


"""simple docstring"""
import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
				from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Optional[int]=13		,       UpperCAmelCase_							:      str=32		,       UpperCAmelCase_							:      List[Any]=3		,       UpperCAmelCase_							:      Optional[Any]=4		,       UpperCAmelCase_							:      Dict=[10, 20, 30, 40]		,       UpperCAmelCase_							:      List[Any]=[2, 2, 3, 2]		,       UpperCAmelCase_							:      Tuple=True		,       UpperCAmelCase_							:      Tuple=True		,       UpperCAmelCase_							:      Any=37		,       UpperCAmelCase_							:      str="gelu"		,       UpperCAmelCase_							:      Optional[int]=10		,       UpperCAmelCase_							:      Dict=0.02		,       UpperCAmelCase_							:      int=["stage2", "stage3", "stage4"]		,       UpperCAmelCase_							:      Optional[int]=[2, 3, 4]		,       UpperCAmelCase_							:      List[str]=None		,       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		image_size
														snake_case_           =		num_channels
														snake_case_           =		num_stages
														snake_case_           =		hidden_sizes
														snake_case_           =		depths
														snake_case_           =		is_training
														snake_case_           =		use_labels
														snake_case_           =		intermediate_size
														snake_case_           =		hidden_act
														snake_case_           =		num_labels
														snake_case_           =		initializer_range
														snake_case_           =		out_features
														snake_case_           =		out_indices
														snake_case_           =		scope



							def 		lowerCAmelCase       (				self							:      List[str]       )					->str:



														"""simple docstring"""



														snake_case_           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

														snake_case_           =		self.get_config()

														return config, pixel_values, labels



							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														return ConvNextVaConfig(
														    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=UpperCAmelCase_		,       initializer_range=self.initializer_range		,       out_features=self.out_features		,       out_indices=self.out_indices		,       num_labels=self.num_labels		,       )



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		ConvNextVaModel(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )
														# 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 // 32, self.image_size // 32)		,       )



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Optional[Any]       )					->Any:



														"""simple docstring"""



														snake_case_           =		ConvNextVaForImageClassification(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_		,       labels=UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		ConvNextVaBackbone(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )

														# 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
														snake_case_           =		None
														snake_case_           =		ConvNextVaBackbone(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )

														# 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 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														snake_case_       ,						snake_case_       ,						snake_case_           =		config_and_inputs
														snake_case_           =		{"""pixel_values""": pixel_values}
														return config, inputs_dict



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



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														snake_case_       ,						snake_case_       ,						snake_case_           =		config_and_inputs
														snake_case_           =		{"""pixel_values""": pixel_values, """labels""": labels}
														return config, inputs_dict





@require_torch
class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Optional[Any]							      =    (
							    (
							        ConvNextVaModel,
							        ConvNextVaForImageClassification,
							        ConvNextVaBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase:		Union[str, Any]							      =    (
							    {"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		Optional[Any]							      =    False
							__lowercase:		Any							      =    False
							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		Dict							      =    False



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



														"""simple docstring"""



														snake_case_           =		ConvNextVaModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       has_text_modality=UpperCAmelCase_		,       hidden_size=37       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Optional[Any]:



														"""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 		lowerCAmelCase       (				self							:      str       )					->Optional[Any]:



														"""simple docstring"""



														return



							@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""       )
							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Union[str, Any]:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""       )
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""       )
							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_with_labels()
																					snake_case_           =		True

																					if model_class.__name__ in [
																					    *get_values(UpperCAmelCase_       ),
																					    *get_values(UpperCAmelCase_       ),
																					]:
																												continue

																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.train()
																					snake_case_           =		self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_		,       return_labels=UpperCAmelCase_       )
																					snake_case_           =		model(**UpperCAmelCase_       ).loss
																					loss.backward()



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Any:



														"""simple docstring"""



														if not self.model_tester.is_training:
																					return

														for model_class in self.all_model_classes:
																					snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_with_labels()
																					snake_case_           =		False
																					snake_case_           =		True

																					if (
																					    model_class.__name__
																					    in [*get_values(UpperCAmelCase_       ), *get_values(UpperCAmelCase_       )]
																					    or not model_class.supports_gradient_checkpointing
																					):
																												continue

																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.gradient_checkpointing_enable()
																					model.train()
																					snake_case_           =		self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_		,       return_labels=UpperCAmelCase_       )
																					snake_case_           =		model(**UpperCAmelCase_       ).loss
																					loss.backward()



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



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

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

																					snake_case_           =		["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														def check_hidden_states_output(UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      str       ):
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()

																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )

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

																					snake_case_           =		self.model_tester.num_stages
																					self.assertEqual(len(UpperCAmelCase_       )		,       expected_num_stages + 1       )

																					# ConvNextV2'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]		,       )

														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

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

																					check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )



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



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_       )



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					snake_case_           =		ConvNextVaModel.from_pretrained(UpperCAmelCase_       )
																					self.assertIsNotNone(UpperCAmelCase_       )







def 						_a				(      )    ->				str:
							snake_case_           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





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





							'''simple docstring'''

							@cached_property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""       ) if is_vision_available() else None



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



														snake_case_           =		ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""       ).to(UpperCAmelCase_       )

														snake_case_           =		self.default_image_processor
														snake_case_           =		prepare_img()
														snake_case_           =		preprocessor(images=UpperCAmelCase_		,       return_tensors="""pt"""       ).to(UpperCAmelCase_       )

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(**UpperCAmelCase_       )

														# verify the logits
														snake_case_           =		torch.Size((1, 1_000)       )
														self.assertEqual(outputs.logits.shape		,       UpperCAmelCase_       )

														snake_case_           =		torch.tensor([0.9_996, 0.1_966, -0.4_386]       ).to(UpperCAmelCase_       )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]		,       UpperCAmelCase_		,       atol=1E-4       )       )







