from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						)
class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			str					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Union[str, Any]=13					,lowerCamelCase__     :			Union[str, Any]=7					,lowerCamelCase__     :			Optional[Any]=True					,lowerCamelCase__     :			List[Any]=True					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			Optional[int]=True					,lowerCamelCase__     :			List[str]=99					,lowerCamelCase__     :			Optional[int]=32					,lowerCamelCase__     :			Dict=2					,lowerCamelCase__     :			Optional[Any]=4					,lowerCamelCase__     :			Optional[Any]=37					,lowerCamelCase__     :			Optional[Any]="gelu"					,lowerCamelCase__     :			Optional[int]=0.1					,lowerCamelCase__     :			Dict=0.1					,lowerCamelCase__     :			Dict=512					,lowerCamelCase__     :			Optional[int]=16					,lowerCamelCase__     :			Optional[Any]=2					,lowerCamelCase__     :			Optional[int]=0.0_2					,lowerCamelCase__     :			str=False					,lowerCamelCase__     :			Any=True					,lowerCamelCase__     :			int="None"					,lowerCamelCase__     :			Dict=3					,lowerCamelCase__     :			List[Any]=4					,lowerCamelCase__     :			List[str]=None					,):
									UpperCAmelCase__    =							parent
									UpperCAmelCase__    =							batch_size
									UpperCAmelCase__    =							seq_length
									UpperCAmelCase__    =							is_training
									UpperCAmelCase__    =							use_input_mask
									UpperCAmelCase__    =							use_token_type_ids
									UpperCAmelCase__    =							use_labels
									UpperCAmelCase__    =							vocab_size
									UpperCAmelCase__    =							hidden_size
									UpperCAmelCase__    =							num_hidden_layers
									UpperCAmelCase__    =							num_attention_heads
									UpperCAmelCase__    =							intermediate_size
									UpperCAmelCase__    =							hidden_act
									UpperCAmelCase__    =							hidden_dropout_prob
									UpperCAmelCase__    =							attention_probs_dropout_prob
									UpperCAmelCase__    =							max_position_embeddings
									UpperCAmelCase__    =							type_vocab_size
									UpperCAmelCase__    =							type_sequence_label_size
									UpperCAmelCase__    =							initializer_range
									UpperCAmelCase__    =							num_labels
									UpperCAmelCase__    =							num_choices
									UpperCAmelCase__    =							relative_attention
									UpperCAmelCase__    =							position_biased_input
									UpperCAmelCase__    =							pos_att_type
									UpperCAmelCase__    =							scope







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size    )

									UpperCAmelCase__    =							None
									if self.use_input_mask:
													UpperCAmelCase__    =							random_attention_mask([self.batch_size, self.seq_length]    )

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

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

									UpperCAmelCase__    =							DebertaVaConfig(
									    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					,relative_attention=self.relative_attention					,position_biased_input=self.position_biased_input					,initializer_range=self.initializer_range					,return_dict=lowerCamelCase__					,)

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







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[Any]    ):
									UpperCAmelCase__    =							TFDebertaVaModel(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									UpperCAmelCase__    =							[input_ids, input_mask]
									UpperCAmelCase__    =							model(lowerCamelCase__    )

									UpperCAmelCase__    =							model(lowerCamelCase__    )

									self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							TFDebertaVaForMaskedLM(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__    =							model(lowerCamelCase__    )
									self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.num_labels
									UpperCAmelCase__    =							TFDebertaVaForSequenceClassification(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									UpperCAmelCase__    =							model(lowerCamelCase__    )
									self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)    )







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							self.num_labels
									UpperCAmelCase__    =							TFDebertaVaForTokenClassification(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									UpperCAmelCase__    =							model(lowerCamelCase__    )
									self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							TFDebertaVaForQuestionAnswering(config=lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									UpperCAmelCase__    =							model(lowerCamelCase__    )
									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     :			List[Any]    ):
									UpperCAmelCase__    =							self.prepare_config_and_inputs()
									(
									    (
									    UpperCAmelCase__			
									)   ,			(
									    UpperCAmelCase__			
									)   ,			(
									    UpperCAmelCase__			
									)   ,			(
									    UpperCAmelCase__			
									)   ,			(
									    UpperCAmelCase__			
									)   ,			(
									    UpperCAmelCase__			
									)   ,			(
									    UpperCAmelCase__			
									)   ,						
									)    =							config_and_inputs
									UpperCAmelCase__    =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict






@require_tf
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				(
					    (
					        TFDebertaVaModel,
					        TFDebertaVaForMaskedLM,
					        TFDebertaVaForQuestionAnswering,
					        TFDebertaVaForSequenceClassification,
					        TFDebertaVaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					snake_case__       					=				(
					    {
					        "feature-extraction": TFDebertaVaModel,
					        "fill-mask": TFDebertaVaForMaskedLM,
					        "question-answering": TFDebertaVaForQuestionAnswering,
					        "text-classification": TFDebertaVaForSequenceClassification,
					        "token-classification": TFDebertaVaForTokenClassification,
					        "zero-shot": TFDebertaVaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					snake_case__       					=				False
					snake_case__       					=				False







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							TFDebertaVaModelTester(self    )
									UpperCAmelCase__    =							ConfigTester(self					,config_class=lowerCamelCase__					,hidden_size=37    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									self.config_tester.run_common_tests()







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'    )
									self.assertIsNotNone(lowerCamelCase__    )






@require_tf
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					@unittest.skip(reason='Model not available yet'    )
					def 	__lowerCAmelCase		(       self     :			Any    ):
									pass







					@slow
					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'    )
									UpperCAmelCase__    =							tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    )
									UpperCAmelCase__    =							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
									UpperCAmelCase__    =							model(lowerCamelCase__					,attention_mask=lowerCamelCase__    )[0]

									UpperCAmelCase__    =							tf.constant(
									    [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]    )
									tf.debugging.assert_near(output[:, 1:4, 1:4]					,lowerCamelCase__					,atol=1e-4    )




"""simple docstring"""






import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__						:						Optional[int]		  =			logging.get_logger(__name__)


lowerCAmelCase__						:						Optional[Any]		  =			['model.decoder.embed_positions.weights']



def 						a_		(      lowerCamelCase ):
				if "emb" in name:
								UpperCAmelCase__    =							name.replace('emb'      ,    'model.decoder.embed_tokens' )
				if "transformer" in name:
								UpperCAmelCase__    =							name.replace('transformer'      ,    'model.decoder' )
				if "cross_attention" in name:
								UpperCAmelCase__    =							name.replace('cross_attention'      ,    'encoder_attn' )
				if "linear1" in name:
								UpperCAmelCase__    =							name.replace('linear1'      ,    'fc1' )
				if "linear2" in name:
								UpperCAmelCase__    =							name.replace('linear2'      ,    'fc2' )
				if "norm1" in name:
								UpperCAmelCase__    =							name.replace('norm1'      ,    'self_attn_layer_norm' )
				if "norm_cross" in name:
								UpperCAmelCase__    =							name.replace('norm_cross'      ,    'encoder_attn_layer_norm' )
				if "norm2" in name:
								UpperCAmelCase__    =							name.replace('norm2'      ,    'final_layer_norm' )
				if "out_norm" in name:
								UpperCAmelCase__    =							name.replace('out_norm'      ,    'model.decoder.layer_norm' )
				if "linears" in name:
								UpperCAmelCase__    =							name.replace('linears'      ,    'lm_heads' )
				if "condition_provider.conditioners.description.output_proj" in name:
								UpperCAmelCase__    =							name.replace('condition_provider.conditioners.description.output_proj'      ,    'enc_to_dec_proj' )
				return name



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							list(state_dict.keys() )
				UpperCAmelCase__    =							{}
				for key in keys:
								UpperCAmelCase__    =							state_dict.pop(lowerCamelCase )
								UpperCAmelCase__    =							rename_keys(lowerCamelCase )
								if "in_proj_weight" in key:
												# split fused qkv proj
												UpperCAmelCase__    =							val[:hidden_size, :]
												UpperCAmelCase__    =							val[hidden_size : 2 * hidden_size, :]
												UpperCAmelCase__    =							val[-hidden_size:, :]
								elif "enc_to_dec_proj" in key:
												UpperCAmelCase__    =							val
								else:
												UpperCAmelCase__    =							val
				return state_dict, enc_dec_proj_state_dict



def 						a_		(      lowerCamelCase ):
				if checkpoint == "small":
								# default config values
								UpperCAmelCase__    =							1_0_2_4
								UpperCAmelCase__    =							2_4
								UpperCAmelCase__    =							1_6
				elif checkpoint == "medium":
								UpperCAmelCase__    =							1_5_3_6
								UpperCAmelCase__    =							4_8
								UpperCAmelCase__    =							2_4
				elif checkpoint == "large":
								UpperCAmelCase__    =							2_0_4_8
								UpperCAmelCase__    =							4_8
								UpperCAmelCase__    =							3_2
				else:
								raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
				UpperCAmelCase__    =							MusicgenDecoderConfig(
				    hidden_size=lowerCamelCase      ,    ffn_dim=hidden_size * 4      ,    num_hidden_layers=lowerCamelCase      ,    num_attention_heads=lowerCamelCase      ,    )
				return config



