


"""simple docstring"""
import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				)
				from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]=13		,       UpperCAmelCase_							:      Optional[int]=7		,       UpperCAmelCase_							:      List[str]=True		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      List[Any]=True		,       UpperCAmelCase_							:      Optional[int]=True		,       UpperCAmelCase_							:      List[str]=99		,       UpperCAmelCase_							:      Dict=24		,       UpperCAmelCase_							:      List[str]=2		,       UpperCAmelCase_							:      Optional[Any]=6		,       UpperCAmelCase_							:      int=37		,       UpperCAmelCase_							:      Optional[Any]="gelu"		,       UpperCAmelCase_							:      Optional[Any]=0.1		,       UpperCAmelCase_							:      List[Any]=0.1		,       UpperCAmelCase_							:      Any=512		,       UpperCAmelCase_							:      str=16		,       UpperCAmelCase_							:      List[str]=2		,       UpperCAmelCase_							:      Optional[int]=0.02		,       UpperCAmelCase_							:      Tuple=3		,       UpperCAmelCase_							:      Union[str, Any]=None		,       UpperCAmelCase_							:      Any=1_000		,       )					->Tuple:



														"""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_           =		scope
														snake_case_           =		range_bbox



							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



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

														snake_case_           =		ids_tensor([self.batch_size, self.seq_length, 4]		,       self.range_bbox       )
														# Ensure that bbox is legal
														for i in range(bbox.shape[0]       ):
																					for j in range(bbox.shape[1]       ):
																												if bbox[i, j, 3] < bbox[i, j, 1]:
																																			snake_case_           =		bbox[i, j, 3]
																																			snake_case_           =		bbox[i, j, 1]
																																			snake_case_           =		t
																												if bbox[i, j, 2] < bbox[i, j, 0]:
																																			snake_case_           =		bbox[i, j, 2]
																																			snake_case_           =		bbox[i, j, 0]
																																			snake_case_           =		t

														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
														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_           =		self.get_config()

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



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



														"""simple docstring"""



														return LiltConfig(
														    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		,       )



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



														"""simple docstring"""



														snake_case_           =		LiltModel(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_       )
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_       )
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_       )

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



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



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_           =		LiltForQuestionAnswering(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(
														    UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_		,       start_positions=UpperCAmelCase_		,       end_positions=UpperCAmelCase_		,       )
														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							:      int       )					->Optional[int]:



														"""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,
														    """bbox""": bbox,
														    """token_type_ids""": token_type_ids,
														    """attention_mask""": input_mask,
														}
														return config, inputs_dict





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





							'''simple docstring'''

							__lowercase:		Optional[int]							      =    (
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase:		Optional[Any]							      =    (
							    {
							        """feature-extraction""": LiltModel,
							        """question-answering""": LiltForQuestionAnswering,
							        """text-classification""": LiltForSequenceClassification,
							        """token-classification""": LiltForTokenClassification,
							        """zero-shot""": LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		List[str]							      =    False



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



														"""simple docstring"""



														return True



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



														"""simple docstring"""



														snake_case_           =		LiltModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       hidden_size=37       )



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



														"""simple docstring"""



														self.config_tester.run_common_tests()



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



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					snake_case_           =		type
																					self.model_tester.create_and_check_model(*UpperCAmelCase_       )



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



														"""simple docstring"""



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



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



														"""simple docstring"""



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



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



														"""simple docstring"""



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





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





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_           =		LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""       ).to(UpperCAmelCase_       )

														snake_case_           =		torch.tensor([[1, 2]]		,       device=UpperCAmelCase_       )
														snake_case_           =		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]		,       device=UpperCAmelCase_       )

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(input_ids=UpperCAmelCase_		,       bbox=UpperCAmelCase_       )

														snake_case_           =		torch.Size([1, 2, 768]       )
														snake_case_           =		torch.tensor(
														    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]		,       device=UpperCAmelCase_		,       )

														self.assertTrue(outputs.last_hidden_state.shape		,       UpperCAmelCase_       )
														self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]		,       UpperCAmelCase_		,       atol=1E-3       )       )







"""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)



