






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	int:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

						_snake_case						:					Any    =   (
						    ("layer.", "layer_"),
						    ("word_embeddings.weight", "word_embeddings"),
						    ("position_embeddings.weight", "position_embeddings"),
						    ("token_type_embeddings.weight", "token_type_embeddings"),
						    (".", "/"),
						    ("LayerNorm/weight", "LayerNorm/gamma"),
						    ("LayerNorm/bias", "LayerNorm/beta"),
						    ("weight", "kernel"),
						)

						if not os.path.isdir(__lowercase						):
												os.makedirs(__lowercase						)

						_snake_case						:					Union[str, Any]    =   model.state_dict()

						def to_tf_var_name(__lowercase						):
												for patt, repl in iter(__lowercase						):
																		_snake_case						:					Dict    =   name.replace(__lowercase ,      __lowercase						)
												return F"""bert/{name}"""

						def create_tf_var(__lowercase ,      __lowercase ,      __lowercase						):
												_snake_case						:					int    =   tf.dtypes.as_dtype(tensor.dtype						)
												_snake_case						:					List[str]    =   tf.get_variable(dtype=__lowercase ,      shape=tensor.shape ,      name=__lowercase ,      initializer=tf.zeros_initializer()						)
												session.run(tf.variables_initializer([tf_var]						)						)
												session.run(__lowercase						)
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		_snake_case						:					List[str]    =   to_tf_var_name(__lowercase						)
																		_snake_case						:					Optional[Any]    =   state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose						):
																								_snake_case						:					List[Any]    =   torch_tensor.T
																		_snake_case						:					str    =   create_tf_var(tensor=__lowercase ,      name=__lowercase ,      session=__lowercase						)
																		tf.keras.backend.set_value(__lowercase ,      __lowercase						)
																		_snake_case						:					Dict    =   session.run(__lowercase						)
																		print(F"""Successfully created {tf_name}: {np.allclose(__lowercase ,      __lowercase						)}"""						)

												_snake_case						:					Tuple    =   tf.train.Saver(tf.trainable_variables()						)
												saver.save(__lowercase ,      os.path.join(__lowercase ,      model_name.replace("-" ,      "_"						) + ".ckpt"						)						)
def  snake_case						(__lowercase=None						)	->	Tuple:

						'''simple docstring'''



						_snake_case						:					List[Any]    =   argparse.ArgumentParser()
						parser.add_argument("--model_name" ,      type=__lowercase ,      required=__lowercase ,      help="model name e.g. bert-base-uncased"						)
						parser.add_argument(
						    "--cache_dir" ,      type=__lowercase ,      default=__lowercase ,      required=__lowercase ,      help="Directory containing pytorch model"						)
						parser.add_argument("--pytorch_model_path" ,      type=__lowercase ,      required=__lowercase ,      help="/path/to/<pytorch-model-name>.bin"						)
						parser.add_argument("--tf_cache_dir" ,      type=__lowercase ,      required=__lowercase ,      help="Directory in which to save tensorflow model"						)
						_snake_case						:					Tuple    =   parser.parse_args(__lowercase						)

						_snake_case						:					Tuple    =   BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,      state_dict=torch.load(args.pytorch_model_path						) ,      cache_dir=args.cache_dir ,      )

						convert_pytorch_checkpoint_to_tf(model=__lowercase ,      ckpt_dir=args.tf_cache_dir ,      model_name=args.model_name						)


if __name__ == "__main__":
				main()
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   "laion/clap-htsat-unfused"
													_snake_case						:					Dict    =   tempfile.mkdtemp()



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self    ):
													shutil.rmtree(self.tmpdirname    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.get_tokenizer()
													_snake_case						:					List[Any]    =   self.get_feature_extractor()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													processor.save_pretrained(self.tmpdirname    )
													_snake_case						:					Tuple    =   ClapProcessor.from_pretrained(self.tmpdirname    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()    )
													processor.save_pretrained(self.tmpdirname    )

													_snake_case						:					Any    =   self.get_tokenizer(bos_token="(BOS)"				,    eos_token="(EOS)"    )
													_snake_case						:					List[Any]    =   self.get_feature_extractor(do_normalize=lowercase_				,    padding_value=1.0    )

													_snake_case						:					List[Any]    =   ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token="(BOS)"				,    eos_token="(EOS)"				,    do_normalize=lowercase_				,    padding_value=1.0    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_feature_extractor()
													_snake_case						:					Union[str, Any]    =   self.get_tokenizer()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					List[str]    =   floats_list((3, 1_000)    )

													_snake_case						:					Union[str, Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    )
													_snake_case						:					Any    =   processor(audios=lowercase_				,    return_tensors="np"    )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.get_feature_extractor()
													_snake_case						:					Optional[Any]    =   self.get_tokenizer()

													_snake_case						:					Dict    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Any    =   "This is a test string"

													_snake_case						:					Optional[Any]    =   processor(text=lowercase_    )

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_    )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.get_feature_extractor()
													_snake_case						:					Dict    =   self.get_tokenizer()

													_snake_case						:					List[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Dict    =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													_snake_case						:					List[Any]    =   processor.batch_decode(lowercase_    )
													_snake_case						:					Optional[int]    =   tokenizer.batch_decode(lowercase_    )

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.get_feature_extractor()
													_snake_case						:					str    =   self.get_tokenizer()

													_snake_case						:					Optional[int]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg="`processor` and `feature_extractor` model input names do not match"				,    )