


import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.albert.modeling_flax_albert import (
			    FlaxAlbertForMaskedLM,
			    FlaxAlbertForMultipleChoice,
			    FlaxAlbertForPreTraining,
			    FlaxAlbertForQuestionAnswering,
			    FlaxAlbertForSequenceClassification,
			    FlaxAlbertForTokenClassification,
			    FlaxAlbertModel,
			)

class        a	(unittest.TestCase   ):




							"""simple docstring"""


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

							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														__snake_case	:    Optional[int]											=     ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size       )

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

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

														__snake_case	:    Optional[Any]											=     AlbertConfig(
														    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=lowerCamelCase					,			initializer_range=self.initializer_range					,			)

														return config, input_ids, token_type_ids, attention_mask






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

@require_flax
class        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Tuple  						=						(
							    (
							        FlaxAlbertModel,
							        FlaxAlbertForPreTraining,
							        FlaxAlbertForMaskedLM,
							        FlaxAlbertForMultipleChoice,
							        FlaxAlbertForQuestionAnswering,
							        FlaxAlbertForSequenceClassification,
							        FlaxAlbertForTokenClassification,
							        FlaxAlbertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def 					__snake_case      (							self :						Optional[int]       )			->					List[str]:
														__snake_case	:    str											=     FlaxAlbertModelTester(self       )






							@slow
							def 					__snake_case      (							self :						Optional[int]       )			->					Tuple:
														for model_class_name in self.all_model_classes:
																					__snake_case	:    List[Any]											=     model_class_name.from_pretrained("albert-base-v2"       )
																					__snake_case	:    str											=     model(np.ones((1, 1)       )       )
																					self.assertIsNotNone(lowerCamelCase       )

@require_flax
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							@slow
							def 					__snake_case      (							self :						int       )			->					Tuple:
														__snake_case	:    Union[str, Any]											=     FlaxAlbertModel.from_pretrained("albert-base-v2"       )
														__snake_case	:    Any											=     np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
														__snake_case	:    int											=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
														__snake_case	:    Optional[Any]											=     model(lowerCamelCase					,			attention_mask=lowerCamelCase       )[0]
														__snake_case	:    Optional[int]											=     (1, 11, 768)
														self.assertEqual(output.shape					,			lowerCamelCase       )
														__snake_case	:    Optional[int]											=     np.array(
														    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]       )

														self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]					,			lowerCamelCase					,			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)

