


'''simple docstring'''

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
	import PIL

if is_torch_available():
	import torch


_UpperCAmelCase				: str							    =				logging.get_logger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[Any]	=	['pixel_values']




				def __init__(self       ,			__lowercase = True       ,			__lowercase = None       ,			__lowercase = PILImageResampling.BILINEAR       ,			__lowercase = True       ,			__lowercase = 1 / 2_55       ,			__lowercase = True       ,			__lowercase = None       ,			__lowercase = True       ,			**__lowercase       ,			):
										super().__init__(**__lowercase		)
										__lowerCAmelCase							       =				size if size is not None else {'''shortest_edge''': 2_24}
										__lowerCAmelCase							       =				get_size_dict(__lowercase       ,			default_to_square=__lowercase		)
										__lowerCAmelCase							       =				crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
										__lowerCAmelCase							       =				get_size_dict(__lowercase       ,			param_name='''crop_size'''		)

										__lowerCAmelCase							       =				do_resize
										__lowerCAmelCase							       =				size
										__lowerCAmelCase							       =				resample
										__lowerCAmelCase							       =				do_rescale
										__lowerCAmelCase							       =				rescale_factor
										__lowerCAmelCase							       =				do_center_crop
										__lowerCAmelCase							       =				crop_size
										__lowerCAmelCase							       =				do_flip_channel_order




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = PIL.Image.BILINEAR       ,			__lowercase = None       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				get_size_dict(__lowercase       ,			default_to_square=__lowercase		)
										if "shortest_edge" not in size:
																raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""		)
										__lowerCAmelCase							       =				get_resize_output_image_size(__lowercase       ,			size=size['''shortest_edge''']       ,			default_to_square=__lowercase		)
										return resize(__lowercase       ,			size=__lowercase       ,			resample=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				get_size_dict(__lowercase		)
										if "height" not in size or "width" not in size:
																raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
										return center_crop(__lowercase       ,			size=(size['''height'''], size['''width'''])       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase       ,			):
										return rescale(__lowercase       ,			scale=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):
										return flip_channel_order(__lowercase       ,			data_format=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = ChannelDimension.FIRST       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				do_resize if do_resize is not None else self.do_resize
										__lowerCAmelCase							       =				resample if resample is not None else self.resample
										__lowerCAmelCase							       =				do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase							       =				rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase							       =				do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowerCAmelCase							       =				(
										    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
										)

										__lowerCAmelCase							       =				size if size is not None else self.size
										__lowerCAmelCase							       =				get_size_dict(__lowercase       ,			default_to_square=__lowercase		)
										__lowerCAmelCase							       =				crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase							       =				get_size_dict(__lowercase       ,			param_name='''crop_size'''		)

										__lowerCAmelCase							       =				make_list_of_images(__lowercase		)

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

										if do_resize and size is None:
																raise ValueError('''Size must be specified if do_resize is True.'''		)

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

										if do_center_crop and crop_size is None:
																raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

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

										if do_resize:
																__lowerCAmelCase							       =				[self.resize(image=__lowercase       ,			size=__lowercase       ,			resample=__lowercase		) for image in images]

										if do_center_crop:
																__lowerCAmelCase							       =				[self.center_crop(image=__lowercase       ,			size=__lowercase		) for image in images]

										if do_rescale:
																__lowerCAmelCase							       =				[self.rescale(image=__lowercase       ,			scale=__lowercase		) for image in images]

										# the pretrained checkpoints assume images are BGR, not RGB
										if do_flip_channel_order:
																__lowerCAmelCase							       =				[self.flip_channel_order(image=__lowercase		) for image in images]

										__lowerCAmelCase							       =				[to_channel_dimension_format(__lowercase       ,			__lowercase		) for image in images]

										__lowerCAmelCase							       =				{'''pixel_values''': images}
										return BatchFeature(data=__lowercase       ,			tensor_type=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):
										__lowerCAmelCase							       =				outputs.logits

										# Resize logits and compute semantic segmentation maps
										if target_sizes is not None:
																if len(__lowercase		) != len(__lowercase		):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''		)

																if is_torch_tensor(__lowercase		):
																						__lowerCAmelCase							       =				target_sizes.numpy()

																__lowerCAmelCase							       =				[]

																for idx in range(len(__lowercase		)		):
																						__lowerCAmelCase							       =				torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0		)       ,			size=target_sizes[idx]       ,			mode='''bilinear'''       ,			align_corners=__lowercase		)
																						__lowerCAmelCase							       =				resized_logits[0].argmax(dim=0		)
																						semantic_segmentation.append(__lowercase		)
										else:
																__lowerCAmelCase							       =				logits.argmax(dim=1		)
																__lowerCAmelCase							       =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

										return semantic_segmentation





'''simple docstring'''

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_UpperCAmelCase				: str							    =				{
    """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""",
}

_UpperCAmelCase				: Optional[int]							    =				AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				create_model(
						    '''HTSAT-tiny''',				'''roberta''',				lowerCamelCase,				precision='''fp32''',				device='''cuda:0''' if torch.cuda.is_available() else '''cpu''',				enable_fusion=lowerCamelCase,				fusion_type='''aff_2d''' if enable_fusion else None,				)
						return model, model_cfg

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				{}

						__lowerCAmelCase							       =				r'''.*sequential.(\d+).*'''
						__lowerCAmelCase							       =				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:
																								__lowerCAmelCase							       =				key.replace(lowerCamelCase,				lowerCamelCase)

												if re.match(lowerCamelCase,				lowerCamelCase):
																		# replace sequential layers with list
																		__lowerCAmelCase							       =				re.match(lowerCamelCase,				lowerCamelCase).group(1)

																		__lowerCAmelCase							       =				key.replace(F"""sequential.{sequential_layer}.""",				F"""layers.{int(lowerCamelCase)//3}.linear.""")
												elif re.match(lowerCamelCase,				lowerCamelCase):
																		__lowerCAmelCase							       =				int(re.match(lowerCamelCase,				lowerCamelCase).group(1))

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

																		__lowerCAmelCase							       =				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
																		__lowerCAmelCase							       =				value
																		__lowerCAmelCase							       =				mixed_qkv.size(0) // 3

																		__lowerCAmelCase							       =				mixed_qkv[:qkv_dim]
																		__lowerCAmelCase							       =				mixed_qkv[qkv_dim : qkv_dim * 2]
																		__lowerCAmelCase							       =				mixed_qkv[qkv_dim * 2 :]

																		__lowerCAmelCase							       =				query_layer
																		__lowerCAmelCase							       =				key_layer
																		__lowerCAmelCase							       =				value_layer
												else:
																		__lowerCAmelCase							       =				value

						return model_state_dict

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=False):
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				init_clap(lowerCamelCase,				enable_fusion=lowerCamelCase)

						clap_model.eval()
						__lowerCAmelCase							       =				clap_model.state_dict()
						__lowerCAmelCase							       =				rename_state_dict(lowerCamelCase)

						__lowerCAmelCase							       =				ClapConfig()
						__lowerCAmelCase							       =				enable_fusion
						__lowerCAmelCase							       =				ClapModel(lowerCamelCase)

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

						model.save_pretrained(lowerCamelCase)
						transformers_config.save_pretrained(lowerCamelCase)


if __name__ == "__main__":
	_UpperCAmelCase				: Any							    =				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""")
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()

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

