





'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			__lowerCamelCase : Any											=			"platform"

			import jax
			import jax.numpy as jnp

			from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
			    FlaxBlenderbotSmallForConditionalGeneration,
			    FlaxBlenderbotSmallModel,
			    shift_tokens_right,
			)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=None		,							lowerCAmelCase_=None		,							lowerCAmelCase_=None		,							lowerCAmelCase_=None		,							lowerCAmelCase_=None		,							lowerCAmelCase_=None		,							):

				"""simple docstring"""






				if attention_mask is None:
								lowercase         = np.where(input_ids != config.pad_token_id		,							1		,							0							)
				if decoder_attention_mask is None:
								lowercase         = np.where(decoder_input_ids != config.pad_token_id		,							1		,							0							)
				if head_mask is None:
								lowercase         = np.ones((config.encoder_layers, config.encoder_attention_heads)							)
				if decoder_head_mask is None:
								lowercase         = np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				if cross_attn_head_mask is None:
								lowercase         = np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				}

class       UpperCAmelCase :




		def __init__(self      :	Any							,			A__      :	Dict							,			A__      :	int=1_3							,			A__      :	Optional[int]=7							,			A__      :	List[str]=True							,			A__      :	Optional[int]=False							,			A__      :	str=9_9							,			A__      :	int=1_6							,			A__      :	Union[str, Any]=2							,			A__      :	List[Any]=4							,			A__      :	Tuple=4							,			A__      :	Union[str, Any]="gelu"							,			A__      :	List[str]=0.1							,			A__      :	Optional[Any]=0.1							,			A__      :	List[Any]=3_2							,			A__      :	str=2							,			A__      :	Optional[int]=1							,			A__      :	Union[str, Any]=0							,			A__      :	List[Any]=0.0_2							,			)					-> int:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = seq_length
						lowercase         = is_training
						lowercase         = use_labels
						lowercase         = vocab_size
						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = max_position_embeddings
						lowercase         = eos_token_id
						lowercase         = pad_token_id
						lowercase         = bos_token_id
						lowercase         = initializer_range




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Optional[int]:
						lowercase         = np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,			self.vocab_size				)							,			3							,			self.vocab_size				)
						lowercase         = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,			dtype=np.intaa				))							,			-1				)

						lowercase         = shift_tokens_right(A__							,			1							,			2				)

						lowercase         = BlenderbotSmallConfig(
						    vocab_size=self.vocab_size							,			d_model=self.hidden_size							,			encoder_layers=self.num_hidden_layers							,			decoder_layers=self.num_hidden_layers							,			encoder_attention_heads=self.num_attention_heads							,			decoder_attention_heads=self.num_attention_heads							,			encoder_ffn_dim=self.intermediate_size							,			decoder_ffn_dim=self.intermediate_size							,			dropout=self.hidden_dropout_prob							,			attention_dropout=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			eos_token_id=self.eos_token_id							,			bos_token_id=self.bos_token_id							,			pad_token_id=self.pad_token_id							,			initializer_range=self.initializer_range							,			use_cache=A__							,			)
						lowercase         = prepare_blenderbot_inputs_dict(A__							,			A__							,			A__				)
						return config, inputs_dict




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Union[str, Any]:
						lowercase   ,							lowercase         = self.prepare_config_and_inputs()
						return config, inputs_dict




		def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	List[str]							,			A__      :	List[str]							,			A__      :	str				)					-> Union[str, Any]:
						lowercase         = 2_0
						lowercase         = model_class_name(A__				)

						lowercase         = model.encode(inputs_dict["input_ids"]				)

						lowercase   ,							lowercase         = (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						lowercase         = model.init_cache(decoder_input_ids.shape[0]							,			A__							,			A__				)
						lowercase         = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,			dtype="i4"				)

						lowercase         = jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,			)
						lowercase         = model.decode(
						    decoder_input_ids[:, :-1]							,			A__							,			decoder_attention_mask=A__							,			past_key_values=A__							,			decoder_position_ids=A__							,			)

						lowercase         = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,			dtype="i4"				)
						lowercase         = model.decode(
						    decoder_input_ids[:, -1:]							,			A__							,			decoder_attention_mask=A__							,			past_key_values=outputs_cache.past_key_values							,			decoder_position_ids=A__							,			)

						lowercase         = model.decode(A__							,			A__				)

