


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
			    FlaxRobertaPreLayerNormForCausalLM,
			    FlaxRobertaPreLayerNormForMaskedLM,
			    FlaxRobertaPreLayerNormForMultipleChoice,
			    FlaxRobertaPreLayerNormForQuestionAnswering,
			    FlaxRobertaPreLayerNormForSequenceClassification,
			    FlaxRobertaPreLayerNormForTokenClassification,
			    FlaxRobertaPreLayerNormModel,
			)

class        a	(unittest.TestCase   ):




	"""simple docstring"""


	def __init__(							self :						Optional[int]					,			lowerCamelCase :						int					,			lowerCamelCase :						str=13					,			lowerCamelCase :						Optional[Any]=7					,			lowerCamelCase :						Any=True					,			lowerCamelCase :						Tuple=True					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Dict=True					,			lowerCamelCase :						int=99					,			lowerCamelCase :						List[Any]=32					,			lowerCamelCase :						str=5					,			lowerCamelCase :						str=4					,			lowerCamelCase :						str=37					,			lowerCamelCase :						Optional[Any]="gelu"					,			lowerCamelCase :						Optional[int]=0.1					,			lowerCamelCase :						Tuple=0.1					,			lowerCamelCase :						str=512					,			lowerCamelCase :						List[Any]=16					,			lowerCamelCase :						List[Any]=2					,			lowerCamelCase :						Any=0.02					,			lowerCamelCase :						Tuple=4					,			)			->					str:
								__snake_case	:    Optional[int]											=     parent
								__snake_case	:    str											=     batch_size
								__snake_case	:    Union[str, Any]											=     seq_length
								__snake_case	:    Any											=     is_training
								__snake_case	:    Union[str, Any]											=     use_attention_mask
								__snake_case	:    str											=     use_token_type_ids
								__snake_case	:    str											=     use_labels
								__snake_case	:    Union[str, Any]											=     vocab_size
								__snake_case	:    Any											=     hidden_size
								__snake_case	:    List[str]											=     num_hidden_layers
								__snake_case	:    int											=     num_attention_heads
								__snake_case	:    Optional[int]											=     intermediate_size
								__snake_case	:    List[str]											=     hidden_act
								__snake_case	:    Union[str, Any]											=     hidden_dropout_prob
								__snake_case	:    List[str]											=     attention_probs_dropout_prob
								__snake_case	:    Optional[Any]											=     max_position_embeddings
								__snake_case	:    str											=     type_vocab_size
								__snake_case	:    Optional[Any]											=     type_sequence_label_size
								__snake_case	:    Optional[int]											=     initializer_range
								__snake_case	:    Optional[Any]											=     num_choices

	def 					__snake_case      (							self :						List[Any]       )			->					Tuple:
								__snake_case	:    Union[str, Any]											=     ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size       )

								__snake_case	:    Any											=     None
								if self.use_attention_mask:
															__snake_case	:    Union[str, Any]											=     random_attention_mask([self.batch_size, self.seq_length]       )

								__snake_case	:    Union[str, Any]											=     None
								if self.use_token_type_ids:
															__snake_case	:    List[str]											=     ids_tensor([self.batch_size, self.seq_length]					,			self.type_vocab_size       )

								__snake_case	:    Tuple											=     RobertaPreLayerNormConfig(
								    vocab_size=self.vocab_size					,			hidden_size=self.hidden_size					,			num_hidden_layers=self.num_hidden_layers					,			num_attention_heads=self.num_attention_heads					,			intermediate_size=self.intermediate_size					,			hidden_act=self.hidden_act					,			hidden_dropout_prob=self.hidden_dropout_prob					,			attention_probs_dropout_prob=self.attention_probs_dropout_prob					,			max_position_embeddings=self.max_position_embeddings					,			type_vocab_size=self.type_vocab_size					,			is_decoder=__A					,			initializer_range=self.initializer_range					,			)

								return config, input_ids, token_type_ids, attention_mask

	def 					__snake_case      (							self :						Union[str, Any]       )			->					int:
								__snake_case	:    Union[str, Any]											=     self.prepare_config_and_inputs()
								__snake_case				,    __snake_case				,    __snake_case				,    __snake_case	:    Tuple											=     config_and_inputs
								__snake_case	:    Dict											=     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict






	def 					__snake_case      (							self :						Optional[int]       )			->					List[str]:
								__snake_case	:    int											=     self.prepare_config_and_inputs()
								__snake_case				,    __snake_case				,    __snake_case				,    __snake_case	:    Optional[Any]											=     config_and_inputs

