"""simple docstring"""







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_a					:					List[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",
}

_a					:					Tuple=			AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")
def   __UpperCAmelCase    (   UpperCAmelCase_      ,							UpperCAmelCase_=False  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							str						=			create_model(
	    'HTSAT-tiny'      ,							'roberta'      ,							__SCREAMING_SNAKE_CASE      ,							precision='fp32'      ,							device='cuda:0' if torch.cuda.is_available() else 'cpu'      ,							enable_fusion=__SCREAMING_SNAKE_CASE      ,							fusion_type='aff_2d' if enable_fusion else None      ,							)
	return model, model_cfg
def   __UpperCAmelCase    (   UpperCAmelCase_  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			{}

	__snake_case      :							List[Any]						=			R""".*sequential.(\d+).*"""
	__snake_case      :							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:
				__snake_case      :							Optional[Any]						=			key.replace(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE  )

		if re.match(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE  ):
			# replace sequential layers with list
			__snake_case      :							Optional[Any]						=			re.match(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE  ).group(1  )

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

			# Because in CLAP they use `nn.Sequential`...
			__snake_case      :							List[Any]						=			1 if projecton_layer == 0 else 2

			__snake_case      :							Tuple						=			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
			__snake_case      :							Optional[int]						=			value
			__snake_case      :							Any						=			mixed_qkv.size(0  ) // 3

			__snake_case      :							List[Any]						=			mixed_qkv[:qkv_dim]
			__snake_case      :							Tuple						=			mixed_qkv[qkv_dim : qkv_dim * 2]
			__snake_case      :							List[Any]						=			mixed_qkv[qkv_dim * 2 :]

			__snake_case      :							Any						=			query_layer
			__snake_case      :							Dict						=			key_layer
			__snake_case      :							Optional[Any]						=			value_layer
		else:
			__snake_case      :							List[str]						=			value

	return model_state_dict
def   __UpperCAmelCase    (   UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_=False  )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							List[str]						=			init_clap(__SCREAMING_SNAKE_CASE      ,							enable_fusion=__SCREAMING_SNAKE_CASE  )

	clap_model.eval()
	__snake_case      :							Tuple						=			clap_model.state_dict()
	__snake_case      :							Union[str, Any]						=			rename_state_dict(__SCREAMING_SNAKE_CASE  )

	__snake_case      :							List[Any]						=			ClapConfig()
	__snake_case      :							Tuple						=			enable_fusion
	__snake_case      :							Any						=			ClapModel(__SCREAMING_SNAKE_CASE  )

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

	model.save_pretrained(__SCREAMING_SNAKE_CASE  )
	transformers_config.save_pretrained(__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
	_a					:					Union[str, 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")
	_a					:					Optional[Any]=			parser.parse_args()

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






"""simple docstring"""







import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class 		UpperCamelCase      (   lowercase						):



				@require_torch
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
					# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
					# while running an external program

					# python one-liner segments

					# this must be loaded before socket.socket is monkey-patched
					__snake_case      :							Any						=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

					__snake_case      :							Tuple						=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

					__snake_case      :							int						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

					# Force fetching the files so that we can use the cache
					__snake_case      :							int						=			'hf-internal-testing/tiny-random-bert'
					BertConfig.from_pretrained(_A)
					BertModel.from_pretrained(_A)
					BertTokenizer.from_pretrained(_A)
					pipeline(task='fill-mask'     ,  model=_A)

					# baseline - just load from_pretrained with normal network
					__snake_case      :							Union[str, Any]						=			[sys.executable, '-c', '\n'.join([load, run, mock])]

					# should succeed
					__snake_case      :							Union[str, Any]						=			self.get_env()
					# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
					__snake_case      :							str						=			'1'
					__snake_case      :							Union[str, Any]						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())



				@require_torch
				def  _lowercase					(self    :				Union[str, Any])   ->     Union[str, Any]:
					# python one-liner segments
					# this must be loaded before socket.socket is monkey-patched
					__snake_case      :							Optional[Any]						=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

					__snake_case      :							Optional[Any]						=			'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

					__snake_case      :							Union[str, Any]						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

					# Force fetching the files so that we can use the cache
					__snake_case      :							str						=			'hf-internal-testing/tiny-random-bert'
					BertConfig.from_pretrained(_A)
					BertModel.from_pretrained(_A)
					BertTokenizer.from_pretrained(_A)
					pipeline(task='fill-mask'     ,  model=_A)

					# baseline - just load from_pretrained with normal network
					__snake_case      :							Any						=			[sys.executable, '-c', '\n'.join([load, run, mock])]

					# should succeed
					__snake_case      :							int						=			self.get_env()
					__snake_case      :							Tuple						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())



				@require_torch
				def  _lowercase					(self    :				int)   ->     Any:
					# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
					# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
					# while running an external program

					# python one-liner segments

					# this must be loaded before socket.socket is monkey-patched
					__snake_case      :							int						=			'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

					__snake_case      :							int						=			'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

					__snake_case      :							Optional[int]						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

					# baseline - just load from_pretrained with normal network
					__snake_case      :							Optional[Any]						=			[sys.executable, '-c', '\n'.join([load, run])]

					# should succeed
					__snake_case      :							Optional[int]						=			self.get_env()
					__snake_case      :							Dict						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())

					# next emulate no network
					__snake_case      :							Optional[Any]						=			[sys.executable, '-c', '\n'.join([load, mock, run])]

					# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
					# env["TRANSFORMERS_OFFLINE"] = "0"
					# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
					# self.assertEqual(result.returncode, 1, result.stderr)

					# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
					__snake_case      :							Union[str, Any]						=			'1'
					__snake_case      :							str						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())



				@require_torch
				def  _lowercase					(self    :				str)   ->     Dict:
					__snake_case      :							Dict						=			'\nfrom transformers import pipeline\n        '
					__snake_case      :							List[Any]						=			'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

					__snake_case      :							List[str]						=			'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
					__snake_case      :							str						=			self.get_env()
					__snake_case      :							Tuple						=			'1'
					__snake_case      :							Tuple						=			[sys.executable, '-c', '\n'.join([load, mock, run])]
					__snake_case      :							Optional[Any]						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  1     ,  result.stderr)
					self.assertIn(
					    'You cannot infer task automatically within `pipeline` when using offline mode'     ,  result.stderr.decode().replace('\n'     ,  '')     ,  )






				@require_torch
				def  _lowercase					(self    :				int)   ->     Optional[Any]:
					__snake_case      :							int						=			'\nfrom transformers import AutoModel\n        '
					__snake_case      :							str						=			'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

					# baseline - just load from_pretrained with normal network
					__snake_case      :							str						=			[sys.executable, '-c', '\n'.join([load, run])]

					# should succeed
					__snake_case      :							str						=			self.get_env()
					__snake_case      :							Dict						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())

					# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
					__snake_case      :							List[str]						=			'1'
					__snake_case      :							Optional[int]						=			subprocess.run(_A     ,  env=_A     ,  check=_A     ,  capture_output=_A)
					self.assertEqual(result.returncode     ,  0     ,  result.stderr)
					self.assertIn('success'     ,  result.stdout.decode())





