



import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
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 torch import nn

							from transformers import (
							    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
							    TimesformerForVideoClassification,
							    TimesformerModel,
							)
							from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from transformers import VideoMAEImageProcessor







class     __lowerCAmelCase					:



			def __init__(		self     :						int				,	A     :						str				,	A     :						List[str]=13				,	A     :						Optional[Any]=10				,	A     :						str=3				,	A     :						Optional[Any]=2				,	A     :						Dict=2				,	A     :						Optional[int]=True				,	A     :						int=True				,	A     :						Optional[int]=32				,	A     :						Optional[Any]=5				,	A     :						Dict=4				,	A     :						int=37				,	A     :						Optional[int]="gelu"				,	A     :						Union[str, Any]=0.1				,	A     :						str=0.1				,	A     :						Dict=10				,	A     :						Union[str, Any]=0.0_2				,	A     :						List[str]="divided_space_time"				,	A     :						str=None				,	)     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     image_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     patch_size
						_UpperCAmelCase	 =     num_frames
						_UpperCAmelCase	 =     is_training
						_UpperCAmelCase	 =     use_labels
						_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	 =     attention_type
						_UpperCAmelCase	 =     initializer_range
						_UpperCAmelCase	 =     scope
						_UpperCAmelCase	 =     num_labels

						# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
						_UpperCAmelCase	 =     (image_size // patch_size) ** 2
						_UpperCAmelCase	 =     (num_frames) * self.num_patches_per_frame + 1



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



						"""simple docstring"""






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

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

						_UpperCAmelCase	 =     self.get_config()

						return config, pixel_values, labels



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     TimesformerConfig(
						    image_size=self.image_size				,	patch_size=self.patch_size				,	num_channels=self.num_channels				,	num_frames=self.num_frames				,	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				,	initializer_range=self.initializer_range				,	attention_type=self.attention_type				,	)
						_UpperCAmelCase	 =     self.num_labels
						return config



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						List[str]				,	A     :						Optional[int]				,	A     :						str)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     TimesformerModel(config=A)
						model.to(A)
						model.eval()
						_UpperCAmelCase	 =     model(A)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))



			def 					_lowerCamelCase (		self     :						str				,	A     :						Tuple				,	A     :						List[str]				,	A     :						Dict)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     TimesformerForVideoClassification(A)
						model.to(A)
						model.eval()

						_UpperCAmelCase	 =     model(A)

						# verify the logits shape
						_UpperCAmelCase	 =     torch.Size((self.batch_size, self.num_labels))
						self.parent.assertEqual(result.logits.shape				,	A)



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.prepare_config_and_inputs()
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     config_and_inputs
						_UpperCAmelCase	 =     {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
			UpperCamelCase           =  (
			    {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase           =  False
			UpperCamelCase           =  False
			UpperCamelCase           =  False
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						int)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     TimesformerModelTester(self)
						_UpperCAmelCase	 =     ConfigTester(
						    self				,	config_class=A				,	has_text_modality=A				,	hidden_size=37)



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Union[str, Any]				,	A     :						int				,	A     :						int=False)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     copy.deepcopy(A)

						if return_labels:
									if model_class in get_values(A):
												_UpperCAmelCase	 =     torch.zeros(
												    self.model_tester.batch_size				,	dtype=torch.long				,	device=A)

						return inputs_dict



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



						"""simple docstring"""






						self.config_tester.run_common_tests()



			@unittest.skip(reason='TimeSformer does not use inputs_embeds')
			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					int:



						"""simple docstring"""






						pass



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



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     model_class(A)
									self.assertIsInstance(model.get_input_embeddings()				,	(nn.Module))
									_UpperCAmelCase	 =     model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A				,	nn.Linear))



			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

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

									_UpperCAmelCase	 =     ['pixel_values']
									self.assertListEqual(arg_names[:1]				,	A)



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



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



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



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_video_classification(*A)



			@slow
			def 					_lowerCamelCase (		self     :						Tuple)     ->					List[str]:



						"""simple docstring"""






						for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase	 =     TimesformerModel.from_pretrained(A)
									self.assertIsNotNone(A)



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



						"""simple docstring"""






						if not self.has_attentions:
									pass

						else:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase	 =     True

									for model_class in self.all_model_classes:
												_UpperCAmelCase	 =     self.model_tester.seq_length
												_UpperCAmelCase	 =     self.model_tester.num_frames

												_UpperCAmelCase	 =     True
												_UpperCAmelCase	 =     False
												_UpperCAmelCase	 =     True
												_UpperCAmelCase	 =     model_class(A)
												model.to(A)
												model.eval()
												with torch.no_grad():
															_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))
												_UpperCAmelCase	 =     outputs.attentions
												self.assertEqual(len(A)				,	self.model_tester.num_hidden_layers)

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												_UpperCAmelCase	 =     True
												_UpperCAmelCase	 =     model_class(A)
												model.to(A)
												model.eval()
												with torch.no_grad():
															_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))
												_UpperCAmelCase	 =     outputs.attentions
												self.assertEqual(len(A)				,	self.model_tester.num_hidden_layers)

												# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
												self.assertListEqual(
												    list(attentions[0].shape[-3:])				,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]				,	)
												_UpperCAmelCase	 =     len(A)

												# Check attention is always last and order is fine
												_UpperCAmelCase	 =     True
												_UpperCAmelCase	 =     True
												_UpperCAmelCase	 =     model_class(A)
												model.to(A)
												model.eval()
												with torch.no_grad():
															_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))

												self.assertEqual(out_len + 1				,	len(A))