								__snake_case	:    List[Any]											=     True
								__snake_case	:    Any											=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
								__snake_case	:    Optional[int]											=     ids_tensor([self.batch_size, self.seq_length]					,			vocab_size=2       )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        a	(__lowercase  ,       unittest.TestCase   ):




	"""simple docstring"""


	__UpperCAmelCase					:							Any  						=						True

	__UpperCAmelCase					:							Any  						=						(
	    (
	        FlaxRobertaPreLayerNormModel,
	        FlaxRobertaPreLayerNormForCausalLM,
	        FlaxRobertaPreLayerNormForMaskedLM,
	        FlaxRobertaPreLayerNormForSequenceClassification,
	        FlaxRobertaPreLayerNormForTokenClassification,
	        FlaxRobertaPreLayerNormForMultipleChoice,
	        FlaxRobertaPreLayerNormForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)

	def 					__snake_case      (							self :						List[Any]       )			->					Tuple:
								__snake_case	:    List[Any]											=     FlaxRobertaPreLayerNormModelTester(self       )






	@slow
	def 					__snake_case      (							self :						Optional[Any]       )			->					int:
								for model_class_name in self.all_model_classes:
															__snake_case	:    Tuple											=     model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,			from_pt=__A       )
															__snake_case	:    Optional[int]											=     model(np.ones((1, 1)       )       )
															self.assertIsNotNone(__A       )

@require_flax
class        a	(unittest.TestCase   ):




	"""simple docstring"""


	@slow
	def 					__snake_case      (							self :						List[Any]       )			->					Optional[int]:
								__snake_case	:    Optional[Any]											=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,			from_pt=__A       )

								__snake_case	:    Any											=     np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					,			dtype=jnp.intaa       )
								__snake_case	:    Tuple											=     model(__A       )[0]
								__snake_case	:    Optional[Any]											=     [1, 11, 50265]
								self.assertEqual(list(output.shape       )					,			__A       )
								# compare the actual values for a slice.
								__snake_case	:    Dict											=     np.array(
								    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]					,			dtype=np.floataa       )
								self.assertTrue(np.allclose(output[:, :3, :3]					,			__A					,			atol=1E-4       )       )






	@slow
	def 					__snake_case      (							self :						int       )			->					Optional[Any]:
								__snake_case	:    Tuple											=     FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,			from_pt=__A       )

								__snake_case	:    Any											=     np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					,			dtype=jnp.intaa       )
								__snake_case	:    Tuple											=     model(__A       )[0]
								# compare the actual values for a slice.
								__snake_case	:    str											=     np.array(
								    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]					,			dtype=np.floataa       )
								self.assertTrue(np.allclose(output[:, :3, :3]					,			__A					,			atol=1E-4       )       )





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	int			      =       {
    "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",
}

_snake_case  :	Dict			      =       AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    Any											=     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        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    int											=     {}

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

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

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

																					# Because in CLAP they use `nn.Sequential`...
																					__snake_case	:    List[str]											=     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	:    List[str]											=     value
																					__snake_case	:    Optional[int]											=     mixed_qkv.size(0			) // 3

																					__snake_case	:    List[str]											=     mixed_qkv[:qkv_dim]
																					__snake_case	:    int											=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case	:    str											=     mixed_qkv[qkv_dim * 2 :]

																					__snake_case	:    int											=     query_layer
																					__snake_case	:    Tuple											=     key_layer
																					__snake_case	:    List[str]											=     value_layer
														else:
																					__snake_case	:    Tuple											=     value

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

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

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[Any]											=     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__":
			_snake_case  :	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")
			_snake_case  :	Tuple			      =       parser.parse_args()

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

