"""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()
__lowercase		:		int														=							logging.get_logger(__name__)


__lowercase		:		List[Any]														=							"https://openaipublic.azureedge.net/jukebox/models/"
__lowercase		:		Optional[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    SCREAMING_SNAKE_CASE       (     snake_case):
     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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case):
     __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(snake_case):
               __snake_case				=							re_encoder_block_conv_in.match(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(snake_case,      snake_case)

          elif re_encoder_block_resnet.fullmatch(snake_case):
               __snake_case				=							re_encoder_block_resnet.match(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(snake_case,      snake_case)

          elif re_encoder_block_proj_out.fullmatch(snake_case):
               __snake_case				=							re_encoder_block_proj_out.match(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(snake_case,      snake_case)

          # rename vqvae.decoder keys
          elif re_decoder_block_conv_out.fullmatch(snake_case):
               __snake_case				=							re_decoder_block_conv_out.match(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(snake_case,      snake_case)

          elif re_decoder_block_resnet.fullmatch(snake_case):
               __snake_case				=							re_decoder_block_resnet.match(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(snake_case,      snake_case)

          elif re_decoder_block_proj_in.fullmatch(snake_case):
               __snake_case				=							re_decoder_block_proj_in.match(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(snake_case,      snake_case)

          # rename prior cond.model to upsampler.upsample_block and resnet
          elif re_prior_cond_conv_out.fullmatch(snake_case):
               __snake_case				=							re_prior_cond_conv_out.match(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(snake_case,      snake_case)

          elif re_prior_cond_resnet.fullmatch(snake_case):
               __snake_case				=							re_prior_cond_resnet.match(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(snake_case,      snake_case)

          elif re_prior_cond_proj_in.fullmatch(snake_case):
               __snake_case				=							re_prior_cond_proj_in.match(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(snake_case,      snake_case)

          # keep original key
          else:
               __snake_case				=							original_key

          __snake_case				=							replace_key(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    SCREAMING_SNAKE_CASE       (     snake_case=None,      snake_case=None):
     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=snake_case)
               os.makedirs(f"{pytorch_dump_folder_path}/",      exist_ok=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(snake_case)
     __snake_case				=							JukeboxModel(snake_case)

     __snake_case				=							[]
     __snake_case				=							{}
     for i, dict_name in enumerate(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(snake_case,      model.state_dict(),      snake_case,      snake_case)
          weight_dict.append(snake_case)

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

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

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

     return weight_dict


if __name__ == "__main__":
       __lowercase		:		str														=							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.",
       )
       __lowercase		:		Dict														=							parser.parse_args()
       convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import (
           AutoProcessor,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     str    )    ->       str:
         __snake_case				=							tempfile.mkdtemp()

         __snake_case				=							BlipImageProcessor()
         __snake_case				=							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''    )
         __snake_case				=							BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )

         __snake_case				=							InstructBlipProcessor(A_	,      A_	,      A_    )

         processor.save_pretrained(self.tmpdirname    )
    def  lowercase					(		self							:     Tuple	,      **A_							:     str    )    ->       List[Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).tokenizer
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Union[str, Any]:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).image_processor
    def  lowercase					(		self							:     Union[str, Any]	,      **A_							:     Tuple    )    ->       Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname	,      **A_    ).qformer_tokenizer
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Tuple:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

         __snake_case				=							[Image.fromarray(np.moveaxis(A_	,      0	,      -1    )    ) for x in image_inputs]

         return image_inputs
    def  lowercase					(		self							:     int    )    ->       Dict:
         __snake_case				=							InstructBlipProcessor(
             tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(bos_token='''(BOS)'''	,      eos_token='''(EOS)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_	,      padding_value=1.0    )

         __snake_case				=							InstructBlipProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
         self.assertIsInstance(processor.qformer_tokenizer	,      A_    )
    def  lowercase					(		self							:     int    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     List[str]    )    ->       Optional[int]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_	,      return_token_type_ids=A_    )
         __snake_case				=							qformer_tokenizer(A_	,      return_token_type_ids=A_    )

         for key in encoded_tokens.keys():
              self.assertListEqual(encoded_tokens[key]	,      encoded_processor[key]    )

         for key in encoded_tokens_qformer.keys():
              self.assertListEqual(encoded_tokens_qformer[key]	,      encoded_processor['''qformer_''' + key]    )
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(
             list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']	,      )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     str    )    ->       Union[str, Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )







    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_qformer_tokenizer()

         __snake_case				=							InstructBlipProcessor(
             tokenizer=A_	,      image_processor=A_	,      qformer_tokenizer=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(
             list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']	,      )