						lowercase         = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
						self.parent.assertTrue(diff < 1e-3							,			msg=f'Max diff is {diff}'				)





		def 				UpperCAmelCase__   (self      :	int							,			A__      :	Optional[Any]							,			A__      :	Tuple							,			A__      :	List[str]				)					-> int:
						lowercase         = 2_0
						lowercase         = model_class_name(A__				)

						lowercase         = model.encode(inputs_dict["input_ids"]				)

						lowercase   ,							lowercase         = (
						    inputs_dict["decoder_input_ids"],
						    inputs_dict["decoder_attention_mask"],
						)

						lowercase         = jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
						    ]							,			axis=-1							,			)

						lowercase         = model.init_cache(decoder_input_ids.shape[0]							,			A__							,			A__				)
						lowercase         = jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]							,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,			)

						lowercase         = model.decode(
						    decoder_input_ids[:, :-1]							,			A__							,			decoder_attention_mask=A__							,			past_key_values=A__							,			decoder_position_ids=A__							,			)
						lowercase         = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,			dtype="i4"				)
						lowercase         = model.decode(
						    decoder_input_ids[:, -1:]							,			A__							,			past_key_values=outputs_cache.past_key_values							,			decoder_attention_mask=A__							,			decoder_position_ids=A__							,			)

						lowercase         = model.decode(A__							,			A__							,			decoder_attention_mask=A__				)

						lowercase         = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
						self.parent.assertTrue(diff < 1e-3							,			msg=f'Max diff is {diff}'				)




@require_flax
class       UpperCAmelCase (						unittest.TestCase  ):
		UpperCAmelCase					:	Dict 			=		99




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Any:
						lowercase         = np.array(
						    [
						        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
						        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
						        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
						        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
						        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
						        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
						        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
						        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
						        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
						        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
						        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
						        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
						        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
						    ]							,			dtype=np.intaa							,			)

						lowercase         = input_ids.shape[0]
						lowercase         = BlenderbotSmallConfig(
						    vocab_size=self.vocab_size							,			d_model=2_4							,			encoder_layers=2							,			decoder_layers=2							,			encoder_attention_heads=2							,			decoder_attention_heads=2							,			encoder_ffn_dim=3_2							,			decoder_ffn_dim=3_2							,			max_position_embeddings=4_8							,			eos_token_id=2							,			pad_token_id=1							,			bos_token_id=0							,			)
						return config, input_ids, batch_size




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[int]:
						lowercase   ,							lowercase   ,							lowercase         = self._get_config_and_data()
						lowercase         = FlaxBlenderbotSmallForConditionalGeneration(A__				)
						lowercase         = lm_model(input_ids=A__				)
						lowercase         = (batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs["logits"].shape							,			A__				)




		def 				UpperCAmelCase__   (self      :	Any				)					-> Any:
						lowercase         = BlenderbotSmallConfig(
						    vocab_size=self.vocab_size							,			d_model=1_4							,			encoder_layers=2							,			decoder_layers=2							,			encoder_attention_heads=2							,			decoder_attention_heads=2							,			encoder_ffn_dim=8							,			decoder_ffn_dim=8							,			max_position_embeddings=4_8							,			)
						lowercase         = FlaxBlenderbotSmallForConditionalGeneration(A__				)
						lowercase         = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,			dtype=np.intaa				)
						lowercase         = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,			dtype=np.intaa				)
						lowercase         = lm_model(input_ids=A__							,			decoder_input_ids=A__				)
						lowercase         = (*summary.shape, config.vocab_size)
						self.assertEqual(outputs["logits"].shape							,			A__				)





		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Optional[Any]:
						lowercase         = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,			dtype=np.intaa				)
						lowercase         = shift_tokens_right(A__							,			1							,			2				)
						lowercase         = np.equal(A__							,			1				).astype(np.floataa				).sum()
						lowercase         = np.equal(A__							,			1				).astype(np.floataa				).sum()
						self.assertEqual(shifted.shape							,			input_ids.shape				)
						self.assertEqual(A__							,			n_pad_before - 1				)
						self.assertTrue(np.equal(shifted[:, 0]							,			2				).all()				)




