


'''simple docstring'''

import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def 							__magic_name__(						lowerCamelCase=None,				lowerCamelCase=None):
						return field(default_factory=lambda: default,				metadata=lowerCamelCase)


@dataclass
class       a__				:







				"""simple docstring"""



				__UpperCamelCase    :		str	=	field(
				    metadata={'help': 'The csv file to plot.'}							,						)
				__UpperCamelCase    :		bool	=	field(
				    default=__A							,						metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}							,						)
				__UpperCamelCase    :		bool	=	field(
				    default=__A							,						metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}							,						)
				__UpperCamelCase    :		bool	=	field(
				    default=__A							,						metadata={'help': 'Disable logarithmic scale when plotting'}							,						)
				__UpperCamelCase    :		bool	=	field(
				    default=__A							,						metadata={
				        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
				    }							,						)
				__UpperCamelCase    :		Optional[str]	=	field(
				    default=__A							,						metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}							,						)
				__UpperCamelCase    :		Optional[List[str]]	=	list_field(
				    default=__A							,						metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}      )

def 							__magic_name__(						lowerCamelCase):
						try:
												int(lowerCamelCase)
												return True
						except ValueError:
												return False

def 							__magic_name__(						lowerCamelCase):
						try:
												float(lowerCamelCase)
												return True
						except ValueError:
												return False


class       a__				:







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										__lowerCAmelCase							       =				args
										__lowerCAmelCase							       =				defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

										with open(self.args.csv_file       ,			newline=''''''		) as csv_file:
																__lowerCAmelCase							       =				csv.DictReader(__lowercase		)
																for row in reader:
																						__lowerCAmelCase							       =				row['''model''']
																						self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']		)		)
																						self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']		)		)
																						if can_convert_to_int(row['''result''']		):
																												# value is not None
																												__lowerCAmelCase							       =				int(row['''result''']		)
																						elif can_convert_to_float(row['''result''']		):
																												# value is not None
																												__lowerCAmelCase							       =				float(row['''result''']		)




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				plt.subplots()
										__lowerCAmelCase							       =				'''Time usage''' if self.args.is_time else '''Memory usage'''
										__lowerCAmelCase							       =				title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

										if not self.args.no_log_scale:
																# set logarithm scales
																ax.set_xscale('''log'''		)
																ax.set_yscale('''log'''		)

										for axis in [ax.xaxis, ax.yaxis]:
																axis.set_major_formatter(ScalarFormatter()		)

										for model_name_idx, model_name in enumerate(self.result_dict.keys()		):
																__lowerCAmelCase							       =				sorted(set(self.result_dict[model_name]['''bsz''']		)		)
																__lowerCAmelCase							       =				sorted(set(self.result_dict[model_name]['''seq_len''']		)		)
																__lowerCAmelCase							       =				self.result_dict[model_name]['''result''']

																((__lowerCAmelCase)       ,					(__lowerCAmelCase))							       =				(
																    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																)

																__lowerCAmelCase							       =				(
																    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																)

																for inner_loop_value in inner_loop_array:
																						if self.args.plot_along_batch:
																												__lowerCAmelCase							       =				np.asarray(
																												    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,			dtype=__lowercase       ,			)
																						else:
																												__lowerCAmelCase							       =				np.asarray(
																												    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,			dtype=np.floataa       ,			)

																						((__lowerCAmelCase)       ,					(__lowerCAmelCase))							       =				(
																						    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																						)

																						__lowerCAmelCase							       =				np.asarray(__lowercase       ,			__lowercase		)[: len(__lowercase		)]
																						plt.scatter(
																						    __lowercase       ,			__lowercase       ,			label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""		)
																						plt.plot(__lowercase       ,			__lowercase       ,			'''--'''		)

																title_str += F""" {label_model_name} vs."""

										__lowerCAmelCase							       =				title_str[:-4]
										__lowerCAmelCase							       =				'''Time in s''' if self.args.is_time else '''Memory in MB'''

										# plot
										plt.title(__lowercase		)
										plt.xlabel(__lowercase		)
										plt.ylabel(__lowercase		)
										plt.legend()

										if self.args.figure_png_file is not None:
																plt.savefig(self.args.figure_png_file		)
										else:
																plt.show()

def 							__magic_name__(						):
						__lowerCAmelCase							       =				HfArgumentParser(lowerCamelCase)
						__lowerCAmelCase							       =				parser.parse_args_into_dataclasses()[0]
						__lowerCAmelCase							       =				Plot(args=lowerCamelCase)
						plot.plot()


if __name__ == "__main__":
	main()





'''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)

