





'''simple docstring'''


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
		from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor






class       UpperCamelCase__  :







	def __init__(				self			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   List[str]=1_3 , lowerCamelCase			:   Tuple=3_2 , lowerCamelCase			:   str=3 , lowerCamelCase			:   List[str]=4 , lowerCamelCase			:   str=[1_0, 2_0, 3_0, 4_0] , lowerCamelCase			:   Dict=[2, 2, 3, 2] , lowerCamelCase			:   Dict=True , lowerCamelCase			:   Union[str, Any]=True , lowerCamelCase			:   Union[str, Any]=3_7 , lowerCamelCase			:   List[str]="gelu" , lowerCamelCase			:   str=1_0 , lowerCamelCase			:   Union[str, Any]=0.02 , lowerCamelCase			:   Any=["stage2", "stage3", "stage4"] , lowerCamelCase			:   Union[str, Any]=[2, 3, 4] , lowerCamelCase			:   List[Any]=None , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = image_size
				a__     = num_channels
				a__     = num_stages
				a__     = hidden_sizes
				a__     = depths
				a__     = is_training
				a__     = use_labels
				a__     = intermediate_size
				a__     = hidden_act
				a__     = num_labels
				a__     = initializer_range
				a__     = out_features
				a__     = out_indices
				a__     = scope







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				a__     = None
				if self.use_labels:
							a__     = ids_tensor([self.batch_size] , self.num_labels		)

				a__     = self.get_config()
				return config, pixel_values, labels







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				return ConvNextConfig(
				    num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=lowerCamelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )







	def 						__a   (				self			:   Dict , lowerCamelCase			:   Tuple , lowerCamelCase			:   str , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = ConvNextModel(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(lowerCamelCase		)
				# expected last hidden states: B, C, H // 32, W // 32
				self.parent.assertEqual(
				    result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Dict , lowerCamelCase			:   List[str] , lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				a__     = ConvNextForImageClassification(lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(lowerCamelCase , labels=lowerCamelCase		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels)		)







	def 						__a   (				self			:   Any , lowerCamelCase			:   Tuple , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = ConvNextBackbone(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(lowerCamelCase		)

				# verify hidden states
				self.parent.assertEqual(len(result.feature_maps		) , len(config.out_features		)		)
				self.parent.assertListEqual(list(result.feature_maps[0].shape		) , [self.batch_size, self.hidden_sizes[1], 4, 4]		)

				# verify channels
				self.parent.assertEqual(len(model.channels		) , len(config.out_features		)		)
				self.parent.assertListEqual(model.channels , config.hidden_sizes[1:]		)

				# verify backbone works with out_features=None
				a__     = None
				a__     = ConvNextBackbone(config=lowerCamelCase		)
				model.to(lowerCamelCase		)
				model.eval()
				a__     = model(lowerCamelCase		)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps		) , 1		)
				self.parent.assertListEqual(list(result.feature_maps[0].shape		) , [self.batch_size, self.hidden_sizes[-1], 1, 1]		)

				# verify channels
				self.parent.assertEqual(len(model.channels		) , 1		)
				self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]]		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				a__   ,				a__   ,				a__     = config_and_inputs
				a__     = {"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class       UpperCamelCase__  (		__lowerCAmelCase	,__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			List[str]								=						(
	    (
	        ConvNextModel,
	        ConvNextForImageClassification,
	        ConvNextBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCAmelCase__					:			Any								=						(
	    {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
	    if is_torch_available()
	    else {}
	)

	lowerCAmelCase__					:			Tuple								=						True
	lowerCAmelCase__					:			List[Any]								=						False
	lowerCAmelCase__					:			Any								=						False
	lowerCAmelCase__					:			Any								=						False
	lowerCAmelCase__					:			Tuple								=						False







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = ConvNextModelTester(self		)
				a__     = ConfigTester(self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=3_7		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return







	@unittest.skip(reason="ConvNext does not use inputs_embeds"		)
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				pass







	@unittest.skip(reason="ConvNext does not support input and output embeddings"		)
	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				pass







	@unittest.skip(reason="ConvNext does not use feedforward chunking"		)
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				pass







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__   ,				a__     = self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							a__     = model_class(lowerCamelCase		)
							a__     = inspect.signature(model.forward		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							a__     = [*signature.parameters.keys()]

							a__     = ["pixel_values"]
							self.assertListEqual(arg_names[:1] , lowerCamelCase		)







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCamelCase		)







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*lowerCamelCase		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				def check_hidden_states_output(lowerCamelCase			:   Any , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Union[str, Any]		):
							a__     = model_class(lowerCamelCase		)
							model.to(lowerCamelCase		)
							model.eval()

							with torch.no_grad():
										a__     = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase		)		)

							a__     = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							a__     = self.model_tester.num_stages
							self.assertEqual(len(lowerCamelCase		) , expected_num_stages + 1		)

							# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]		) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )

				a__   ,				a__     = self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							a__     = True
							check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							a__     = True

							check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase		)







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)







	@slow
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							a__     = ConvNextModel.from_pretrained(lowerCamelCase		)
							self.assertIsNotNone(lowerCamelCase		)







def    _lowerCamelCase  ()   ->    Any:
			a__     = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
			return image






@require_torch
@require_vision
class       UpperCamelCase__  (		unittest.TestCase     ):







	@cached_property
	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"		) if is_vision_available() else None







