
'''simple docstring'''
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase__											={
    '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__											=AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')






def      UpperCamelCase_				(      A__  ,      A__=False						):
	a_      				=						create_model(
	    """HTSAT-tiny"""  ,      """roberta"""  ,      A__  ,      precision="""fp32"""  ,      device="""cuda:0""" if torch.cuda.is_available() else """cpu"""  ,      enable_fusion=A__  ,      fusion_type="""aff_2d""" if enable_fusion else None  ,      )
	return model, model_cfg






def      UpperCamelCase_				(      A__						):
	a_      				=						{}

	a_      				=						r""".*sequential.(\d+).*"""
	a_      				=						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_      				=						key.replace(A__  ,      A__						)

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

			a_      				=						key.replace(F'''sequential.{sequential_layer}.'''  ,      F'''layers.{int(A__						)//3}.linear.'''						)
		elif re.match(A__  ,      A__						):
			a_      				=						int(re.match(A__  ,      A__						).group(1						)						)

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

			a_      				=						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_      				=						value
			a_      				=						mixed_qkv.size(0						) // 3

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

			a_      				=						query_layer
			a_      				=						key_layer
			a_      				=						value_layer
		else:
			a_      				=						value

	return model_state_dict






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__=False						):
	a_      				=						init_clap(A__  ,      enable_fusion=A__						)

	clap_model.eval()
	a_      				=						clap_model.state_dict()
	a_      				=						rename_state_dict(A__						)

	a_      				=						ClapConfig()
	a_      				=						enable_fusion
	a_      				=						ClapModel(A__						)

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

	model.save_pretrained(A__						)
	transformers_config.save_pretrained(A__						)


if __name__ == "__main__":
		lowercase__											=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__											=parser.parse_args()

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







import qiskit





def  __magic_name__  (				lowercase				,       lowercase				)				-> qiskit.result.counts.Counts:

							"""simple docstring"""



							lowercase_	:   Dict  				=     qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							lowercase_	:   str  				=     qiskit.QuantumCircuit(lowercase				,       lowercase				)

							# Map the quantum measurement to the classical bits
							circuit.measure([0]				,       [0]				)

							# Execute the circuit on the simulator
							lowercase_	:   List[Any]  				=     qiskit.execute(lowercase				,       lowercase				,       shots=1000				)

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(lowercase				)


if __name__ == "__main__":
							print(F'''Total count for various states are: {single_qubit_measure(1, 1)}''')