_6}
    __lowercase								=						do_normalize
    __lowercase								=						do_convert_rgb
    __lowercase								=						max_patches
    __lowercase								=						is_vqa


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			np.ndarray,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			dict,					**UpperCAmelCase__							:			Dict	):
    requires_backends(self.extract_flattened_patches,					"torch"	)
    _check_torch_version()

    # convert to torch
    __lowercase								=						to_channel_dimension_format(UpperCAmelCase__,					ChannelDimension.FIRST	)
    __lowercase								=						torch.from_numpy(UpperCAmelCase__	)

    __lowercase		,__lowercase								=						patch_size["height"], patch_size["width"]
    __lowercase		,__lowercase								=						get_image_size(UpperCAmelCase__	)

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

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

    # [1, rows, columns, patch_height * patch_width * image_channels]
    __lowercase								=						torch_extract_patches(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)

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

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

    # [rows * columns, 1]
    __lowercase								=						torch.arange(UpperCAmelCase__	).reshape([rows, 1]	).repeat(1,					UpperCAmelCase__	).reshape([rows * columns, 1]	)
    __lowercase								=						torch.arange(UpperCAmelCase__	).reshape([1, columns]	).repeat(UpperCAmelCase__,					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]
    __lowercase								=						row_ids.to(torch.floataa	)
    __lowercase								=						col_ids.to(torch.floataa	)

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

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

    __lowercase								=						to_numpy_array(UpperCAmelCase__	)

    return result


 def 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			np.ndarray,					UpperCAmelCase__							:			Optional[Union[str, ChannelDimension]] = None,					**UpperCAmelCase__							:			Any	):
    if image.dtype == np.uinta:
       __lowercase								=						image.astype(np.floataa	)

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

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



 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			ImageInput,					UpperCAmelCase__							:			Optional[str] = None,					UpperCAmelCase__							:			bool = None,					UpperCAmelCase__							:			Optional[bool] = None,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			Optional[Dict[str, int]] = None,					UpperCAmelCase__							:			Optional[Union[str, TensorType]] = None,					UpperCAmelCase__							:			ChannelDimension = ChannelDimension.FIRST,					**UpperCAmelCase__							:			Any,					):
    __lowercase								=						do_normalize if do_normalize is not None else self.do_normalize
    __lowercase								=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
    __lowercase								=						patch_size if patch_size is not None else self.patch_size
    __lowercase								=						max_patches if max_patches is not None else self.max_patches
    __lowercase								=						self.is_vqa

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

    __lowercase								=						make_list_of_images(UpperCAmelCase__	)

    if not valid_images(UpperCAmelCase__	):
       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:
       __lowercase								=						[convert_to_rgb(UpperCAmelCase__	) for image in images]

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

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

       if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
          __lowercase								=						[header_text] * len(UpperCAmelCase__	)

       __lowercase								=						[
           render_header(UpperCAmelCase__,					header_text[i],					font_bytes=UpperCAmelCase__,					font_path=UpperCAmelCase__	)
           for i, image in enumerate(UpperCAmelCase__	)
       ]

    if do_normalize:
       __lowercase								=						[self.normalize(image=UpperCAmelCase__	) for image in images]

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

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

    __lowercase								=						BatchFeature(
        data={"flattened_patches": images, "attention_mask": attention_masks},					tensor_type=UpperCAmelCase__	)

    return encoded_outputs






'''simple docstring'''




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase			:				Union[str, Any]    		=			{
    'text_branch': 'text_model',
    'audio_branch': 'audio_model.audio_encoder',
    'attn': 'attention.self',
    'self.proj': 'output.dense',
    'attention.self_mask': 'attn_mask',
    'mlp.fc1': 'intermediate.dense',
    'mlp.fc2': 'output.dense',
    'norm1': 'layernorm_before',
    'norm2': 'layernorm_after',
    'bn0': 'batch_norm',
}

lowercase			:				Tuple    		=			AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   Tuple					=						create_model(
        '''HTSAT-tiny'''		,				'''roberta'''		,				snake_case__		,				precision='''fp32'''		,				device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''		,				enable_fusion=snake_case__		,				fusion_type='''aff_2d''' if enable_fusion else None		,				)
    return model, model_cfg







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						{}

    A       :   str					=						R'''.*sequential.(\d+).*'''
    A       :   Union[str, Any]					=						R'''.*_projection.(\d+).*'''

    for key, value in state_dict.items():
        # check if any key needs to be modified
        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
            if key_to_modify in key:
                A       :   Any					=						key.replace(snake_case__		,				snake_case__						)

        if re.match(snake_case__		,				snake_case__						):
            # replace sequential layers with list
            A       :   Any					=						re.match(snake_case__		,				snake_case__						).group(1						)

            A       :   List[str]					=						key.replace(F'sequential.{sequential_layer}.'		,				F'layers.{int(snake_case__						)//3}.linear.'						)
        elif re.match(snake_case__		,				snake_case__						):
            A       :   Union[str, Any]					=						int(re.match(snake_case__		,				snake_case__						).group(1						)						)

            # Because in CLAP they use `nn.Sequential`...
            A       :   str					=						1 if projecton_layer == 0 else 2

            A       :   Optional[Any]					=						key.replace(F'_projection.{projecton_layer}.'		,				F'_projection.linear{transformers_projection_layer}.'						)

        if "audio" and "qkv" in key:
            # split qkv into query key and value
            A       :   int					=						value
            A       :   List[Any]					=						mixed_qkv.size(0						) // 3

            A       :   Union[str, Any]					=						mixed_qkv[:qkv_dim]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim : qkv_dim * 2]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim * 2 :]

            A       :   Tuple					=						query_layer
            A       :   Union[str, Any]					=						key_layer
            A       :   Optional[int]					=						value_layer
        else:
            A       :   Dict					=						value

    return model_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   int					=						init_clap(snake_case__		,				enable_fusion=snake_case__						)

    clap_model.eval()
    A       :   str					=						clap_model.state_dict()
    A       :   Union[str, Any]					=						rename_state_dict(snake_case__						)

    A       :   Tuple					=						ClapConfig()
    A       :   str					=						enable_fusion
    A       :   str					=						ClapModel(snake_case__						)

    # ignore the spectrogram embedding layer
    model.load_state_dict(snake_case__		,				strict=snake_case__						)

    model.save_pretrained(snake_case__						)
    transformers_config.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				List[str]    		=			argparse.ArgumentParser()
      parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
      parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
      lowercase			:				Tuple    		=			parser.parse_args()

      convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)




