




'''simple docstring'''





import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class  _snake_case		(	lowercase_							):




			def __init__(					self				,							a__				,							a__=13				,							a__=7				,							a__=True				,							a__=True				,							a__=True				,							a__=True				,							a__=99				,							a__=32				,							a__=5				,							a__=4				,							a__=37				,							a__="gelu"				,							a__=0.1				,							a__=0.1				,							a__=512				,							a__=16				,							a__=2				,							a__=0.0_2				,							a__=False				,							a__=True				,							a__="None"				,							a__=3				,							a__=4				,							a__=None				,							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      parent
										snake_case_						=      batch_size
										snake_case_						=      seq_length
										snake_case_						=      is_training
										snake_case_						=      use_input_mask
										snake_case_						=      use_token_type_ids
										snake_case_						=      use_labels
										snake_case_						=      vocab_size
										snake_case_						=      hidden_size
										snake_case_						=      num_hidden_layers
										snake_case_						=      num_attention_heads
										snake_case_						=      intermediate_size
										snake_case_						=      hidden_act
										snake_case_						=      hidden_dropout_prob
										snake_case_						=      attention_probs_dropout_prob
										snake_case_						=      max_position_embeddings
										snake_case_						=      type_vocab_size
										snake_case_						=      type_sequence_label_size
										snake_case_						=      initializer_range
										snake_case_						=      num_labels
										snake_case_						=      num_choices
										snake_case_						=      relative_attention
										snake_case_						=      position_biased_input
										snake_case_						=      pos_att_type
										snake_case_						=      scope




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size							)

										snake_case_						=      None
										if self.use_input_mask:
																	snake_case_						=      ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2							)

										snake_case_						=      None
										if self.use_token_type_ids:
																	snake_case_						=      ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size							)

										snake_case_						=      None
										snake_case_						=      None
										snake_case_						=      None
										if self.use_labels:
																	snake_case_						=      ids_tensor([self.batch_size]				,							self.type_sequence_label_size							)
																	snake_case_						=      ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels							)
																	snake_case_						=      ids_tensor([self.batch_size]				,							self.num_choices							)

										snake_case_						=      self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										return DebertaConfig(
										    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				,							initializer_range=self.initializer_range				,							relative_attention=self.relative_attention				,							position_biased_input=self.position_biased_input				,							pos_att_type=self.pos_att_type				,							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      self.get_config()
										snake_case_						=      300
										return config




			def    lowerCAmelCase__				(					self				,							a__							)				->				List[str]:

										'''simple docstring'''




										self.parent.assertListEqual(list(result.loss.size()							)				,							[]							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__							)				->				Any:

										'''simple docstring'''




										snake_case_						=      DebertaModel(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__				,							attention_mask=a__				,							token_type_ids=a__							)[0]
										snake_case_						=      model(a__				,							token_type_ids=a__							)[0]
										snake_case_						=      model(a__							)[0]

										self.parent.assertListEqual(list(sequence_output.size()							)				,							[self.batch_size, self.seq_length, self.hidden_size]							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__							)				->				Any:

										'''simple docstring'''




										snake_case_						=      DebertaForMaskedLM(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__				,							attention_mask=a__				,							token_type_ids=a__				,							labels=a__							)

										self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__							)				->				Union[str, Any]:

										'''simple docstring'''




										snake_case_						=      self.num_labels
										snake_case_						=      DebertaForSequenceClassification(a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__				,							attention_mask=a__				,							token_type_ids=a__				,							labels=a__							)
										self.parent.assertListEqual(list(result.logits.size()							)				,							[self.batch_size, self.num_labels]							)
										self.check_loss_output(a__							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      self.num_labels
										snake_case_						=      DebertaForTokenClassification(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(a__				,							attention_mask=a__				,							token_type_ids=a__				,							labels=a__							)
										self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__				,							a__							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      DebertaForQuestionAnswering(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_						=      model(
										    a__				,							attention_mask=a__				,							token_type_ids=a__				,							start_positions=a__				,							end_positions=a__				,							)
										self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length)							)
										self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length)							)




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.prepare_config_and_inputs()
										(
										    (
										    snake_case_
										)					,      (
										    snake_case_
										)					,      (
										    snake_case_
										)					,      (
										    snake_case_
										)					,      (
										    snake_case_
										)					,      (
										    snake_case_
										)					,      (
										    snake_case_
										)					,      
										)						=      config_and_inputs
										snake_case_						=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict




@require_torch
class  _snake_case		(	lowercase_						,						lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      Optional[int]						       =    (
			    (
			        DebertaModel,
			        DebertaForMaskedLM,
			        DebertaForSequenceClassification,
			        DebertaForTokenClassification,
			        DebertaForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCAmelCase_     :      Dict						       =    (
			    {
			        "feature-extraction": DebertaModel,
			        "fill-mask": DebertaForMaskedLM,
			        "question-answering": DebertaForQuestionAnswering,
			        "text-classification": DebertaForSequenceClassification,
			        "token-classification": DebertaForTokenClassification,
			        "zero-shot": DebertaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCAmelCase_     :      Optional[int]						       =    True
			lowerCAmelCase_     :      Union[str, Any]						       =    False
			lowerCAmelCase_     :      Tuple						       =    False
			lowerCAmelCase_     :      Optional[int]						       =    False
			lowerCAmelCase_     :      Tuple						       =    False




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      DebertaModelTester(self							)
										snake_case_						=      ConfigTester(self				,							config_class=a__				,							hidden_size=37							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										self.config_tester.run_common_tests()




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_model(*a__							)




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_sequence_classification(*a__							)




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_masked_lm(*a__							)




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_question_answering(*a__							)




			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										snake_case_						=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_deberta_for_token_classification(*a__							)




			@slow
			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_						=      DebertaModel.from_pretrained(a__							)
																	self.assertIsNotNone(a__							)




@require_torch
@require_sentencepiece
@require_tokenizers
class  _snake_case		(	unittest.TestCase							):




			@unittest.skip(reason="Model not available yet"							)
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										pass




			@slow
			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      DebertaModel.from_pretrained("microsoft/deberta-base"							)

										snake_case_						=      torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]							)
										snake_case_						=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
										with torch.no_grad():
																	snake_case_						=      model(a__				,							attention_mask=a__							)[0]
										# compare the actual values for a slice.
										snake_case_						=      torch.tensor(
										    [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]							)
										self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,							a__				,							atol=1e-4							)				,							F'{output[:, 1:4, 1:4]}'							)








'''simple docstring'''


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase__					=   {
    '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',
}

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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[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      ):
		_UpperCAmelCase						:				Dict	    =					{}

		_UpperCAmelCase						:				str	    =					R".*sequential.(\d+).*"
		_UpperCAmelCase						:				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:
								_UpperCAmelCase						:				Union[str, Any]	    =					key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

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

						_UpperCAmelCase						:				Optional[int]	    =					key.replace(F"""sequential.{sequential_layer}."""					,       F"""layers.{int(__lowerCAmelCase      )//3}.linear."""      )
				elif re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				List[str]	    =					int(re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )      )

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

						_UpperCAmelCase						:				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
						_UpperCAmelCase						:				List[str]	    =					value
						_UpperCAmelCase						:				Tuple	    =					mixed_qkv.size(0      ) // 3

						_UpperCAmelCase						:				Union[str, Any]	    =					mixed_qkv[:qkv_dim]
						_UpperCAmelCase						:				int	    =					mixed_qkv[qkv_dim : qkv_dim * 2]
						_UpperCAmelCase						:				Optional[int]	    =					mixed_qkv[qkv_dim * 2 :]

						_UpperCAmelCase						:				List[Any]	    =					query_layer
						_UpperCAmelCase						:				int	    =					key_layer
						_UpperCAmelCase						:				Any	    =					value_layer
				else:
						_UpperCAmelCase						:				Dict	    =					value

		return model_state_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					init_clap(__lowerCAmelCase					,       enable_fusion=__lowerCAmelCase      )

		clap_model.eval()
		_UpperCAmelCase						:				List[str]	    =					clap_model.state_dict()
		_UpperCAmelCase						:				str	    =					rename_state_dict(__lowerCAmelCase      )

		_UpperCAmelCase						:				List[Any]	    =					ClapConfig()
		_UpperCAmelCase						:				str	    =					enable_fusion
		_UpperCAmelCase						:				Union[str, 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__":
						lowerCamelCase__					=   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')
						lowerCamelCase__					=   parser.parse_args()

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