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase=None      ,    lowerCamelCase=None      ,    lowerCamelCase="cpu" ):
				UpperCAmelCase__    =							MusicGen.get_pretrained(lowerCamelCase      ,    device=lowerCamelCase )
				UpperCAmelCase__    =							decoder_config_from_checkpoint(lowerCamelCase )

				UpperCAmelCase__    =							fairseq_model.lm.state_dict()
				UpperCAmelCase__   ,			UpperCAmelCase__    =							rename_state_dict(
				    lowerCamelCase      ,    hidden_size=decoder_config.hidden_size )

				UpperCAmelCase__    =							TaEncoderModel.from_pretrained('t5-base' )
				UpperCAmelCase__    =							EncodecModel.from_pretrained('facebook/encodec_32khz' )
				UpperCAmelCase__    =							MusicgenForCausalLM(lowerCamelCase ).eval()

				# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
				UpperCAmelCase__   ,			UpperCAmelCase__    =							decoder.load_state_dict(lowerCamelCase      ,    strict=lowerCamelCase )

				for key in missing_keys.copy():
								if key.startswith(('text_encoder', 'audio_encoder') ) or key in EXPECTED_MISSING_KEYS:
												missing_keys.remove(lowerCamelCase )

				if len(lowerCamelCase ) > 0:
								raise ValueError(f'''Missing key(s) in state_dict: {missing_keys}''' )

				if len(lowerCamelCase ) > 0:
								raise ValueError(f'''Unexpected key(s) in state_dict: {unexpected_keys}''' )

				# init the composite model
				UpperCAmelCase__    =							MusicgenForConditionalGeneration(text_encoder=lowerCamelCase      ,    audio_encoder=lowerCamelCase      ,    decoder=lowerCamelCase )

				# load the pre-trained enc-dec projection (from the decoder state dict)
				model.enc_to_dec_proj.load_state_dict(lowerCamelCase )

				# check we can do a forward pass
				UpperCAmelCase__    =							torch.arange(0      ,    8      ,    dtype=torch.long ).reshape(2      ,    -1 )
				UpperCAmelCase__    =							input_ids.reshape(2 * 4      ,    -1 )

				with torch.no_grad():
								UpperCAmelCase__    =							model(input_ids=lowerCamelCase      ,    decoder_input_ids=lowerCamelCase ).logits

				if logits.shape != (8, 1, 2_0_4_8):
								raise ValueError('Incorrect shape for logits' )

				# now construct the processor
				UpperCAmelCase__    =							AutoTokenizer.from_pretrained('t5-base' )
				UpperCAmelCase__    =							AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'      ,    padding_side='left' )

				UpperCAmelCase__    =							MusicgenProcessor(feature_extractor=lowerCamelCase      ,    tokenizer=lowerCamelCase )

				# set the appropriate bos/pad token ids
				UpperCAmelCase__    =							2_0_4_8
				UpperCAmelCase__    =							2_0_4_8

				# set other default generation config params
				UpperCAmelCase__    =							int(3_0 * audio_encoder.config.frame_rate )
				UpperCAmelCase__    =							True
				UpperCAmelCase__    =							3.0

				if pytorch_dump_folder is not None:
								Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
								logger.info(f'''Saving model {checkpoint} to {pytorch_dump_folder}''' )
								model.save_pretrained(lowerCamelCase )
								processor.save_pretrained(lowerCamelCase )

				if repo_id:
								logger.info(f'''Pushing model {checkpoint} to {repo_id}''' )
								model.push_to_hub(lowerCamelCase )
								processor.push_to_hub(lowerCamelCase )


if __name__ == "__main__":
						lowerCAmelCase__						:						Tuple		  =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint',
						    default='small',
						    type=str,
						    help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder',
						    required=True,
						    default=None,
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument(
						    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
						)
						parser.add_argument(
						    '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
						)

						lowerCAmelCase__						:						List[str]		  =			parser.parse_args()
						convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