@require_flax
class       UpperCAmelCase (						_lowercase			,       unittest.TestCase			,       _lowercase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		True
		UpperCAmelCase					:	List[Any] 			=		(
		    (
		        FlaxBlenderbotSmallModel,
		        FlaxBlenderbotSmallForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		UpperCAmelCase					:	List[Any] 			=		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




		def 				UpperCAmelCase__   (self      :	Dict				)					-> List[str]:
						lowercase         = FlaxBlenderbotSmallModelTester(self				)




		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Tuple:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward(A__							,			A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	str				)					-> Optional[Any]:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
										self.model_tester.check_use_cache_forward_with_attn_mask(A__							,			A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Tuple:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
														lowercase         = self._prepare_for_class(A__							,			A__				)
														lowercase         = model_class(A__				)

														@jax.jit
														def encode_jitted(A__      :	Optional[int]							,			A__      :	Any=None							,			**A__      :	Optional[int]				):
																		return model.encode(input_ids=A__							,			attention_mask=A__				)

														with self.subTest("JIT Enabled"				):
																		lowercase         = encode_jitted(**A__				).to_tuple()

														with self.subTest("JIT Disabled"				):
																		with jax.disable_jit():
																						lowercase         = encode_jitted(**A__				).to_tuple()

														self.assertEqual(len(A__				)							,			len(A__				)				)
														for jitted_output, output in zip(A__							,			A__				):
																		self.assertEqual(jitted_output.shape							,			output.shape				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Tuple:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__				):
														lowercase         = model_class(A__				)
														lowercase         = model.encode(inputs_dict["input_ids"]							,			inputs_dict["attention_mask"]				)

														lowercase         = {
														    "decoder_input_ids": inputs_dict["decoder_input_ids"],
														    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
														    "encoder_outputs": encoder_outputs,
														}

														@jax.jit
														def decode_jitted(A__      :	int							,			A__      :	List[Any]							,			A__      :	Union[str, Any]				):
																		return model.decode(
																		    decoder_input_ids=A__							,			decoder_attention_mask=A__							,			encoder_outputs=A__							,			)

														with self.subTest("JIT Enabled"				):
																		lowercase         = decode_jitted(**A__				).to_tuple()

														with self.subTest("JIT Disabled"				):
																		with jax.disable_jit():
																						lowercase         = decode_jitted(**A__				).to_tuple()

														self.assertEqual(len(A__				)							,			len(A__				)				)
														for jitted_output, output in zip(A__							,			A__				):
																		self.assertEqual(jitted_output.shape							,			output.shape				)





		@slow
		def 				UpperCAmelCase__   (self      :	int				)					-> Union[str, Any]:
						for model_class_name in self.all_model_classes:
										lowercase         = model_class_name.from_pretrained("facebook/blenderbot_small-90M"				)
										# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
										lowercase         = np.ones((1, 1)				) * model.config.eos_token_id
										lowercase         = model(A__				)
										self.assertIsNotNone(A__				)











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


__lowerCamelCase : List[Any]											=			"https://openaipublic.azureedge.net/jukebox/models/"
__lowerCamelCase : Tuple											=			{
    "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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if key.endswith(".model.1.bias"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.1.bias"		,							".conv1d_1.bias"							)
				elif key.endswith(".model.1.weight"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.1.weight"		,							".conv1d_1.weight"							)
				elif key.endswith(".model.3.bias"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.3.bias"		,							".conv1d_2.bias"							)
				elif key.endswith(".model.3.weight"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.3.weight"		,							".conv1d_2.weight"							)

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

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

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								lowercase         = 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:
								lowercase         = 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 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {}
				import re

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

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

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

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

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

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

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

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

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

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

								elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_							):
												lowercase         = re_prior_cond_proj_in.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
												lowercase         = re_prior_cond_proj_in.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								# keep original key
								else:
												lowercase         = original_key

								lowercase         = replace_key(lowerCAmelCase_							)

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

								lowercase         = original_key
								lowercase         = value

				return new_dict







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_=None		,							lowerCAmelCase_=None							):

				"""simple docstring"""






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

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

				lowercase         = JukeboxConfig.from_pretrained(lowerCAmelCase_							)
				lowercase         = JukeboxModel(lowerCAmelCase_							)

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

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

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

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

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

				print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
				model.save_pretrained(lowerCAmelCase_							)

				return weight_dict


if __name__ == "__main__":
			__lowerCamelCase : int											=			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.",
			)
			__lowerCamelCase : List[str]											=			parser.parse_args()
			convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