												_UpperCAmelCase	 =     outputs.attentions

												self.assertEqual(len(A)				,	self.model_tester.num_hidden_layers)

												# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:])				,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]				,	)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					str:



						"""simple docstring"""






						def check_hidden_states_output(A     :						Tuple				,	A     :						Optional[int]				,	A     :						Dict):
									_UpperCAmelCase	 =     model_class(A)
									model.to(A)
									model.eval()

									with torch.no_grad():
												_UpperCAmelCase	 =     model(**self._prepare_for_class(A				,	A))

									_UpperCAmelCase	 =     outputs.hidden_states
									_UpperCAmelCase	 =     self.model_tester.num_hidden_layers + 1
									self.assertEqual(len(A)				,	A)

									_UpperCAmelCase	 =     self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:])				,	[seq_length, self.model_tester.hidden_size]				,	)

						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_UpperCAmelCase	 =     True
									check_hidden_states_output(A				,	A				,	A)

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

									check_hidden_states_output(A				,	A				,	A)





def 							A			(  )       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     hf_hub_download(
			    repo_id='hf-internal-testing/spaghetti-video'							,     filename='eating_spaghetti.npy'							,     repo_type='dataset'					)
			_UpperCAmelCase	 =     np.load(_UpperCAmelCase					)
			return list(_UpperCAmelCase					)







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



			@cached_property
			def 					_lowerCamelCase (		self     :						List[str])     ->					List[Any]:



						"""simple docstring"""






						return (
						    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]				,	image_std=[0.5, 0.5, 0.5])
						    if is_vision_available()
						    else None
						)



			@slow
			def 					_lowerCamelCase (		self     :						List[Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400').to(
						    A)

						_UpperCAmelCase	 =     self.default_image_processor
						_UpperCAmelCase	 =     prepare_video()
						_UpperCAmelCase	 =     image_processor(video[:8]				,	return_tensors='pt').to(A)

						# forward pass
						with torch.no_grad():
									_UpperCAmelCase	 =     model(**A)

						# verify the logits
						_UpperCAmelCase	 =     torch.Size((1, 4_00))
						self.assertEqual(outputs.logits.shape				,	A)

						_UpperCAmelCase	 =     torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]).to(A)

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





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()
UpperCAmelCase__     = logging.get_logger(__name__)


UpperCAmelCase__     = "https://openaipublic.azureedge.net/jukebox/models/"
UpperCAmelCase__     = {
    "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			(  _UpperCAmelCase       :     List[str]					)       ->				Tuple:







			'''simple docstring'''
			if key.endswith('.model.1.bias'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.1.bias'							,     '.conv1d_1.bias'					)
			elif key.endswith('.model.1.weight'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.1.weight'							,     '.conv1d_1.weight'					)
			elif key.endswith('.model.3.bias'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.3.bias'							,     '.conv1d_2.bias'					)
			elif key.endswith('.model.3.weight'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.3.weight'							,     '.conv1d_2.weight'					)

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

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

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						_UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     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			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			import re

			_UpperCAmelCase	 =     re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(
			    R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'					)

			_UpperCAmelCase	 =     re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(
			    R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'					)

			_UpperCAmelCase	 =     re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(
			    R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
			_UpperCAmelCase	 =     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(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_encoder_block_conv_in.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[2]					) * 2 + int(groups[3]					)
									_UpperCAmelCase	 =     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
									_UpperCAmelCase	 =     re_encoder_block_conv_in.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

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

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

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

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

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

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

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

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

						# keep original key
						else:
									_UpperCAmelCase	 =     original_key

						_UpperCAmelCase	 =     replace_key(_UpperCAmelCase					)

						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:
									_UpperCAmelCase	 =     model_state_dict[F"{key_prefix}.{key}"]
									print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"					)
									_UpperCAmelCase	 =     original_key

						_UpperCAmelCase	 =     original_key
						_UpperCAmelCase	 =     value

			return new_dict





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     List[str]=None							,     _UpperCAmelCase       :     Dict=None					)       ->				Dict:







			'''simple docstring'''
			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'					)[-1]}"					):
									_UpperCAmelCase	 =     requests.get(F"{PREFIX}{file}"							,     allow_redirects=_UpperCAmelCase					)
									os.makedirs(F"{pytorch_dump_folder_path}/"							,     exist_ok=_UpperCAmelCase					)
									open(F"{pytorch_dump_folder_path}/{file.split('/'					)[-1]}"							,     'wb'					).write(r.content					)

			_UpperCAmelCase	 =     MODEL_MAPPING[model_name.split('/'					)[-1]]

			_UpperCAmelCase	 =     JukeboxConfig.from_pretrained(_UpperCAmelCase					)
			_UpperCAmelCase	 =     JukeboxModel(_UpperCAmelCase					)

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

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

						_UpperCAmelCase	 =     'vqvae' if i == 0 else F"priors.{3 - i}"
						_UpperCAmelCase	 =     fix_jukebox_keys(_UpperCAmelCase							,     model.state_dict()							,     _UpperCAmelCase							,     _UpperCAmelCase					)
						weight_dict.append(_UpperCAmelCase					)

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

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

			print(F"Saving model {model_name} to {pytorch_dump_folder_path}"					)
			model.save_pretrained(_UpperCAmelCase					)

			return weight_dict


if __name__ == "__main__":
							UpperCAmelCase__     = 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.",
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