	@slow
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"		).to(lowerCamelCase		)

				a__     = self.default_image_processor
				a__     = prepare_img()
				a__     = image_processor(images=lowerCamelCase , return_tensors="pt"		).to(lowerCamelCase		)

				# forward pass
				with torch.no_grad():
							a__     = model(**lowerCamelCase		)

				# verify the logits
				a__     = torch.Size((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape , lowerCamelCase		)

				a__     = torch.tensor([-0.0260, -0.4739, 0.1911]		).to(lowerCamelCase		)

				self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1e-4		)		)






@require_torch
class       UpperCamelCase__  (		unittest.TestCase	,__lowerCAmelCase     ):
	lowerCAmelCase__					:			Dict								=						(ConvNextBackbone,) if is_torch_available() else ()
	lowerCAmelCase__					:			List[Any]								=						ConvNextConfig

	lowerCAmelCase__					:			Dict								=						False







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = ConvNextModelTester(self		)










'''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_   :   List[str]       =					logging.get_logger(__name__)


lowerCAmelCase_   :   Optional[int]       =					["model.decoder.embed_positions.weights"]







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Optional[int]:
			if "emb" in name:
						a__     = name.replace("emb"					,  "model.decoder.embed_tokens"     )
			if "transformer" in name:
						a__     = name.replace("transformer"					,  "model.decoder"     )
			if "cross_attention" in name:
						a__     = name.replace("cross_attention"					,  "encoder_attn"     )
			if "linear1" in name:
						a__     = name.replace("linear1"					,  "fc1"     )
			if "linear2" in name:
						a__     = name.replace("linear2"					,  "fc2"     )
			if "norm1" in name:
						a__     = name.replace("norm1"					,  "self_attn_layer_norm"     )
			if "norm_cross" in name:
						a__     = name.replace("norm_cross"					,  "encoder_attn_layer_norm"     )
			if "norm2" in name:
						a__     = name.replace("norm2"					,  "final_layer_norm"     )
			if "out_norm" in name:
						a__     = name.replace("out_norm"					,  "model.decoder.layer_norm"     )
			if "linears" in name:
						a__     = name.replace("linears"					,  "lm_heads"     )
			if "condition_provider.conditioners.description.output_proj" in name:
						a__     = name.replace("condition_provider.conditioners.description.output_proj"					,  "enc_to_dec_proj"     )
			return name







def    _lowerCamelCase  (__lowerCamelCase  :     OrderedDict					,  __lowerCamelCase  :     int     )   ->    Tuple[Dict, Dict]:
			a__     = list(state_dict.keys()     )
			a__     = {}
			for key in keys:
						a__     = state_dict.pop(__lowerCamelCase     )
						a__     = rename_keys(__lowerCamelCase     )
						if "in_proj_weight" in key:
									# split fused qkv proj
									a__     = val[:hidden_size, :]
									a__     = val[hidden_size : 2 * hidden_size, :]
									a__     = val[-hidden_size:, :]
						elif "enc_to_dec_proj" in key:
									a__     = val
						else:
									a__     = val
			return state_dict, enc_dec_proj_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    MusicgenDecoderConfig:
			if checkpoint == "small":
						# default config values
						a__     = 1024
						a__     = 24
						a__     = 16
			elif checkpoint == "medium":
						a__     = 1536
						a__     = 48
						a__     = 24
			elif checkpoint == "large":
						a__     = 2048
						a__     = 48
						a__     = 32
			else:
						raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''     )
			a__     = MusicgenDecoderConfig(
			    hidden_size=__lowerCamelCase					,  ffn_dim=hidden_size * 4					,  num_hidden_layers=__lowerCamelCase					,  num_attention_heads=__lowerCamelCase					,  )
			return config







@torch.no_grad()
def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Dict=None					,  __lowerCamelCase  :     List[Any]="cpu"     )   ->    str:
			a__     = MusicGen.get_pretrained(__lowerCamelCase					,  device=__lowerCamelCase     )
			a__     = decoder_config_from_checkpoint(__lowerCamelCase     )

			a__     = fairseq_model.lm.state_dict()
			a__   ,				a__     = rename_state_dict(
			    __lowerCamelCase					,  hidden_size=decoder_config.hidden_size     )

			a__     = TaEncoderModel.from_pretrained("t5-base"     )
			a__     = EncodecModel.from_pretrained("facebook/encodec_32khz"     )
			a__     = MusicgenForCausalLM(__lowerCamelCase     ).eval()

			# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
			a__   ,				a__     = 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
			a__     = 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
			a__     = torch.arange(0					,  8					,  dtype=torch.long     ).reshape(2					,  -1     )
			a__     = input_ids.reshape(2 * 4					,  -1     )

			with torch.no_grad():
						a__     = model(input_ids=__lowerCamelCase					,  decoder_input_ids=__lowerCamelCase     ).logits

			if logits.shape != (8, 1, 2048):
						raise ValueError("Incorrect shape for logits"     )

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

			a__     = MusicgenProcessor(feature_extractor=__lowerCamelCase					,  tokenizer=__lowerCamelCase     )

			# set the appropriate bos/pad token ids
			a__     = 2048
			a__     = 2048

			# set other default generation config params
			a__     = int(30 * audio_encoder.config.frame_rate     )
			a__     = True
			a__     = 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_   :   List[Any]       =					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_   :   str       =					parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