"""simple docstring"""
import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Optional[int]		=    'https://openaipublic.azureedge.net/jukebox/models/'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    {
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							if key.endswith(""".model.1.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.bias"""				, """.conv1d_1.bias"""				)
							elif key.endswith(""".model.1.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.weight"""				, """.conv1d_1.weight"""				)
							elif key.endswith(""".model.3.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.bias"""				, """.conv1d_2.bias"""				)
							elif key.endswith(""".model.3.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.weight"""				, """.conv1d_2.weight"""				)

							if "conditioner_blocks.0." in key:
														snake_case_           =		key.replace("""conditioner_blocks.0"""				, """conditioner_blocks"""				)

							if "prime_prior" in key:
														snake_case_           =		key.replace("""prime_prior"""				, """encoder"""				)

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														snake_case_           =		key.replace(""".emb."""				, """."""				)

							if key.endswith("""k"""				):  # replace vqvae.X.k with vqvae.X.codebook
														return key.replace(""".k"""				, """.codebook"""				)
							if "y_emb." in key:
														return key.replace("""y_emb."""				, """metadata_embedding."""				)

							if "x_emb.emb." in key:
														snake_case_           =		key.replace("""0.x_emb.emb"""				, """embed_tokens"""				)

							if "prime_state_ln" in key:
														return key.replace("""prime_state_ln"""				, """encoder.final_layer_norm"""				)
							if ".ln" in key:
														return key.replace(""".ln"""				, """.layer_norm"""				)
							if "_ln" in key:
														return key.replace("""_ln"""				, """_layer_norm"""				)

							if "prime_state_proj" in key:
														return key.replace("""prime_state_proj"""				, """encoder.proj_in"""				)
							if "prime_x_out" in key:
														return key.replace("""prime_x_out"""				, """encoder.lm_head"""				)
							if "prior.x_out" in key:
														return key.replace("""x_out"""				, """fc_proj_out"""				)
							if "x_emb" in key:
														return key.replace("""x_emb"""				, """embed_tokens"""				)

							return key







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							snake_case_           =		{}
							import re

							snake_case_           =		re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""				)

							snake_case_           =		re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""				)

							snake_case_           =		re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""				)
							snake_case_           =		re.compile(
							    r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""				)
							snake_case_           =		re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"""				)

							for original_key, value in state_dict.items():
														# rename vqvae.encoder keys
														if re_encoder_block_conv_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_conv_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				)
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_encoder_block_conv_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				)
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_encoder_block_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_encoder_block_proj_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_encoder_block_proj_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
																					snake_case_           =		re_encoder_block_proj_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_conv_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				) - 2
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_decoder_block_conv_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[2]				) * 2 + int(groups[3]				) - 2
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_decoder_block_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																					snake_case_           =		re_decoder_block_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_conv_out.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[1]				) * 2 + int(groups[2]				) - 2
																					snake_case_           =		f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_conv_out.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_prior_cond_resnet.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_resnet.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		int(groups[1]				) * 2 + int(groups[2]				) - 2
																					snake_case_           =		{"""1""": 1, """3""": 2}[groups[-2]]
																					snake_case_           =		f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
																					snake_case_           =		f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																					snake_case_           =		prefix + resnet_block
																					snake_case_           =		re_prior_cond_resnet.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# keep original key
														else:
																					snake_case_           =		original_key

														snake_case_           =		replace_key(_SCREAMING_SNAKE_CASE				)

														if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
																					print(f"""failed converting {original_key} to {key}, does not match"""				)

														# handle missmatched shape
														elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
																					snake_case_           =		model_state_dict[f"""{key_prefix}.{key}"""]
																					print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""				)
																					snake_case_           =		original_key

														snake_case_           =		original_key
														snake_case_           =		value

							return new_dict







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				)    ->				Optional[int]:
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				):
																					snake_case_           =		requests.get(f"""{PREFIX}{file}"""				, allow_redirects=_SCREAMING_SNAKE_CASE				)
																					os.makedirs(f"""{pytorch_dump_folder_path}/"""				, exist_ok=_SCREAMING_SNAKE_CASE				)
																					open(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				, """wb"""				).write(r.content				)

							snake_case_           =		MODEL_MAPPING[model_name.split("""/"""				)[-1]]

							snake_case_           =		JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		JukeboxModel(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		[]
							snake_case_           =		{}
							for i, dict_name in enumerate(_SCREAMING_SNAKE_CASE				):
														snake_case_           =		torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"				)[-1]}"""				)["""model"""]

														snake_case_           =		{}
														for k in old_dic.keys():
																					if k.endswith(""".b"""				):
																												snake_case_           =		old_dic[k]
																					elif k.endswith(""".w"""				):
																												snake_case_           =		old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												snake_case_           =		old_dic[k]
																					else:
																												snake_case_           =		old_dic[k]

														snake_case_           =		"""vqvae""" if i == 0 else f"""priors.{3 - i}"""
														snake_case_           =		fix_jukebox_keys(_SCREAMING_SNAKE_CASE				, model.state_dict()				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
														weight_dict.append(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		weight_dict.pop(0				)
							model.vqvae.load_state_dict(_SCREAMING_SNAKE_CASE				)
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														model.priors[i].load_state_dict(weight_dict[2 - i]				)

							Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
							with open(f"""{pytorch_dump_folder_path}/mapping.json"""				, """w"""				) as txtfile:
														json.dump(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)

							return weight_dict


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='jukebox-5b-lyrics',
				    type=str,
				    help='Name of the model you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='jukebox-5b-lyrics-converted',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				__SCREAMING_SNAKE_CASE							:    str		=    parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



