




'''simple docstring'''


from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.layoutlm.modeling_tf_layoutlm import (
	    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFLayoutLMForMaskedLM,
	    TFLayoutLMForQuestionAnswering,
	    TFLayoutLMForSequenceClassification,
	    TFLayoutLMForTokenClassification,
	    TFLayoutLMModel,
	)
class 						a_				:




	def __init__(       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Tuple=1_3       ,		__lowerCAmelCase     :  Any=7       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Tuple=9_9       ,		__lowerCAmelCase     :  Optional[Any]=3_2       ,		__lowerCAmelCase     :  Optional[int]=2       ,		__lowerCAmelCase     :  Union[str, Any]=4       ,		__lowerCAmelCase     :  Optional[Any]=3_7       ,		__lowerCAmelCase     :  Any="gelu"       ,		__lowerCAmelCase     :  Tuple=0.1       ,		__lowerCAmelCase     :  List[Any]=0.1       ,		__lowerCAmelCase     :  Optional[Any]=5_1_2       ,		__lowerCAmelCase     :  Tuple=1_6       ,		__lowerCAmelCase     :  Optional[int]=2       ,		__lowerCAmelCase     :  Any=0.02       ,		__lowerCAmelCase     :  Tuple=3       ,		__lowerCAmelCase     :  Optional[int]=4       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		__lowerCAmelCase     :  List[Any]=1_0_0_0       ,		):
							__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						 =   num_choices
							__snake_case						 =   scope
							__snake_case						 =   range_bbox




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

							# convert bbox to numpy since TF does not support item assignment
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length, 4]       ,		self.range_bbox      ).numpy()
							# 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						 =   tf.convert_to_tensor(__SCREAMING_SNAKE_CASE      )

							__snake_case						 =   None
							if self.use_input_mask:
													__snake_case						 =   random_attention_mask([self.batch_size, self.seq_length]      )

							__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
							__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						 =   ids_tensor([self.batch_size]       ,		self.num_choices      )

							__snake_case						 =   LayoutLMConfig(
							    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       ,		)

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




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  int      ):
							__snake_case						 =   TFLayoutLMModel(config=__SCREAMING_SNAKE_CASE      )

							__snake_case						 =   model(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE      )
							__snake_case						 =   model(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE      )
							__snake_case						 =   model(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE      )
							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   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   TFLayoutLMForMaskedLM(config=__SCREAMING_SNAKE_CASE      )

							__snake_case						 =   model(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE       ,		labels=__SCREAMING_SNAKE_CASE      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Optional[int]      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFLayoutLMForSequenceClassification(config=__SCREAMING_SNAKE_CASE      )

							__snake_case						 =   model(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)      )




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFLayoutLMForTokenClassification(config=__SCREAMING_SNAKE_CASE      )

							__snake_case						 =   model(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE       ,		labels=__SCREAMING_SNAKE_CASE      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)      )




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Any      ):
							__snake_case						 =   TFLayoutLMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE      )

							__snake_case						 =   model(__SCREAMING_SNAKE_CASE       ,		__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE      )
							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   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case  
							) ,       (
							    __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_tf
class 						a_				(			a_     ,						a_     ,						unittest.TestCase						):
	lowercase_       :     Tuple			  =      (
	    (
	        TFLayoutLMModel,
	        TFLayoutLMForMaskedLM,
	        TFLayoutLMForTokenClassification,
	        TFLayoutLMForSequenceClassification,
	        TFLayoutLMForQuestionAnswering,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase_       :     Tuple			  =      (
	    {
	        '''feature-extraction''': TFLayoutLMModel,
	        '''fill-mask''': TFLayoutLMForMaskedLM,
	        '''text-classification''': TFLayoutLMForSequenceClassification,
	        '''token-classification''': TFLayoutLMForTokenClassification,
	        '''zero-shot''': TFLayoutLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	lowercase_       :     Optional[Any]			  =      False
	lowercase_       :     Dict			  =      True
	lowercase_       :     Dict			  =      10




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   TFLayoutLMModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__SCREAMING_SNAKE_CASE       ,		hidden_size=3_7      )




	def   lowercase__   (       self     :  Any      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE      )




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE      )




	@slow
	def   lowercase__   (       self     :  Tuple      ):
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   TFLayoutLMModel.from_pretrained(__SCREAMING_SNAKE_CASE      )
													self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )




	@unittest.skip('Onnx compliancy broke with TF 2.10'      )
	def   lowercase__   (       self     :  Dict      ):
							pass





def 						lowerCamelCase__   (				):
						__snake_case						 =   tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]							)  # noqa: E231
						__snake_case						 =   tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]							)  # noqa: E231
						__snake_case						 =   tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]							)  # noqa: E231
						__snake_case						 =   tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]							)  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						__snake_case						 =   tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]							)  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class 						a_				(			unittest.TestCase						):




	@slow
	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'      )

							__snake_case ,       __snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   prepare_layoutlm_batch_inputs()

							# forward pass
							__snake_case						 =   model(input_ids=__SCREAMING_SNAKE_CASE       ,		bbox=__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE      )

							# test the sequence output on [0, :3, :3]
							__snake_case						 =   tf.convert_to_tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]       ,		)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]       ,		__SCREAMING_SNAKE_CASE       ,		atol=1E-3      )      )

							# test the pooled output on [1, :3]
							__snake_case						 =   tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]      )

							self.assertTrue(np.allclose(outputs.pooler_output[1, :3]       ,		__SCREAMING_SNAKE_CASE       ,		atol=1E-3      )      )




	@slow
	def   lowercase__   (       self     :  Any      ):
							# initialize model with randomly initialized sequence classification head
							__snake_case						 =   TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'       ,		num_labels=2      )

							__snake_case ,       __snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   prepare_layoutlm_batch_inputs()

							# forward pass
							__snake_case						 =   model(
							    input_ids=__SCREAMING_SNAKE_CASE       ,		bbox=__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE       ,		labels=tf.convert_to_tensor([1, 1]      )       ,		)

							# test whether we get a loss as a scalar
							__snake_case						 =   outputs.loss
							__snake_case						 =   (2,)
							self.assertEqual(loss.shape       ,		__SCREAMING_SNAKE_CASE      )

							# test the shape of the logits
							__snake_case						 =   outputs.logits
							__snake_case						 =   (2, 2)
							self.assertEqual(logits.shape       ,		__SCREAMING_SNAKE_CASE      )




	@slow
	def   lowercase__   (       self     :  Tuple      ):
							# initialize model with randomly initialized token classification head
							__snake_case						 =   TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'       ,		num_labels=1_3      )

							__snake_case ,       __snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   prepare_layoutlm_batch_inputs()

							# forward pass
							__snake_case						 =   model(
							    input_ids=__SCREAMING_SNAKE_CASE       ,		bbox=__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE       ,		labels=__SCREAMING_SNAKE_CASE      )

							# test the shape of the logits
							__snake_case						 =   outputs.logits
							__snake_case						 =   tf.convert_to_tensor((2, 2_5, 1_3)      )
							self.assertEqual(logits.shape       ,		__SCREAMING_SNAKE_CASE      )




	@slow
	def   lowercase__   (       self     :  Any      ):
							# initialize model with randomly initialized token classification head
							__snake_case						 =   TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'      )

							__snake_case ,       __snake_case ,       __snake_case ,       __snake_case ,       __snake_case						 =   prepare_layoutlm_batch_inputs()

							# forward pass
							__snake_case						 =   model(input_ids=__SCREAMING_SNAKE_CASE       ,		bbox=__SCREAMING_SNAKE_CASE       ,		attention_mask=__SCREAMING_SNAKE_CASE       ,		token_type_ids=__SCREAMING_SNAKE_CASE      )

							# test the shape of the logits
							__snake_case						 =   tf.convert_to_tensor((2, 2_5)      )
							self.assertEqual(outputs.start_logits.shape       ,		__SCREAMING_SNAKE_CASE      )
							self.assertEqual(outputs.end_logits.shape       ,		__SCREAMING_SNAKE_CASE      )


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			:						Optional[int]				  =		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE			:						int				  =		{
    """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		(      _A       ):



 """simple docstring"""


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

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

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

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



 """simple docstring"""


 __a							=	{}
 import re

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

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

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

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

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

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

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

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

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

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

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

  # keep original key
  else:
   __a							=	original_key

  __a							=	replace_key(_A       )

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

  __a							=	original_key
  __a							=	value

 return new_dict



@torch.no_grad()
def 	__A		(      _A=None				,					_A=None       ):



 """simple docstring"""


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

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

 __a							=	JukeboxConfig.from_pretrained(_A       )
 __a							=	JukeboxModel(_A       )

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

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

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

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

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

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

 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			:						Optional[int]				  =		parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


